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Change-Aware Sampling and Contrastive Learning for Satellite Images | Utkarsh Mall, Bharath Hariharan, Kavita Bala | Automatic remote sensing tools can help inform many large-scale challenges such as disaster management, climate change, etc. While a vast amount of spatio-temporal satellite image data is readily available, most of it remains unlabelled. Without labels, this data is not very useful for supervised learning algorithms. Self-supervised learning instead provides a way to learn effective representations for various downstream tasks without labels. In this work, we leverage characteristics unique to satellite images to learn better self-supervised features. Specifically, we use the temporal signal to contrast images with long-term and short-term differences, and we leverage the fact that satellite images do not change frequently. Using these characteristics, we formulate a new loss contrastive loss called Change-Aware Contrastive (CACo) Loss. Further, we also present a novel method of sampling different geographical regions. We show that leveraging these properties leads to better performance on diverse downstream tasks. For example, we see a 6.5% relative improvement for semantic segmentation and an 8.5% relative improvement for change detection over the best-performing baseline with our method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Mall_Change-Aware_Sampling_and_Contrastive_Learning_for_Satellite_Images_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mall_Change-Aware_Sampling_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mall_Change-Aware_Sampling_and_Contrastive_Learning_for_Satellite_Images_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mall_Change-Aware_Sampling_and_Contrastive_Learning_for_Satellite_Images_CVPR_2023_paper.html | CVPR 2023 | null |
Large-Scale Training Data Search for Object Re-Identification | Yue Yao, Tom Gedeon, Liang Zheng | We consider a scenario where we have access to the target domain, but cannot afford on-the-fly training data annotation, and instead would like to construct an alternative training set from a large-scale data pool such that a competitive model can be obtained. We propose a search and pruning (SnP) solution to this training data search problem, tailored to object re-identification (re-ID), an application aiming to match the same object captured by different cameras. Specifically, the search stage identifies and merges clusters of source identities which exhibit similar distributions with the target domain. The second stage, subject to a budget, then selects identities and their images from the Stage I output, to control the size of the resulting training set for efficient training. The two steps provide us with training sets 80% smaller than the source pool while achieving a similar or even higher re-ID accuracy. These training sets are also shown to be superior to a few existing search methods such as random sampling and greedy sampling under the same budget on training data size. If we release the budget, training sets resulting from the first stage alone allow even higher re-ID accuracy. We provide interesting discussions on the specificity of our method to the re-ID problem and particularly its role in bridging the re-ID domain gap. The code is available at https://github.com/yorkeyao/SnP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yao_Large-Scale_Training_Data_Search_for_Object_Re-Identification_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yao_Large-Scale_Training_Data_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16186 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yao_Large-Scale_Training_Data_Search_for_Object_Re-Identification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yao_Large-Scale_Training_Data_Search_for_Object_Re-Identification_CVPR_2023_paper.html | CVPR 2023 | null |
Devil Is in the Queries: Advancing Mask Transformers for Real-World Medical Image Segmentation and Out-of-Distribution Localization | Mingze Yuan, Yingda Xia, Hexin Dong, Zifan Chen, Jiawen Yao, Mingyan Qiu, Ke Yan, Xiaoli Yin, Yu Shi, Xin Chen, Zaiyi Liu, Bin Dong, Jingren Zhou, Le Lu, Ling Zhang, Li Zhang | Real-world medical image segmentation has tremendous long-tailed complexity of objects, among which tail conditions correlate with relatively rare diseases and are clinically significant. A trustworthy medical AI algorithm should demonstrate its effectiveness on tail conditions to avoid clinically dangerous damage in these out-of-distribution (OOD) cases. In this paper, we adopt the concept of object queries in Mask transformers to formulate semantic segmentation as a soft cluster assignment. The queries fit the feature-level cluster centers of inliers during training. Therefore, when performing inference on a medical image in real-world scenarios, the similarity between pixels and the queries detects and localizes OOD regions. We term this OOD localization as MaxQuery. Furthermore, the foregrounds of real-world medical images, whether OOD objects or inliers, are lesions. The difference between them is obviously less than that between the foreground and background, resulting in the object queries may focus redundantly on the background. Thus, we propose a query-distribution (QD) loss to enforce clear boundaries between segmentation targets and other regions at the query level, improving the inlier segmentation and OOD indication. Our proposed framework is tested on two real-world segmentation tasks, i.e., segmentation of pancreatic and liver tumors, outperforming previous leading algorithms by an average of 7.39% on AUROC, 14.69% on AUPR, and 13.79% on FPR95 for OOD localization. On the other hand, our framework improves the performance of inlier segmentation by an average of 5.27% DSC compared with nnUNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yuan_Devil_Is_in_the_Queries_Advancing_Mask_Transformers_for_Real-World_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yuan_Devil_Is_in_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00212 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yuan_Devil_Is_in_the_Queries_Advancing_Mask_Transformers_for_Real-World_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yuan_Devil_Is_in_the_Queries_Advancing_Mask_Transformers_for_Real-World_CVPR_2023_paper.html | CVPR 2023 | null |
KD-DLGAN: Data Limited Image Generation via Knowledge Distillation | Kaiwen Cui, Yingchen Yu, Fangneng Zhan, Shengcai Liao, Shijian Lu, Eric P. Xing | Generative Adversarial Networks (GANs) rely heavily on large-scale training data for training high-quality image generation models. With limited training data, the GAN discriminator often suffers from severe overfitting which directly leads to degraded generation especially in generation diversity. Inspired by the recent advances in knowledge distillation (KD), we propose KD-GAN, a knowledge-distillation based generation framework that introduces pre-trained vision-language models for training effective data-limited image generation models. KD-GAN consists of two innovative designs. The first is aggregated generative KD that mitigates the discriminator overfitting by challenging the discriminator with harder learning tasks and distilling more generalizable knowledge from the pre-trained models. The second is correlated generative KD that improves the generation diversity by distilling and preserving the diverse image-text correlation within the pre-trained models. Extensive experiments over multiple benchmarks show that KD-GAN achieves superior image generation with limited training data. In addition, KD-GAN complements the state-of-the-art with consistent and substantial performance gains. Note that codes will be released. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cui_KD-DLGAN_Data_Limited_Image_Generation_via_Knowledge_Distillation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cui_KD-DLGAN_Data_Limited_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cui_KD-DLGAN_Data_Limited_Image_Generation_via_Knowledge_Distillation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cui_KD-DLGAN_Data_Limited_Image_Generation_via_Knowledge_Distillation_CVPR_2023_paper.html | CVPR 2023 | null |
Batch Model Consolidation: A Multi-Task Model Consolidation Framework | Iordanis Fostiropoulos, Jiaye Zhu, Laurent Itti | In Continual Learning (CL), a model is required to learn a stream of tasks sequentially without significant performance degradation on previously learned tasks. Current approaches fail for a long sequence of tasks from diverse domains and difficulties. Many of the existing CL approaches are difficult to apply in practice due to excessive memory cost or training time, or are tightly coupled to a single device. With the intuition derived from the widely applied mini-batch training, we propose Batch Model Consolidation (BMC) to support more realistic CL under conditions where multiple agents are exposed to a range of tasks. During a regularization phase, BMC trains multiple expert models in parallel on a set of disjoint tasks. Each expert maintains weight similarity to a base model through a stability loss, and constructs a buffer from a fraction of the task's data. During the consolidation phase, we combine the learned knowledge on 'batches' of expert models using a batched consolidation loss in memory data that aggregates all buffers. We thoroughly evaluate each component of our method in an ablation study and demonstrate the effectiveness on standardized benchmark datasets Split-CIFAR-100, Tiny-ImageNet, and the Stream dataset composed of 71 image classification tasks from diverse domains and difficulties. Our method outperforms the next best CL approach by 70% and is the only approach that can maintain performance at the end of 71 tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fostiropoulos_Batch_Model_Consolidation_A_Multi-Task_Model_Consolidation_Framework_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fostiropoulos_Batch_Model_Consolidation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fostiropoulos_Batch_Model_Consolidation_A_Multi-Task_Model_Consolidation_Framework_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fostiropoulos_Batch_Model_Consolidation_A_Multi-Task_Model_Consolidation_Framework_CVPR_2023_paper.html | CVPR 2023 | null |
SelfME: Self-Supervised Motion Learning for Micro-Expression Recognition | Xinqi Fan, Xueli Chen, Mingjie Jiang, Ali Raza Shahid, Hong Yan | Facial micro-expressions (MEs) refer to brief spontaneous facial movements that can reveal a person's genuine emotion. They are valuable in lie detection, criminal analysis, and other areas. While deep learning-based ME recognition (MER) methods achieved impressive success, these methods typically require pre-processing using conventional optical flow-based methods to extract facial motions as inputs. To overcome this limitation, we proposed a novel MER framework using self-supervised learning to extract facial motion for ME (SelfME). To the best of our knowledge, this is the first work using an automatically self-learned motion technique for MER. However, the self-supervised motion learning method might suffer from ignoring symmetrical facial actions on the left and right sides of faces when extracting fine features. To address this issue, we developed a symmetric contrastive vision transformer (SCViT) to constrain the learning of similar facial action features for the left and right parts of faces. Experiments were conducted on two benchmark datasets showing that our method achieved state-of-the-art performance, and ablation studies demonstrated the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fan_SelfME_Self-Supervised_Motion_Learning_for_Micro-Expression_Recognition_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fan_SelfME_Self-Supervised_Motion_Learning_for_Micro-Expression_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fan_SelfME_Self-Supervised_Motion_Learning_for_Micro-Expression_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
DR2: Diffusion-Based Robust Degradation Remover for Blind Face Restoration | Zhixin Wang, Ziying Zhang, Xiaoyun Zhang, Huangjie Zheng, Mingyuan Zhou, Ya Zhang, Yanfeng Wang | Blind face restoration usually synthesizes degraded low-quality data with a pre-defined degradation model for training, while more complex cases could happen in the real world. This gap between the assumed and actual degradation hurts the restoration performance where artifacts are often observed in the output. However, it is expensive and infeasible to include every type of degradation to cover real-world cases in the training data. To tackle this robustness issue, we propose Diffusion-based Robust Degradation Remover (DR2) to first transform the degraded image to a coarse but degradation-invariant prediction, then employ an enhancement module to restore the coarse prediction to a high-quality image. By leveraging a well-performing denoising diffusion probabilistic model, our DR2 diffuses input images to a noisy status where various types of degradation give way to Gaussian noise, and then captures semantic information through iterative denoising steps. As a result, DR2 is robust against common degradation (e.g. blur, resize, noise and compression) and compatible with different designs of enhancement modules. Experiments in various settings show that our framework outperforms state-of-the-art methods on heavily degraded synthetic and real-world datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_DR2_Diffusion-Based_Robust_Degradation_Remover_for_Blind_Face_Restoration_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_DR2_Diffusion-Based_Robust_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.06885 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_DR2_Diffusion-Based_Robust_Degradation_Remover_for_Blind_Face_Restoration_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_DR2_Diffusion-Based_Robust_Degradation_Remover_for_Blind_Face_Restoration_CVPR_2023_paper.html | CVPR 2023 | null |
T-SEA: Transfer-Based Self-Ensemble Attack on Object Detection | Hao Huang, Ziyan Chen, Huanran Chen, Yongtao Wang, Kevin Zhang | Compared to query-based black-box attacks, transfer-based black-box attacks do not require any information of the attacked models, which ensures their secrecy. However, most existing transfer-based approaches rely on ensembling multiple models to boost the attack transferability, which is time- and resource-intensive, not to mention the difficulty of obtaining diverse models on the same task. To address this limitation, in this work, we focus on the single-model transfer-based black-box attack on object detection, utilizing only one model to achieve a high-transferability adversarial attack on multiple black-box detectors. Specifically, we first make observations on the patch optimization process of the existing method and propose an enhanced attack framework by slightly adjusting its training strategies. Then, we analogize patch optimization with regular model optimization, proposing a series of self-ensemble approaches on the input data, the attacked model, and the adversarial patch to efficiently make use of the limited information and prevent the patch from overfitting. The experimental results show that the proposed framework can be applied with multiple classical base attack methods (e.g., PGD and MIM) to greatly improve the black-box transferability of the well-optimized patch on multiple mainstream detectors, meanwhile boosting white-box performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_T-SEA_Transfer-Based_Self-Ensemble_Attack_on_Object_Detection_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_T-SEA_Transfer-Based_Self-Ensemble_Attack_on_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_T-SEA_Transfer-Based_Self-Ensemble_Attack_on_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
LiDAR2Map: In Defense of LiDAR-Based Semantic Map Construction Using Online Camera Distillation | Song Wang, Wentong Li, Wenyu Liu, Xiaolu Liu, Jianke Zhu | Semantic map construction under bird's-eye view (BEV) plays an essential role in autonomous driving. In contrast to camera image, LiDAR provides the accurate 3D observations to project the captured 3D features onto BEV space inherently. However, the vanilla LiDAR-based BEV feature often contains many indefinite noises, where the spatial features have little texture and semantic cues. In this paper, we propose an effective LiDAR-based method to build semantic map. Specifically, we introduce a BEV pyramid feature decoder that learns the robust multi-scale BEV features for semantic map construction, which greatly boosts the accuracy of the LiDAR-based method. To mitigate the defects caused by lacking semantic cues in LiDAR data, we present an online Camera-to-LiDAR distillation scheme to facilitate the semantic learning from image to point cloud. Our distillation scheme consists of feature-level and logit-level distillation to absorb the semantic information from camera in BEV. The experimental results on challenging nuScenes dataset demonstrate the efficacy of our proposed LiDAR2Map on semantic map construction, which significantly outperforms the previous LiDAR-based methods over 27.9% mIoU and even performs better than the state-of-the-art camera-based approaches. Source code is available at: https://github.com/songw-zju/LiDAR2Map. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_LiDAR2Map_In_Defense_of_LiDAR-Based_Semantic_Map_Construction_Using_Online_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_LiDAR2Map_In_Defense_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.11379 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_LiDAR2Map_In_Defense_of_LiDAR-Based_Semantic_Map_Construction_Using_Online_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_LiDAR2Map_In_Defense_of_LiDAR-Based_Semantic_Map_Construction_Using_Online_CVPR_2023_paper.html | CVPR 2023 | null |
NewsNet: A Novel Dataset for Hierarchical Temporal Segmentation | Haoqian Wu, Keyu Chen, Haozhe Liu, Mingchen Zhuge, Bing Li, Ruizhi Qiao, Xiujun Shu, Bei Gan, Liangsheng Xu, Bo Ren, Mengmeng Xu, Wentian Zhang, Raghavendra Ramachandra, Chia-Wen Lin, Bernard Ghanem | Temporal video segmentation is the get-to-go automatic video analysis, which decomposes a long-form video into smaller components for the following-up understanding tasks. Recent works have studied several levels of granularity to segment a video, such as shot, event, and scene. Those segmentations can help compare the semantics in the corresponding scales, but lack a wider view of larger temporal spans, especially when the video is complex and structured. Therefore, we present two abstractive levels of temporal segmentations and study their hierarchy to the existing fine-grained levels. Accordingly, we collect NewsNet, the largest news video dataset consisting of 1,000 videos in over 900 hours, associated with several tasks for hierarchical temporal video segmentation. Each news video is a collection of stories on different topics, represented as aligned audio, visual, and textual data, along with extensive frame-wise annotations in four granularities. We assert that the study on NewsNet can advance the understanding of complex structured video and benefit more areas such as short-video creation, personalized advertisement, digital instruction, and education. Our dataset and code is publicly available at: https://github.com/NewsNet-Benchmark/NewsNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_NewsNet_A_Novel_Dataset_for_Hierarchical_Temporal_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_NewsNet_A_Novel_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_NewsNet_A_Novel_Dataset_for_Hierarchical_Temporal_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_NewsNet_A_Novel_Dataset_for_Hierarchical_Temporal_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Token Contrast for Weakly-Supervised Semantic Segmentation | Lixiang Ru, Heliang Zheng, Yibing Zhan, Bo Du | Weakly-Supervised Semantic Segmentation (WSSS) using image-level labels typically utilizes Class Activation Map (CAM) to generate the pseudo labels. Limited by the local structure perception of CNN, CAM usually cannot identify the integral object regions. Though the recent Vision Transformer (ViT) can remedy this flaw, we observe it also brings the over-smoothing issue, ie, the final patch tokens incline to be uniform. In this work, we propose Token Contrast (ToCo) to address this issue and further explore the virtue of ViT for WSSS. Firstly, motivated by the observation that intermediate layers in ViT can still retain semantic diversity, we designed a Patch Token Contrast module (PTC). PTC supervises the final patch tokens with the pseudo token relations derived from intermediate layers, allowing them to align the semantic regions and thus yield more accurate CAM. Secondly, to further differentiate the low-confidence regions in CAM, we devised a Class Token Contrast module (CTC) inspired by the fact that class tokens in ViT can capture high-level semantics. CTC facilitates the representation consistency between uncertain local regions and global objects by contrasting their class tokens. Experiments on the PASCAL VOC and MS COCO datasets show the proposed ToCo can remarkably surpass other single-stage competitors and achieve comparable performance with state-of-the-art multi-stage methods. Code is available at https://github.com/rulixiang/ToCo. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ru_Token_Contrast_for_Weakly-Supervised_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ru_Token_Contrast_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.01267 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ru_Token_Contrast_for_Weakly-Supervised_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ru_Token_Contrast_for_Weakly-Supervised_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
LightedDepth: Video Depth Estimation in Light of Limited Inference View Angles | Shengjie Zhu, Xiaoming Liu | Video depth estimation infers the dense scene depth from immediate neighboring video frames. While recent works consider it a simplified structure-from-motion (SfM) problem, it still differs from the SfM in that significantly fewer view angels are available in inference. This setting, however, suits the mono-depth and optical flow estimation. This observation motivates us to decouple the video depth estimation into two components, a normalized pose estimation over a flowmap and a logged residual depth estimation over a mono-depth map. The two parts are unified with an efficient off-the-shelf scale alignment algorithm. Additionally, we stabilize the indoor two-view pose estimation by including additional projection constraints and ensuring sufficient camera translation. Though a two-view algorithm, we validate the benefit of the decoupling with the substantial performance improvement over multi-view iterative prior works on indoor and outdoor datasets. Codes and models are available at https://github.com/ShngJZ/LightedDepth. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_LightedDepth_Video_Depth_Estimation_in_Light_of_Limited_Inference_View_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_LightedDepth_Video_Depth_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_LightedDepth_Video_Depth_Estimation_in_Light_of_Limited_Inference_View_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_LightedDepth_Video_Depth_Estimation_in_Light_of_Limited_Inference_View_CVPR_2023_paper.html | CVPR 2023 | null |
Uncertainty-Aware Unsupervised Image Deblurring With Deep Residual Prior | Xiaole Tang, Xile Zhao, Jun Liu, Jianli Wang, Yuchun Miao, Tieyong Zeng | Non-blind deblurring methods achieve decent performance under the accurate blur kernel assumption. Since the kernel uncertainty (i.e. kernel error) is inevitable in practice, semi-blind deblurring is suggested to handle it by introducing the prior of the kernel (or induced) error. However, how to design a suitable prior for the kernel (or induced) error remains challenging. Hand-crafted prior, incorporating domain knowledge, generally performs well but may lead to poor performance when kernel (or induced) error is complex. Data-driven prior, which excessively depends on the diversity and abundance of training data, is vulnerable to out-of-distribution blurs and images. To address this challenge, we suggest a dataset-free deep residual prior for the kernel induced error (termed as residual) expressed by a customized untrained deep neural network, which allows us to flexibly adapt to different blurs and images in real scenarios. By organically integrating the respective strengths of deep priors and hand-crafted priors, we propose an unsupervised semi-blind deblurring model which recovers the latent image from the blurry image and inaccurate blur kernel. To tackle the formulated model, an efficient alternating minimization algorithm is developed. Extensive experiments demonstrate the favorable performance of the proposed method as compared to model-driven and data-driven methods in terms of image quality and the robustness to different types of kernel error. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tang_Uncertainty-Aware_Unsupervised_Image_Deblurring_With_Deep_Residual_Prior_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tang_Uncertainty-Aware_Unsupervised_Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2210.05361 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Uncertainty-Aware_Unsupervised_Image_Deblurring_With_Deep_Residual_Prior_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Uncertainty-Aware_Unsupervised_Image_Deblurring_With_Deep_Residual_Prior_CVPR_2023_paper.html | CVPR 2023 | null |
HouseDiffusion: Vector Floorplan Generation via a Diffusion Model With Discrete and Continuous Denoising | Mohammad Amin Shabani, Sepidehsadat Hosseini, Yasutaka Furukawa | The paper presents a novel approach for vector-floorplan generation via a diffusion model, which denoises 2D coordinates of room/door corners with two inference objectives: 1) a single-step noise as the continuous quantity to precisely invert the continuous forward process; and 2) the final 2D coordinate as the discrete quantity to establish geometric incident relationships such as parallelism, orthogonality, and corner-sharing. Our task is graph-conditioned floorplan generation, a common workflow in floorplan design. We represent a floorplan as 1D polygonal loops, each of which corresponds to a room or a door. Our diffusion model employs a Transformer architecture at the core, which controls the attention masks based on the input graph-constraint and directly generates vector-graphics floorplans via a discrete and continuous denoising process. We have evaluated our approach on RPLAN dataset. The proposed approach makes significant improvements in all the metrics against the state-of-the-art with significant margins, while being capable of generating non-Manhattan structures and controlling the exact number of corners per room. We will share all our code and models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shabani_HouseDiffusion_Vector_Floorplan_Generation_via_a_Diffusion_Model_With_Discrete_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shabani_HouseDiffusion_Vector_Floorplan_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.13287 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shabani_HouseDiffusion_Vector_Floorplan_Generation_via_a_Diffusion_Model_With_Discrete_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shabani_HouseDiffusion_Vector_Floorplan_Generation_via_a_Diffusion_Model_With_Discrete_CVPR_2023_paper.html | CVPR 2023 | null |
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning | Yuanhao Xiong, Ruochen Wang, Minhao Cheng, Felix Yu, Cho-Jui Hsieh | Federated learning (FL) has recently attracted increasing attention from academia and industry, with the ultimate goal of achieving collaborative training under privacy and communication constraints. Existing iterative model averaging based FL algorithms require a large number of communication rounds to obtain a well-performed model due to extremely unbalanced and non-i.i.d data partitioning among different clients. Thus, we propose FedDM to build the global training objective from multiple local surrogate functions, which enables the server to gain a more global view of the loss landscape. In detail, we construct synthetic sets of data on each client to locally match the loss landscape from original data through distribution matching. FedDM reduces communication rounds and improves model quality by transmitting more informative and smaller synthesized data compared with unwieldy model weights. We conduct extensive experiments on three image classification datasets, and results show that our method can outperform other FL counterparts in terms of efficiency and model performance. Moreover, we demonstrate that FedDM can be adapted to preserve differential privacy with Gaussian mechanism and train a better model under the same privacy budget. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xiong_FedDM_Iterative_Distribution_Matching_for_Communication-Efficient_Federated_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xiong_FedDM_Iterative_Distribution_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2207.09653 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xiong_FedDM_Iterative_Distribution_Matching_for_Communication-Efficient_Federated_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xiong_FedDM_Iterative_Distribution_Matching_for_Communication-Efficient_Federated_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting | Haibao Yu, Wenxian Yang, Hongzhi Ruan, Zhenwei Yang, Yingjuan Tang, Xu Gao, Xin Hao, Yifeng Shi, Yifeng Pan, Ning Sun, Juan Song, Jirui Yuan, Ping Luo, Zaiqing Nie | Utilizing infrastructure and vehicle-side information to track and forecast the behaviors of surrounding traffic participants can significantly improve decision-making and safety in autonomous driving. However, the lack of real-world sequential datasets limits research in this area. To address this issue, we introduce V2X-Seq, the first large-scale sequential V2X dataset, which includes data frames, trajectories, vector maps, and traffic lights captured from natural scenery. V2X-Seq comprises two parts: the sequential perception dataset, which includes more than 15,000 frames captured from 95 scenarios, and the trajectory forecasting dataset, which contains about 80,000 infrastructure-view scenarios, 80,000 vehicle-view scenarios, and 50,000 cooperative-view scenarios captured from 28 intersections' areas, covering 672 hours of data. Based on V2X-Seq, we introduce three new tasks for vehicle-infrastructure cooperative (VIC) autonomous driving: VIC3D Tracking, Online-VIC Forecasting, and Offline-VIC Forecasting. We also provide benchmarks for the introduced tasks. Find data, code, and more up-to-date information at https://github.com/AIR-THU/DAIR-V2X-Seq. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_V2X-Seq_A_Large-Scale_Sequential_Dataset_for_Vehicle-Infrastructure_Cooperative_Perception_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_V2X-Seq_A_Large-Scale_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_V2X-Seq_A_Large-Scale_Sequential_Dataset_for_Vehicle-Infrastructure_Cooperative_Perception_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_V2X-Seq_A_Large-Scale_Sequential_Dataset_for_Vehicle-Infrastructure_Cooperative_Perception_and_CVPR_2023_paper.html | CVPR 2023 | null |
PSVT: End-to-End Multi-Person 3D Pose and Shape Estimation With Progressive Video Transformers | Zhongwei Qiu, Qiansheng Yang, Jian Wang, Haocheng Feng, Junyu Han, Errui Ding, Chang Xu, Dongmei Fu, Jingdong Wang | Existing methods of multi-person video 3D human Pose and Shape Estimation (PSE) typically adopt a two-stage strategy, which first detects human instances in each frame and then performs single-person PSE with temporal model. However, the global spatio-temporal context among spatial instances can not be captured. In this paper, we propose a new end-to-end multi-person 3D Pose and Shape estimation framework with progressive Video Transformer, termed PSVT. In PSVT, a spatio-temporal encoder (STE) captures the global feature dependencies among spatial objects. Then, spatio-temporal pose decoder (STPD) and shape decoder (STSD) capture the global dependencies between pose queries and feature tokens, shape queries and feature tokens, respectively. To handle the variances of objects as time proceeds, a novel scheme of progressive decoding is used to update pose and shape queries at each frame. Besides, we propose a novel pose-guided attention (PGA) for shape decoder to better predict shape parameters. The two components strengthen the decoder of PSVT to improve performance. Extensive experiments on the four datasets show that PSVT achieves stage-of-the-art results. | https://openaccess.thecvf.com/content/CVPR2023/papers/Qiu_PSVT_End-to-End_Multi-Person_3D_Pose_and_Shape_Estimation_With_Progressive_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qiu_PSVT_End-to-End_Multi-Person_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.09187 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qiu_PSVT_End-to-End_Multi-Person_3D_Pose_and_Shape_Estimation_With_Progressive_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qiu_PSVT_End-to-End_Multi-Person_3D_Pose_and_Shape_Estimation_With_Progressive_CVPR_2023_paper.html | CVPR 2023 | null |
Bit-Shrinking: Limiting Instantaneous Sharpness for Improving Post-Training Quantization | Chen Lin, Bo Peng, Zheyang Li, Wenming Tan, Ye Ren, Jun Xiao, Shiliang Pu | Post-training quantization (PTQ) is an effective compression method to reduce the model size and computational cost. However, quantizing a model into a low-bit one, e.g., lower than 4, is difficult and often results in nonnegligible performance degradation. To address this, we investigate the loss landscapes of quantized networks with various bit-widths. We show that the network with more ragged loss surface, is more easily trapped into bad local minima, which mostly appears in low-bit quantization. A deeper analysis indicates, the ragged surface is caused by the injection of excessive quantization noise. To this end, we detach a sharpness term from the loss which reflects the impact of quantization noise. To smooth the rugged loss surface, we propose to limit the sharpness term small and stable during optimization. Instead of directly optimizing the target bit network, the bit-width of quantized network has a self-adapted shrinking scheduler in continuous domain from high bit-width to the target by limiting the increasing sharpness term within a proper range. It can be viewed as iteratively adding small "instant" quantization noise and adjusting the network to eliminate its impact. Widely experiments including classification and detection tasks demonstrate the effectiveness of the Bit-shrinking strategy in PTQ. On the Vision Transformer models, our INT8 and INT6 models drop within 0.5% and 1.5% Top-1 accuracy, respectively. On the traditional CNN networks, our INT4 quantized models drop within 1.3% and 3.5% Top-1 accuracy on ResNet18 and MobileNetV2 without fine-tuning, which achieves the state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Bit-Shrinking_Limiting_Instantaneous_Sharpness_for_Improving_Post-Training_Quantization_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Bit-Shrinking_Limiting_Instantaneous_Sharpness_for_Improving_Post-Training_Quantization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Bit-Shrinking_Limiting_Instantaneous_Sharpness_for_Improving_Post-Training_Quantization_CVPR_2023_paper.html | CVPR 2023 | null |
LSTFE-Net:Long Short-Term Feature Enhancement Network for Video Small Object Detection | Jinsheng Xiao, Yuanxu Wu, Yunhua Chen, Shurui Wang, Zhongyuan Wang, Jiayi Ma | Video small object detection is a difficult task due to the lack of object information. Recent methods focus on adding more temporal information to obtain more potent high-level features, which often fail to specify the most vital information for small objects, resulting in insufficient or inappropriate features. Since information from frames at different positions contributes differently to small objects, it is not ideal to assume that using one universal method will extract proper features. We find that context information from the long-term frame and temporal information from the short-term frame are two useful cues for video small object detection. To fully utilize these two cues, we propose a long short-term feature enhancement network (LSTFE-Net) for video small object detection. First, we develop a plug-and-play spatio-temporal feature alignment module to create temporal correspondences between the short-term and current frames. Then, we propose a frame selection module to select the long-term frame that can provide the most additional context information. Finally, we propose a long short-term feature aggregation module to fuse long short-term features. Compared to other state-of-the-art methods, our LSTFE-Net achieves 4.4% absolute boosts in AP on the FL-Drones dataset. More details can be found at https://github.com/xiaojs18/LSTFE-Net. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xiao_LSTFE-NetLong_Short-Term_Feature_Enhancement_Network_for_Video_Small_Object_Detection_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xiao_LSTFE-NetLong_Short-Term_Feature_Enhancement_Network_for_Video_Small_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xiao_LSTFE-NetLong_Short-Term_Feature_Enhancement_Network_for_Video_Small_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation | Lukas Hoyer, Dengxin Dai, Haoran Wang, Luc Van Gool | In unsupervised domain adaptation (UDA), a model trained on source data (e.g. synthetic) is adapted to target data (e.g. real-world) without access to target annotation. Most previous UDA methods struggle with classes that have a similar visual appearance on the target domain as no ground truth is available to learn the slight appearance differences. To address this problem, we propose a Masked Image Consistency (MIC) module to enhance UDA by learning spatial context relations of the target domain as additional clues for robust visual recognition. MIC enforces the consistency between predictions of masked target images, where random patches are withheld, and pseudo-labels that are generated based on the complete image by an exponential moving average teacher. To minimize the consistency loss, the network has to learn to infer the predictions of the masked regions from their context. Due to its simple and universal concept, MIC can be integrated into various UDA methods across different visual recognition tasks such as image classification, semantic segmentation, and object detection. MIC significantly improves the state-of-the-art performance across the different recognition tasks for synthetic-to-real, day-to-nighttime, and clear-to-adverse-weather UDA. For instance, MIC achieves an unprecedented UDA performance of 75.9 mIoU and 92.8% on GTA-to-Cityscapes and VisDA-2017, respectively, which corresponds to an improvement of +2.1 and +3.0 percent points over the previous state of the art. The implementation is available at https://github.com/lhoyer/MIC. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hoyer_MIC_Masked_Image_Consistency_for_Context-Enhanced_Domain_Adaptation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hoyer_MIC_Masked_Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.01322 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hoyer_MIC_Masked_Image_Consistency_for_Context-Enhanced_Domain_Adaptation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hoyer_MIC_Masked_Image_Consistency_for_Context-Enhanced_Domain_Adaptation_CVPR_2023_paper.html | CVPR 2023 | null |
Bridging the Gap Between Model Explanations in Partially Annotated Multi-Label Classification | Youngwook Kim, Jae Myung Kim, Jieun Jeong, Cordelia Schmid, Zeynep Akata, Jungwoo Lee | Due to the expensive costs of collecting labels in multi-label classification datasets, partially annotated multi-label classification has become an emerging field in computer vision. One baseline approach to this task is to assume unobserved labels as negative labels, but this assumption induces label noise as a form of false negative. To understand the negative impact caused by false negative labels, we study how these labels affect the model's explanation. We observe that the explanation of two models, trained with full and partial labels each, highlights similar regions but with different scaling, where the latter tends to have lower attribution scores. Based on these findings, we propose to boost the attribution scores of the model trained with partial labels to make its explanation resemble that of the model trained with full labels. Even with the conceptually simple approach, the multi-label classification performance improves by a large margin in three different datasets on a single positive label setting and one on a large-scale partial label setting. Code is available at https://github.com/youngwk/BridgeGapExplanationPAMC. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_Bridging_the_Gap_Between_Model_Explanations_in_Partially_Annotated_Multi-Label_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_Bridging_the_Gap_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01804 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Bridging_the_Gap_Between_Model_Explanations_in_Partially_Annotated_Multi-Label_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Bridging_the_Gap_Between_Model_Explanations_in_Partially_Annotated_Multi-Label_CVPR_2023_paper.html | CVPR 2023 | null |
SkyEye: Self-Supervised Bird's-Eye-View Semantic Mapping Using Monocular Frontal View Images | Nikhil Gosala, Kürsat Petek, Paulo L. J. Drews-Jr, Wolfram Burgard, Abhinav Valada | Bird's-Eye-View (BEV) semantic maps have become an essential component of automated driving pipelines due to the rich representation they provide for decision-making tasks. However, existing approaches for generating these maps still follow a fully supervised training paradigm and hence rely on large amounts of annotated BEV data. In this work, we address this limitation by proposing the first self-supervised approach for generating a BEV semantic map using a single monocular image from the frontal view (FV). During training, we overcome the need for BEV ground truth annotations by leveraging the more easily available FV semantic annotations of video sequences. Thus, we propose the SkyEye architecture that learns based on two modes of self-supervision, namely, implicit supervision and explicit supervision. Implicit supervision trains the model by enforcing spatial consistency of the scene over time based on FV semantic sequences, while explicit supervision exploits BEV pseudolabels generated from FV semantic annotations and self-supervised depth estimates. Extensive evaluations on the KITTI-360 dataset demonstrate that our self-supervised approach performs on par with the state-of-the-art fully supervised methods and achieves competitive results using only 1% of direct supervision in BEV compared to fully supervised approaches. Finally, we publicly release both our code and the BEV datasets generated from the KITTI-360 and Waymo datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gosala_SkyEye_Self-Supervised_Birds-Eye-View_Semantic_Mapping_Using_Monocular_Frontal_View_Images_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gosala_SkyEye_Self-Supervised_Birds-Eye-View_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gosala_SkyEye_Self-Supervised_Birds-Eye-View_Semantic_Mapping_Using_Monocular_Frontal_View_Images_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gosala_SkyEye_Self-Supervised_Birds-Eye-View_Semantic_Mapping_Using_Monocular_Frontal_View_Images_CVPR_2023_paper.html | CVPR 2023 | null |
Unifying Vision, Text, and Layout for Universal Document Processing | Zineng Tang, Ziyi Yang, Guoxin Wang, Yuwei Fang, Yang Liu, Chenguang Zhu, Michael Zeng, Cha Zhang, Mohit Bansal | We propose Universal Document Processing (UDOP), a foundation Document AI model which unifies text, image, and layout modalities together with varied task formats, including document understanding and generation. UDOP leverages the spatial correlation between textual content and document image to model image, text, and layout modalities with one uniform representation. With a novel Vision-Text-Layout Transformer, UDOP unifies pretraining and multi-domain downstream tasks into a prompt-based sequence generation scheme. UDOP is pretrained on both large-scale unlabeled document corpora using innovative self-supervised objectives and diverse labeled data. UDOP also learns to generate document images from text and layout modalities via masked image reconstruction. To the best of our knowledge, this is the first time in the field of document AI that one model simultaneously achieves high-quality neural document editing and content customization. Our method sets the state-of-the-art on 8 Document AI tasks, e.g., document understanding and QA, across diverse data domains like finance reports, academic papers, and websites. UDOP ranks first on the leaderboard of the Document Understanding Benchmark. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tang_Unifying_Vision_Text_and_Layout_for_Universal_Document_Processing_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tang_Unifying_Vision_Text_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.02623 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Unifying_Vision_Text_and_Layout_for_Universal_Document_Processing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Unifying_Vision_Text_and_Layout_for_Universal_Document_Processing_CVPR_2023_paper.html | CVPR 2023 | null |
SparsePose: Sparse-View Camera Pose Regression and Refinement | Samarth Sinha, Jason Y. Zhang, Andrea Tagliasacchi, Igor Gilitschenski, David B. Lindell | Camera pose estimation is a key step in standard 3D reconstruction pipelines that operates on a dense set of images of a single object or scene. However, methods for pose estimation often fail when there are only a few images available because they rely on the ability to robustly identify and match visual features between pairs of images. While these methods can work robustly with dense camera views, capturing a large set of images can be time consuming or impractical. Here, we propose Sparse-View Camera Pose Regression and Refinement (SparsePose) for recovering accurate camera poses given a sparse set of wide-baseline images (fewer than 10). The method learns to regress initial camera poses and then iteratively refine them after training on a large-scale dataset of objects (Co3D: Common Objects in 3D). SparsePose significantly outperforms conventional and learning-based baselines in recovering accurate camera rotations and translations. We also demonstrate our pipeline for high-fidelity 3D reconstruction using only 5-9 images of an object. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sinha_SparsePose_Sparse-View_Camera_Pose_Regression_and_Refinement_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sinha_SparsePose_Sparse-View_Camera_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.16991 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sinha_SparsePose_Sparse-View_Camera_Pose_Regression_and_Refinement_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sinha_SparsePose_Sparse-View_Camera_Pose_Regression_and_Refinement_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Audio-Visual Source Localization via False Negative Aware Contrastive Learning | Weixuan Sun, Jiayi Zhang, Jianyuan Wang, Zheyuan Liu, Yiran Zhong, Tianpeng Feng, Yandong Guo, Yanhao Zhang, Nick Barnes | Self-supervised audio-visual source localization aims to locate sound-source objects in video frames without extra annotations. Recent methods often approach this goal with the help of contrastive learning, which assumes only the audio and visual contents from the same video are positive samples for each other. However, this assumption would suffer from false negative samples in real-world training. For example, for an audio sample, treating the frames from the same audio class as negative samples may mislead the model and therefore harm the learned representations (e.g., the audio of a siren wailing may reasonably correspond to the ambulances in multiple images). Based on this observation, we propose a new learning strategy named False Negative Aware Contrastive (FNAC) to mitigate the problem of misleading the training with such false negative samples. Specifically, we utilize the intra-modal similarities to identify potentially similar samples and construct corresponding adjacency matrices to guide contrastive learning. Further, we propose to strengthen the role of true negative samples by explicitly leveraging the visual features of sound sources to facilitate the differentiation of authentic sounding source regions. FNAC achieves state-of-the-art performances on Flickr-SoundNet, VGG-Sound, and AVSBench, which demonstrates the effectiveness of our method in mitigating the false negative issue. The code is available at https://github.com/OpenNLPLab/FNAC_AVL | https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_Learning_Audio-Visual_Source_Localization_via_False_Negative_Aware_Contrastive_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sun_Learning_Audio-Visual_Source_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11302 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Learning_Audio-Visual_Source_Localization_via_False_Negative_Aware_Contrastive_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Learning_Audio-Visual_Source_Localization_via_False_Negative_Aware_Contrastive_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
VoxFormer: Sparse Voxel Transformer for Camera-Based 3D Semantic Scene Completion | Yiming Li, Zhiding Yu, Christopher Choy, Chaowei Xiao, Jose M. Alvarez, Sanja Fidler, Chen Feng, Anima Anandkumar | Humans can easily imagine the complete 3D geometry of occluded objects and scenes. This appealing ability is vital for recognition and understanding. To enable such capability in AI systems, we propose VoxFormer, a Transformer-based semantic scene completion framework that can output complete 3D volumetric semantics from only 2D images. Our framework adopts a two-stage design where we start from a sparse set of visible and occupied voxel queries from depth estimation, followed by a densification stage that generates dense 3D voxels from the sparse ones. A key idea of this design is that the visual features on 2D images correspond only to the visible scene structures rather than the occluded or empty spaces. Therefore, starting with the featurization and prediction of the visible structures is more reliable. Once we obtain the set of sparse queries, we apply a masked autoencoder design to propagate the information to all the voxels by self-attention. Experiments on SemanticKITTI show that VoxFormer outperforms the state of the art with a relative improvement of 20.0% in geometry and 18.1% in semantics and reduces GPU memory during training to less than 16GB. Our code is available on https://github.com/NVlabs/VoxFormer. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_VoxFormer_Sparse_Voxel_Transformer_for_Camera-Based_3D_Semantic_Scene_Completion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_VoxFormer_Sparse_Voxel_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.12251 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_VoxFormer_Sparse_Voxel_Transformer_for_Camera-Based_3D_Semantic_Scene_Completion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_VoxFormer_Sparse_Voxel_Transformer_for_Camera-Based_3D_Semantic_Scene_Completion_CVPR_2023_paper.html | CVPR 2023 | null |
Joint Video Multi-Frame Interpolation and Deblurring Under Unknown Exposure Time | Wei Shang, Dongwei Ren, Yi Yang, Hongzhi Zhang, Kede Ma, Wangmeng Zuo | Natural videos captured by consumer cameras often suffer from low framerate and motion blur due to the combination of dynamic scene complexity, lens and sensor imperfection, and less than ideal exposure setting. As a result, computational methods that jointly perform video frame interpolation and deblurring begin to emerge with the unrealistic assumption that the exposure time is known and fixed. In this work, we aim ambitiously for a more realistic and challenging task - joint video multi-frame interpolation and deblurring under unknown exposure time. Toward this goal, we first adopt a variant of supervised contrastive learning to construct an exposure-aware representation from input blurred frames. We then train two U-Nets for intra-motion and inter-motion analysis, respectively, adapting to the learned exposure representation via gain tuning. We finally build our video reconstruction network upon the exposure and motion representation by progressive exposure-adaptive convolution and motion refinement. Extensive experiments on both simulated and real-world datasets show that our optimized method achieves notable performance gains over the state-of-the-art on the joint video x8 interpolation and deblurring task. Moreover, on the seemingly implausible x16 interpolation task, our method outperforms existing methods by more than 1.5 dB in terms of PSNR. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shang_Joint_Video_Multi-Frame_Interpolation_and_Deblurring_Under_Unknown_Exposure_Time_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shang_Joint_Video_Multi-Frame_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15043 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shang_Joint_Video_Multi-Frame_Interpolation_and_Deblurring_Under_Unknown_Exposure_Time_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shang_Joint_Video_Multi-Frame_Interpolation_and_Deblurring_Under_Unknown_Exposure_Time_CVPR_2023_paper.html | CVPR 2023 | null |
Flow Supervision for Deformable NeRF | Chaoyang Wang, Lachlan Ewen MacDonald, László A. Jeni, Simon Lucey | In this paper we present a new method for deformable NeRF that can directly use optical flow as supervision. We overcome the major challenge with respect to the computationally inefficiency of enforcing the flow constraints to the backward deformation field, used by deformable NeRFs. Specifically, we show that inverting the backward deformation function is actually not needed for computing scene flows between frames. This insight dramatically simplifies the problem, as one is no longer constrained to deformation functions that can be analytically inverted. Instead, thanks to the weak assumptions required by our derivation based on the inverse function theorem, our approach can be extended to a broad class of commonly used backward deformation field. We present results on monocular novel view synthesis with rapid object motion, and demonstrate significant improvements over baselines without flow supervision. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Flow_Supervision_for_Deformable_NeRF_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Flow_Supervision_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16333 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Flow_Supervision_for_Deformable_NeRF_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Flow_Supervision_for_Deformable_NeRF_CVPR_2023_paper.html | CVPR 2023 | null |
MMG-Ego4D: Multimodal Generalization in Egocentric Action Recognition | Xinyu Gong, Sreyas Mohan, Naina Dhingra, Jean-Charles Bazin, Yilei Li, Zhangyang Wang, Rakesh Ranjan | In this paper, we study a novel problem in egocentric action recognition, which we term as "Multimodal Generalization" (MMG). MMG aims to study how systems can generalize when data from certain modalities is limited or even completely missing. We thoroughly investigate MMG in the context of standard supervised action recognition and the more challenging few-shot setting for learning new action categories. MMG consists of two novel scenarios, designed to support security, and efficiency considerations in real-world applications: (1) missing modality generalization where some modalities that were present during the train time are missing during the inference time, and (2) cross-modal zero-shot generalization, where the modalities present during the inference time and the training time are disjoint. To enable this investigation, we construct a new dataset MMG-Ego4D containing data points with video, audio, and inertial motion sensor (IMU) modalities. Our dataset is derived from Ego4D dataset, but processed and thoroughly re-annotated by human experts to facilitate research in the MMG problem. We evaluate a diverse array of models on MMG-Ego4D and propose new methods with improved generalization ability. In particular, we introduce a new fusion module with modality dropout training, contrastive-based alignment training, and a novel cross-modal prototypical loss for better few-shot performance. We hope this study will serve as a benchmark and guide future research in multimodal generalization problems. The benchmark and code are available at https://github.com/facebookresearch/MMG_Ego4D | https://openaccess.thecvf.com/content/CVPR2023/papers/Gong_MMG-Ego4D_Multimodal_Generalization_in_Egocentric_Action_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gong_MMG-Ego4D_Multimodal_Generalization_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gong_MMG-Ego4D_Multimodal_Generalization_in_Egocentric_Action_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gong_MMG-Ego4D_Multimodal_Generalization_in_Egocentric_Action_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Zero-Shot Text-to-Parameter Translation for Game Character Auto-Creation | Rui Zhao, Wei Li, Zhipeng Hu, Lincheng Li, Zhengxia Zou, Zhenwei Shi, Changjie Fan | Recent popular Role-Playing Games (RPGs) saw the great success of character auto-creation systems. The bone-driven face model controlled by continuous parameters (like the position of bones) and discrete parameters (like the hairstyles) makes it possible for users to personalize and customize in-game characters. Previous in-game character auto-creation systems are mostly image-driven, where facial parameters are optimized so that the rendered character looks similar to the reference face photo. This paper proposes a novel text-to-parameter translation method (T2P) to achieve zero-shot text-driven game character auto-creation. With our method, users can create a vivid in-game character with arbitrary text description without using any reference photo or editing hundreds of parameters manually. In our method, taking the power of large-scale pre-trained multi-modal CLIP and neural rendering, T2P searches both continuous facial parameters and discrete facial parameters in a unified framework. Due to the discontinuous parameter representation, previous methods have difficulty in effectively learning discrete facial parameters. T2P, to our best knowledge, is the first method that can handle the optimization of both discrete and continuous parameters. Experimental results show that T2P can generate high-quality and vivid game characters with given text prompts. T2P outperforms other SOTA text-to-3D generation methods on both objective evaluations and subjective evaluations. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_Zero-Shot_Text-to-Parameter_Translation_for_Game_Character_Auto-Creation_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.01311 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Zero-Shot_Text-to-Parameter_Translation_for_Game_Character_Auto-Creation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Zero-Shot_Text-to-Parameter_Translation_for_Game_Character_Auto-Creation_CVPR_2023_paper.html | CVPR 2023 | null |
PIVOT: Prompting for Video Continual Learning | Andrés Villa, Juan León Alcázar, Motasem Alfarra, Kumail Alhamoud, Julio Hurtado, Fabian Caba Heilbron, Alvaro Soto, Bernard Ghanem | Modern machine learning pipelines are limited due to data availability, storage quotas, privacy regulations, and expensive annotation processes. These constraints make it difficult or impossible to train and update large-scale models on such dynamic annotated sets. Continual learning directly approaches this problem, with the ultimate goal of devising methods where a deep neural network effectively learns relevant patterns for new (unseen) classes, without significantly altering its performance on previously learned ones. In this paper, we address the problem of continual learning for video data. We introduce PIVOT, a novel method that leverages extensive knowledge in pre-trained models from the image domain, thereby reducing the number of trainable parameters and the associated forgetting. Unlike previous methods, ours is the first approach that effectively uses prompting mechanisms for continual learning without any in-domain pre-training. Our experiments show that PIVOT improves state-of-the-art methods by a significant 27% on the 20-task ActivityNet setup. | https://openaccess.thecvf.com/content/CVPR2023/papers/Villa_PIVOT_Prompting_for_Video_Continual_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Villa_PIVOT_Prompting_for_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Villa_PIVOT_Prompting_for_Video_Continual_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Villa_PIVOT_Prompting_for_Video_Continual_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Dual-Bridging With Adversarial Noise Generation for Domain Adaptive rPPG Estimation | Jingda Du, Si-Qi Liu, Bochao Zhang, Pong C. Yuen | The remote photoplethysmography (rPPG) technique can estimate pulse-related metrics (e.g. heart rate and respiratory rate) from facial videos and has a high potential for health monitoring. The latest deep rPPG methods can model in-distribution noise due to head motion, video compression, etc., and estimate high-quality rPPG signals under similar scenarios. However, deep rPPG models may not generalize well to the target test domain with unseen noise and distortions. In this paper, to improve the generalization ability of rPPG models, we propose a dual-bridging network to reduce the domain discrepancy by aligning intermediate domains and synthesizing the target noise in the source domain for better noise reduction. To comprehensively explore the target domain noise, we propose a novel adversarial noise generation in which the noise generator indirectly competes with the noise reducer. To further improve the robustness of the noise reducer, we propose hard noise pattern mining to encourage the generator to learn hard noise patterns contained in the target domain features. We evaluated the proposed method on three public datasets with different types of interferences. Under different cross-domain scenarios, the comprehensive results show the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Du_Dual-Bridging_With_Adversarial_Noise_Generation_for_Domain_Adaptive_rPPG_Estimation_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Dual-Bridging_With_Adversarial_Noise_Generation_for_Domain_Adaptive_rPPG_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Dual-Bridging_With_Adversarial_Noise_Generation_for_Domain_Adaptive_rPPG_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
Panoptic Video Scene Graph Generation | Jingkang Yang, Wenxuan Peng, Xiangtai Li, Zujin Guo, Liangyu Chen, Bo Li, Zheng Ma, Kaiyang Zhou, Wayne Zhang, Chen Change Loy, Ziwei Liu | Towards building comprehensive real-world visual perception systems, we propose and study a new problem called panoptic scene graph generation (PVSG). PVSG is related to the existing video scene graph generation (VidSGG) problem, which focuses on temporal interactions between humans and objects localized with bounding boxes in videos. However, the limitation of bounding boxes in detecting non-rigid objects and backgrounds often causes VidSGG systems to miss key details that are crucial for comprehensive video understanding. In contrast, PVSG requires nodes in scene graphs to be grounded by more precise, pixel-level segmentation masks, which facilitate holistic scene understanding. To advance research in this new area, we contribute a high-quality PVSG dataset, which consists of 400 videos (289 third-person + 111 egocentric videos) with totally 150K frames labeled with panoptic segmentation masks as well as fine, temporal scene graphs. We also provide a variety of baseline methods and share useful design practices for future work. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Panoptic_Video_Scene_Graph_Generation_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Panoptic_Video_Scene_Graph_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Panoptic_Video_Scene_Graph_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
3D Video Object Detection With Learnable Object-Centric Global Optimization | Jiawei He, Yuntao Chen, Naiyan Wang, Zhaoxiang Zhang | We explore long-term temporal visual correspondence-based optimization for 3D video object detection in this work. Visual correspondence refers to one-to-one mappings for pixels across multiple images. Correspondence-based optimization is the cornerstone for 3D scene reconstruction but is less studied in 3D video object detection, because moving objects violate multi-view geometry constraints and are treated as outliers during scene reconstruction. We address this issue by treating objects as first-class citizens during correspondence-based optimization. In this work, we propose BA-Det, an end-to-end optimizable object detector with object-centric temporal correspondence learning and featuremetric object bundle adjustment. Empirically, we verify the effectiveness and efficiency of BA-Det for multiple baseline 3D detectors under various setups. Our BA-Det achieves SOTA performance on the large-scale Waymo Open Dataset (WOD) with only marginal computation cost. Our code is available at https://github.com/jiaweihe1996/BA-Det. | https://openaccess.thecvf.com/content/CVPR2023/papers/He_3D_Video_Object_Detection_With_Learnable_Object-Centric_Global_Optimization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/He_3D_Video_Object_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15416 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/He_3D_Video_Object_Detection_With_Learnable_Object-Centric_Global_Optimization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/He_3D_Video_Object_Detection_With_Learnable_Object-Centric_Global_Optimization_CVPR_2023_paper.html | CVPR 2023 | null |
Improving the Transferability of Adversarial Samples by Path-Augmented Method | Jianping Zhang, Jen-tse Huang, Wenxuan Wang, Yichen Li, Weibin Wu, Xiaosen Wang, Yuxin Su, Michael R. Lyu | Deep neural networks have achieved unprecedented success on diverse vision tasks. However, they are vulnerable to adversarial noise that is imperceptible to humans. This phenomenon negatively affects their deployment in real-world scenarios, especially security-related ones. To evaluate the robustness of a target model in practice, transfer-based attacks craft adversarial samples with a local model and have attracted increasing attention from researchers due to their high efficiency. The state-of-the-art transfer-based attacks are generally based on data augmentation, which typically augments multiple training images from a linear path when learning adversarial samples. However, such methods selected the image augmentation path heuristically and may augment images that are semantics-inconsistent with the target images, which harms the transferability of the generated adversarial samples. To overcome the pitfall, we propose the Path-Augmented Method (PAM). Specifically, PAM first constructs a candidate augmentation path pool. It then settles the employed augmentation paths during adversarial sample generation with greedy search. Furthermore, to avoid augmenting semantics-inconsistent images, we train a Semantics Predictor (SP) to constrain the length of the augmentation path. Extensive experiments confirm that PAM can achieve an improvement of over 4.8% on average compared with the state-of-the-art baselines in terms of the attack success rates. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Improving_the_Transferability_of_Adversarial_Samples_by_Path-Augmented_Method_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.15735 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Improving_the_Transferability_of_Adversarial_Samples_by_Path-Augmented_Method_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Improving_the_Transferability_of_Adversarial_Samples_by_Path-Augmented_Method_CVPR_2023_paper.html | CVPR 2023 | null |
Robust Mean Teacher for Continual and Gradual Test-Time Adaptation | Mario Döbler, Robert A. Marsden, Bin Yang | Since experiencing domain shifts during test-time is inevitable in practice, test-time adaption (TTA) continues to adapt the model after deployment. Recently, the area of continual and gradual test-time adaptation (TTA) emerged. In contrast to standard TTA, continual TTA considers not only a single domain shift, but a sequence of shifts. Gradual TTA further exploits the property that some shifts evolve gradually over time. Since in both settings long test sequences are present, error accumulation needs to be addressed for methods relying on self-training. In this work, we propose and show that in the setting of TTA, the symmetric cross-entropy is better suited as a consistency loss for mean teachers compared to the commonly used cross-entropy. This is justified by our analysis with respect to the (symmetric) cross-entropy's gradient properties. To pull the test feature space closer to the source domain, where the pre-trained model is well posed, contrastive learning is leveraged. Since applications differ in their requirements, we address several settings, including having source data available and the more challenging source-free setting. We demonstrate the effectiveness of our proposed method "robust mean teacher" (RMT) on the continual and gradual corruption benchmarks CIFAR10C, CIFAR100C, and Imagenet-C. We further consider ImageNet-R and propose a new continual DomainNet-126 benchmark. State-of-the-art results are achieved on all benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dobler_Robust_Mean_Teacher_for_Continual_and_Gradual_Test-Time_Adaptation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dobler_Robust_Mean_Teacher_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dobler_Robust_Mean_Teacher_for_Continual_and_Gradual_Test-Time_Adaptation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dobler_Robust_Mean_Teacher_for_Continual_and_Gradual_Test-Time_Adaptation_CVPR_2023_paper.html | CVPR 2023 | null |
Understanding Imbalanced Semantic Segmentation Through Neural Collapse | Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia | A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equiangular tight frame at the terminal phase of training for classification. In this paper, we explore the corresponding structures of the last-layer feature centers and classifiers in semantic segmentation. Based on our empirical and theoretical analysis, we point out that semantic segmentation naturally brings contextual correlation and imbalanced distribution among classes, which breaks the equiangular and maximally separated structure of neural collapse for both feature centers and classifiers. However, such a symmetric structure is beneficial to discrimination for the minor classes. To preserve these advantages, we introduce a regularizer on feature centers to encourage the network to learn features closer to the appealing structure in imbalanced semantic segmentation. Experimental results show that our method can bring significant improvements on both 2D and 3D semantic segmentation benchmarks. Moreover, our method ranks first and sets a new record (+6.8% mIoU) on the ScanNet200 test leaderboard. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhong_Understanding_Imbalanced_Semantic_Segmentation_Through_Neural_Collapse_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhong_Understanding_Imbalanced_Semantic_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.01100 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhong_Understanding_Imbalanced_Semantic_Segmentation_Through_Neural_Collapse_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhong_Understanding_Imbalanced_Semantic_Segmentation_Through_Neural_Collapse_CVPR_2023_paper.html | CVPR 2023 | null |
MOVES: Manipulated Objects in Video Enable Segmentation | Richard E. L. Higgins, David F. Fouhey | We present a method that uses manipulation to learn to understand the objects people hold and as well as hand-object contact. We train a system that takes a single RGB image and produces a pixel-embedding that can be used to answer grouping questions (do these two pixels go together) as well as hand-association questions (is this hand holding that pixel). Rather painstakingly annotate segmentation masks, we observe people in realistic video data. We show that pairing epipolar geometry with modern optical flow produces simple and effective pseudo-labels for grouping. Given people segmentations, we can further associate pixels with hands to understand contact. Our system achieves competitive results on hand and hand-held object tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Higgins_MOVES_Manipulated_Objects_in_Video_Enable_Segmentation_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Higgins_MOVES_Manipulated_Objects_in_Video_Enable_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Higgins_MOVES_Manipulated_Objects_in_Video_Enable_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Generating Holistic 3D Human Motion From Speech | Hongwei Yi, Hualin Liang, Yifei Liu, Qiong Cao, Yandong Wen, Timo Bolkart, Dacheng Tao, Michael J. Black | This work addresses the problem of generating 3D holistic body motions from human speech. Given a speech recording, we synthesize sequences of 3D body poses, hand gestures, and facial expressions that are realistic and diverse. To achieve this, we first build a high-quality dataset of 3D holistic body meshes with synchronous speech. We then define a novel speech-to-motion generation framework in which the face, body, and hands are modeled separately. The separated modeling stems from the fact that face articulation strongly correlates with human speech, while body poses and hand gestures are less correlated. Specifically, we employ an autoencoder for face motions, and a compositional vector-quantized variational autoencoder (VQ-VAE) for the body and hand motions. The compositional VQ-VAE is key to generating diverse results. Additionally, we propose a cross conditional autoregressive model that generates body poses and hand gestures, leading to coherent and realistic motions. Extensive experiments and user studies demonstrate that our proposed approach achieves state-of-the-art performance both qualitatively and quantitatively. Our novel dataset and code will be released for research purposes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yi_Generating_Holistic_3D_Human_Motion_From_Speech_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yi_Generating_Holistic_3D_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2212.04420 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yi_Generating_Holistic_3D_Human_Motion_From_Speech_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yi_Generating_Holistic_3D_Human_Motion_From_Speech_CVPR_2023_paper.html | CVPR 2023 | null |
NeuDA: Neural Deformable Anchor for High-Fidelity Implicit Surface Reconstruction | Bowen Cai, Jinchi Huang, Rongfei Jia, Chengfei Lv, Huan Fu | This paper studies implicit surface reconstruction leveraging differentiable ray casting. Previous works such as IDR and NeuS overlook the spatial context in 3D space when predicting and rendering the surface, thereby may fail to capture sharp local topologies such as small holes and structures. To mitigate the limitation, we propose a flexible neural implicit representation leveraging hierarchical voxel grids, namely Neural Deformable Anchor (NeuDA), for high-fidelity surface reconstruction. NeuDA maintains the hierarchical anchor grids where each vertex stores a 3d position (or anchor) instead of the direct embedding (or feature). We optimize the anchor grids such that different local geometry structures can be adaptively encoded. Besides, we dig into the frequency encoding strategies and introduce a simple hierarchical positional encoding method for the hierarchical anchor structure to flexibly exploited the properties of high-frequency and low-frequency geometry and appearance. Experiments on both the DTU and BlendedMVS datasets demonstrate that NeuDA can produce promising mesh surfaces. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cai_NeuDA_Neural_Deformable_Anchor_for_High-Fidelity_Implicit_Surface_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cai_NeuDA_Neural_Deformable_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.02375 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cai_NeuDA_Neural_Deformable_Anchor_for_High-Fidelity_Implicit_Surface_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cai_NeuDA_Neural_Deformable_Anchor_for_High-Fidelity_Implicit_Surface_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
HOICLIP: Efficient Knowledge Transfer for HOI Detection With Vision-Language Models | Shan Ning, Longtian Qiu, Yongfei Liu, Xuming He | Human-Object Interaction (HOI) detection aims to localize human-object pairs and recognize their interactions. Recently, Contrastive Language-Image Pre-training (CLIP) has shown great potential in providing interaction prior for HOI detectors via knowledge distillation. However, such approaches often rely on large-scale training data and suffer from inferior performance under few/zero-shot scenarios. In this paper, we propose a novel HOI detection framework that efficiently extracts prior knowledge from CLIP and achieves better generalization. In detail, we first introduce a novel interaction decoder to extract informative regions in the visual feature map of CLIP via a cross-attention mechanism, which is then fused with the detection backbone by a knowledge integration block for more accurate human-object pair detection. In addition, prior knowledge in CLIP text encoder is leveraged to generate a classifier by embedding HOI descriptions. To distinguish fine-grained interactions, we build a verb classifier from training data via visual semantic arithmetic and a lightweight verb representation adapter. Furthermore, we propose a training-free enhancement to exploit global HOI predictions from CLIP. Extensive experiments demonstrate that our method outperforms the state of the art by a large margin on various settings, e.g. +4.04 mAP on HICO-Det. The source code is available in https://github.com/Artanic30/HOICLIP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ning_HOICLIP_Efficient_Knowledge_Transfer_for_HOI_Detection_With_Vision-Language_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ning_HOICLIP_Efficient_Knowledge_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15786 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ning_HOICLIP_Efficient_Knowledge_Transfer_for_HOI_Detection_With_Vision-Language_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ning_HOICLIP_Efficient_Knowledge_Transfer_for_HOI_Detection_With_Vision-Language_Models_CVPR_2023_paper.html | CVPR 2023 | null |
ShadowNeuS: Neural SDF Reconstruction by Shadow Ray Supervision | Jingwang Ling, Zhibo Wang, Feng Xu | By supervising camera rays between a scene and multi-view image planes, NeRF reconstructs a neural scene representation for the task of novel view synthesis. On the other hand, shadow rays between the light source and the scene have yet to be considered. Therefore, we propose a novel shadow ray supervision scheme that optimizes both the samples along the ray and the ray location. By supervising shadow rays, we successfully reconstruct a neural SDF of the scene from single-view images under multiple lighting conditions. Given single-view binary shadows, we train a neural network to reconstruct a complete scene not limited by the camera's line of sight. By further modeling the correlation between the image colors and the shadow rays, our technique can also be effectively extended to RGB inputs. We compare our method with previous works on challenging tasks of shape reconstruction from single-view binary shadow or RGB images and observe significant improvements. The code and data are available at https://github.com/gerwang/ShadowNeuS. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ling_ShadowNeuS_Neural_SDF_Reconstruction_by_Shadow_Ray_Supervision_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ling_ShadowNeuS_Neural_SDF_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2211.14086 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ling_ShadowNeuS_Neural_SDF_Reconstruction_by_Shadow_Ray_Supervision_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ling_ShadowNeuS_Neural_SDF_Reconstruction_by_Shadow_Ray_Supervision_CVPR_2023_paper.html | CVPR 2023 | null |
Generalized UAV Object Detection via Frequency Domain Disentanglement | Kunyu Wang, Xueyang Fu, Yukun Huang, Chengzhi Cao, Gege Shi, Zheng-Jun Zha | When deploying the Unmanned Aerial Vehicles object detection (UAV-OD) network to complex and unseen real-world scenarios, the generalization ability is usually reduced due to the domain shift. To address this issue, this paper proposes a novel frequency domain disentanglement method to improve the UAV-OD generalization. Specifically, we first verified that the spectrum of different bands in the image has different effects to the UAV-OD generalization. Based on this conclusion, we design two learnable filters to extract domain-invariant spectrum and domain-specific spectrum, respectively. The former can be used to train the UAV-OD network and improve its capacity for generalization. In addition, we design a new instance-level contrastive loss to guide the network training. This loss enables the network to concentrate on extracting domain-invariant spectrum and domain-specific spectrum, so as to achieve better disentangling results. Experimental results on three unseen target domains demonstrate that our method has better generalization ability than both the baseline method and state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Generalized_UAV_Object_Detection_via_Frequency_Domain_Disentanglement_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Generalized_UAV_Object_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Generalized_UAV_Object_Detection_via_Frequency_Domain_Disentanglement_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Generalized_UAV_Object_Detection_via_Frequency_Domain_Disentanglement_CVPR_2023_paper.html | CVPR 2023 | null |
Boosting Weakly-Supervised Temporal Action Localization With Text Information | Guozhang Li, De Cheng, Xinpeng Ding, Nannan Wang, Xiaoyu Wang, Xinbo Gao | Due to the lack of temporal annotation, current Weakly-supervised Temporal Action Localization (WTAL) methods are generally stuck into over-complete or incomplete localization. In this paper, we aim to leverage the text information to boost WTAL from two aspects, i.e., (a) the discriminative objective to enlarge the inter-class difference, thus reducing the over-complete; (b) the generative objective to enhance the intra-class integrity, thus finding more complete temporal boundaries. For the discriminative objective, we propose a Text-Segment Mining (TSM) mechanism, which constructs a text description based on the action class label, and regards the text as the query to mine all class-related segments. Without the temporal annotation of actions, TSM compares the text query with the entire videos across the dataset to mine the best matching segments while ignoring irrelevant ones. Due to the shared sub-actions in different categories of videos, merely applying TSM is too strict to neglect the semantic-related segments, which results in incomplete localization. We further introduce a generative objective named Video-text Language Completion (VLC), which focuses on all semantic-related segments from videos to complete the text sentence. We achieve the state-of-the-art performance on THUMOS14 and ActivityNet1.3. Surprisingly, we also find our proposed method can be seamlessly applied to existing methods, and improve their performances with a clear margin. The code is available at https://github.com/lgzlIlIlI/Boosting-WTAL. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Boosting_Weakly-Supervised_Temporal_Action_Localization_With_Text_Information_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Boosting_Weakly-Supervised_Temporal_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.00607 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Boosting_Weakly-Supervised_Temporal_Action_Localization_With_Text_Information_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Boosting_Weakly-Supervised_Temporal_Action_Localization_With_Text_Information_CVPR_2023_paper.html | CVPR 2023 | null |
DINER: Disorder-Invariant Implicit Neural Representation | Shaowen Xie, Hao Zhu, Zhen Liu, Qi Zhang, You Zhou, Xun Cao, Zhan Ma | Implicit neural representation (INR) characterizes the attributes of a signal as a function of corresponding coordinates which emerges as a sharp weapon for solving inverse problems. However, the capacity of INR is limited by the spectral bias in the network training. In this paper, we find that such a frequency-related problem could be largely solved by re-arranging the coordinates of the input signal, for which we propose the disorder-invariant implicit neural representation (DINER) by augmenting a hash-table to a traditional INR backbone. Given discrete signals sharing the same histogram of attributes and different arrangement orders, the hash-table could project the coordinates into the same distribution for which the mapped signal can be better modeled using the subsequent INR network, leading to significantly alleviated spectral bias. Experiments not only reveal the generalization of the DINER for different INR backbones (MLP vs. SIREN) and various tasks (image/video representation, phase retrieval, and refractive index recovery) but also show the superiority over the state-of-the-art algorithms both in quality and speed. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_DINER_Disorder-Invariant_Implicit_Neural_Representation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xie_DINER_Disorder-Invariant_Implicit_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2211.07871 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_DINER_Disorder-Invariant_Implicit_Neural_Representation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_DINER_Disorder-Invariant_Implicit_Neural_Representation_CVPR_2023_paper.html | CVPR 2023 | null |
A Light Touch Approach to Teaching Transformers Multi-View Geometry | Yash Bhalgat, João F. Henriques, Andrew Zisserman | Transformers are powerful visual learners, in large part due to their conspicuous lack of manually-specified priors. This flexibility can be problematic in tasks that involve multiple-view geometry, due to the near-infinite possible variations in 3D shapes and viewpoints (requiring flexibility), and the precise nature of projective geometry (obeying rigid laws). To resolve this conundrum, we propose a "light touch" approach, guiding visual Transformers to learn multiple-view geometry but allowing them to break free when needed. We achieve this by using epipolar lines to guide the Transformer's cross-attention maps, penalizing attention values outside the epipolar lines and encouraging higher attention along these lines since they contain geometrically plausible matches. Unlike previous methods, our proposal does not require any camera pose information at test-time. We focus on pose-invariant object instance retrieval, where standard Transformer networks struggle, due to the large differences in viewpoint between query and retrieved images. Experimentally, our method outperforms state-of-the-art approaches at object retrieval, without needing pose information at test-time. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bhalgat_A_Light_Touch_Approach_to_Teaching_Transformers_Multi-View_Geometry_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bhalgat_A_Light_Touch_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.15107 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bhalgat_A_Light_Touch_Approach_to_Teaching_Transformers_Multi-View_Geometry_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bhalgat_A_Light_Touch_Approach_to_Teaching_Transformers_Multi-View_Geometry_CVPR_2023_paper.html | CVPR 2023 | null |
Trade-Off Between Robustness and Accuracy of Vision Transformers | Yanxi Li, Chang Xu | Although deep neural networks (DNNs) have shown great successes in computer vision tasks, they are vulnerable to perturbations on inputs, and there exists a trade-off between the natural accuracy and robustness to such perturbations, which is mainly caused by the existence of robust non-predictive features and non-robust predictive features. Recent empirical analyses find Vision Transformers (ViTs) are inherently robust to various kinds of perturbations, but the aforementioned trade-off still exists for them. In this work, we propose Trade-off between Robustness and Accuracy of Vision Transformers (TORA-ViTs), which aims to efficiently transfer ViT models pretrained on natural tasks for both accuracy and robustness. TORA-ViTs consist of two major components, including a pair of accuracy and robustness adapters to extract predictive and robust features, respectively, and a gated fusion module to adjust the trade-off. The gated fusion module takes outputs of a pretrained ViT block as queries and outputs of our adapters as keys and values, and tokens from different adapters at different spatial locations are compared with each other to generate attention scores for a balanced mixing of predictive and robust features. Experiments on ImageNet with various robust benchmarks show that our TORA-ViTs can efficiently improve the robustness of naturally pretrained ViTs while maintaining competitive natural accuracy. Our most balanced setting (TORA-ViTs with lambda = 0.5) can maintain 83.7% accuracy on clean ImageNet and reach 54.7% and 38.0% accuracy under FGSM and PGD white-box attacks, respectively. In terms of various ImageNet variants, it can reach 39.2% and 56.3% accuracy on ImageNet-A and ImageNet-R and reach 34.4% mCE on ImageNet-C. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Trade-Off_Between_Robustness_and_Accuracy_of_Vision_Transformers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Trade-Off_Between_Robustness_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Trade-Off_Between_Robustness_and_Accuracy_of_Vision_Transformers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Trade-Off_Between_Robustness_and_Accuracy_of_Vision_Transformers_CVPR_2023_paper.html | CVPR 2023 | null |
Focused and Collaborative Feedback Integration for Interactive Image Segmentation | Qiaoqiao Wei, Hui Zhang, Jun-Hai Yong | Interactive image segmentation aims at obtaining a segmentation mask for an image using simple user annotations. During each round of interaction, the segmentation result from the previous round serves as feedback to guide the user's annotation and provides dense prior information for the segmentation model, effectively acting as a bridge between interactions. Existing methods overlook the importance of feedback or simply concatenate it with the original input, leading to underutilization of feedback and an increase in the number of required annotations. To address this, we propose an approach called Focused and Collaborative Feedback Integration (FCFI) to fully exploit the feedback for click-based interactive image segmentation. FCFI first focuses on a local area around the new click and corrects the feedback based on the similarities of high-level features. It then alternately and collaboratively updates the feedback and deep features to integrate the feedback into the features. The efficacy and efficiency of FCFI were validated on four benchmarks, namely GrabCut, Berkeley, SBD, and DAVIS. Experimental results show that FCFI achieved new state-of-the-art performance with less computational overhead than previous methods. The source code is available at https://github.com/veizgyauzgyauz/FCFI. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wei_Focused_and_Collaborative_Feedback_Integration_for_Interactive_Image_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wei_Focused_and_Collaborative_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11880 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_Focused_and_Collaborative_Feedback_Integration_for_Interactive_Image_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_Focused_and_Collaborative_Feedback_Integration_for_Interactive_Image_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Class Prototypes Based Contrastive Learning for Classifying Multi-Label and Fine-Grained Educational Videos | Rohit Gupta, Anirban Roy, Claire Christensen, Sujeong Kim, Sarah Gerard, Madeline Cincebeaux, Ajay Divakaran, Todd Grindal, Mubarak Shah | The recent growth in the consumption of online media by children during early childhood necessitates data-driven tools enabling educators to filter out appropriate educational content for young learners. This paper presents an approach for detecting educational content in online videos. We focus on two widely used educational content classes: literacy and math. For each class, we choose prominent codes (sub-classes) based on the Common Core Standards. For example, literacy codes include 'letter names', 'letter sounds', and math codes include 'counting', 'sorting'. We pose this as a fine-grained multilabel classification problem as videos can contain multiple types of educational content and the content classes can get visually similar (e.g., 'letter names' vs 'letter sounds'). We propose a novel class prototypes based supervised contrastive learning approach that can handle fine-grained samples associated with multiple labels. We learn a class prototype for each class and a loss function is employed to minimize the distances between a class prototype and the samples from the class. Similarly, distances between a class prototype and the samples from other classes are maximized. As the alignment between visual and audio cues are crucial for effective comprehension, we consider a multimodal transformer network to capture the interaction between visual and audio cues in videos while learning the embedding for videos. For evaluation, we present a dataset, APPROVE, employing educational videos from YouTube labeled with fine-grained education classes by education researchers. APPROVE consists of 193 hours of expert-annotated videos with 19 classes. The proposed approach outperforms strong baselines on APPROVE and other benchmarks such as Youtube-8M, and COIN. The dataset is available at https://nusci.csl.sri.com/project/APPROVE. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gupta_Class_Prototypes_Based_Contrastive_Learning_for_Classifying_Multi-Label_and_Fine-Grained_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gupta_Class_Prototypes_Based_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gupta_Class_Prototypes_Based_Contrastive_Learning_for_Classifying_Multi-Label_and_Fine-Grained_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gupta_Class_Prototypes_Based_Contrastive_Learning_for_Classifying_Multi-Label_and_Fine-Grained_CVPR_2023_paper.html | CVPR 2023 | null |
Deep Graph-Based Spatial Consistency for Robust Non-Rigid Point Cloud Registration | Zheng Qin, Hao Yu, Changjian Wang, Yuxing Peng, Kai Xu | We study the problem of outlier correspondence pruning for non-rigid point cloud registration. In rigid registration, spatial consistency has been a commonly used criterion to discriminate outliers from inliers. It measures the compatibility of two correspondences by the discrepancy between the respective distances in two point clouds. However, spatial consistency no longer holds in non-rigid cases and outlier rejection for non-rigid registration has not been well studied. In this work, we propose Graph-based Spatial Consistency Network (GraphSCNet) to filter outliers for non-rigid registration. Our method is based on the fact that non-rigid deformations are usually locally rigid, or local shape preserving. We first design a local spatial consistency measure over the deformation graph of the point cloud, which evaluates the spatial compatibility only between the correspondences in the vicinity of a graph node. An attention-based non-rigid correspondence embedding module is then devised to learn a robust representation of non-rigid correspondences from local spatial consistency. Despite its simplicity, GraphSCNet effectively improves the quality of the putative correspondences and attains state-of-the-art performance on three challenging benchmarks. Our code and models are available at https://github.com/qinzheng93/GraphSCNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Qin_Deep_Graph-Based_Spatial_Consistency_for_Robust_Non-Rigid_Point_Cloud_Registration_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qin_Deep_Graph-Based_Spatial_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.09950 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qin_Deep_Graph-Based_Spatial_Consistency_for_Robust_Non-Rigid_Point_Cloud_Registration_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qin_Deep_Graph-Based_Spatial_Consistency_for_Robust_Non-Rigid_Point_Cloud_Registration_CVPR_2023_paper.html | CVPR 2023 | null |
Source-Free Adaptive Gaze Estimation by Uncertainty Reduction | Xin Cai, Jiabei Zeng, Shiguang Shan, Xilin Chen | Gaze estimation across domains has been explored recently because the training data are usually collected under controlled conditions while the trained gaze estimators are used in real and diverse environments. However, due to privacy and efficiency concerns, simultaneous access to annotated source data and to-be-predicted target data can be challenging. In light of this, we present an unsupervised source-free domain adaptation approach for gaze estimation, which adapts a source-trained gaze estimator to unlabeled target domains without source data. We propose the Uncertainty Reduction Gaze Adaptation (UnReGA) framework, which achieves adaptation by reducing both sample and model uncertainty. Sample uncertainty is mitigated by enhancing image quality and making them gaze-estimation-friendly, whereas model uncertainty is reduced by minimizing prediction variance on the same inputs. Extensive experiments are conducted on six cross-domain tasks, demonstrating the effectiveness of UnReGA and its components. Results show that UnReGA outperforms other state-of-the-art cross-domain gaze estimation methods under both protocols, with and without source data | https://openaccess.thecvf.com/content/CVPR2023/papers/Cai_Source-Free_Adaptive_Gaze_Estimation_by_Uncertainty_Reduction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cai_Source-Free_Adaptive_Gaze_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cai_Source-Free_Adaptive_Gaze_Estimation_by_Uncertainty_Reduction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cai_Source-Free_Adaptive_Gaze_Estimation_by_Uncertainty_Reduction_CVPR_2023_paper.html | CVPR 2023 | null |
Slide-Transformer: Hierarchical Vision Transformer With Local Self-Attention | Xuran Pan, Tianzhu Ye, Zhuofan Xia, Shiji Song, Gao Huang | Self-attention mechanism has been a key factor in the recent progress of Vision Transformer (ViT), which enables adaptive feature extraction from global contexts. However, existing self-attention methods either adopt sparse global attention or window attention to reduce the computation complexity, which may compromise the local feature learning or subject to some handcrafted designs. In contrast, local attention, which restricts the receptive field of each query to its own neighboring pixels, enjoys the benefits of both convolution and self-attention, namely local inductive bias and dynamic feature selection. Nevertheless, current local attention modules either use inefficient Im2Col function or rely on specific CUDA kernels that are hard to generalize to devices without CUDA support. In this paper, we propose a novel local attention module, Slide Attention, which leverages common convolution operations to achieve high efficiency, flexibility and generalizability. Specifically, we first re-interpret the column-based Im2Col function from a new row-based perspective and use Depthwise Convolution as an efficient substitution. On this basis, we propose a deformed shifting module based on the re-parameterization technique, which further relaxes the fixed key/value positions to deformed features in the local region. In this way, our module realizes the local attention paradigm in both efficient and flexible manner. Extensive experiments show that our slide attention module is applicable to a variety of advanced Vision Transformer models and compatible with various hardware devices, and achieves consistently improved performances on comprehensive benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Pan_Slide-Transformer_Hierarchical_Vision_Transformer_With_Local_Self-Attention_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Pan_Slide-Transformer_Hierarchical_Vision_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pan_Slide-Transformer_Hierarchical_Vision_Transformer_With_Local_Self-Attention_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pan_Slide-Transformer_Hierarchical_Vision_Transformer_With_Local_Self-Attention_CVPR_2023_paper.html | CVPR 2023 | null |
NeRF-Supervised Deep Stereo | Fabio Tosi, Alessio Tonioni, Daniele De Gregorio, Matteo Poggi | We introduce a novel framework for training deep stereo networks effortlessly and without any ground-truth. By leveraging state-of-the-art neural rendering solutions, we generate stereo training data from image sequences collected with a single handheld camera. On top of them, a NeRF-supervised training procedure is carried out, from which we exploit rendered stereo triplets to compensate for occlusions and depth maps as proxy labels. This results in stereo networks capable of predicting sharp and detailed disparity maps. Experimental results show that models trained under this regime yield a 30-40% improvement over existing self-supervised methods on the challenging Middlebury dataset, filling the gap to supervised models and, most times, outperforming them at zero-shot generalization. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tosi_NeRF-Supervised_Deep_Stereo_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tosi_NeRF-Supervised_Deep_Stereo_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.17603 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tosi_NeRF-Supervised_Deep_Stereo_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tosi_NeRF-Supervised_Deep_Stereo_CVPR_2023_paper.html | CVPR 2023 | null |
Decoupled Multimodal Distilling for Emotion Recognition | Yong Li, Yuanzhi Wang, Zhen Cui | Human multimodal emotion recognition (MER) aims to perceive human emotions via language, visual and acoustic modalities. Despite the impressive performance of previous MER approaches, the inherent multimodal heterogeneities still haunt and the contribution of different modalities varies significantly. In this work, we mitigate this issue by proposing a decoupled multimodal distillation (DMD) approach that facilitates flexible and adaptive crossmodal knowledge distillation, aiming to enhance the discriminative features of each modality. Specially, the representation of each modality is decoupled into two parts, i.e., modality-irrelevant/-exclusive spaces, in a self-regression manner. DMD utilizes a graph distillation unit (GD-Unit) for each decoupled part so that each GD can be performed in a more specialized and effective manner. A GD-Unit consists of a dynamic graph where each vertice represents a modality and each edge indicates a dynamic knowledge distillation. Such GD paradigm provides a flexible knowledge transfer manner where the distillation weights can be automatically learned, thus enabling diverse crossmodal knowledge transfer patterns. Experimental results show DMD consistently obtains superior performance than state-of-the-art MER methods. Visualization results show the graph edges in DMD exhibit meaningful distributional patterns w.r.t. the modality-irrelevant/-exclusive feature spaces. Codes are released at https://github.com/mdswyz/DMD. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Decoupled_Multimodal_Distilling_for_Emotion_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Decoupled_Multimodal_Distilling_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13802 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Decoupled_Multimodal_Distilling_for_Emotion_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Decoupled_Multimodal_Distilling_for_Emotion_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
SuperDisco: Super-Class Discovery Improves Visual Recognition for the Long-Tail | Yingjun Du, Jiayi Shen, Xiantong Zhen, Cees G. M. Snoek | Modern image classifiers perform well on populated classes while degrading considerably on tail classes with only a few instances. Humans, by contrast, effortlessly handle the long-tailed recognition challenge, since they can learn the tail representation based on different levels of semantic abstraction, making the learned tail features more discriminative. This phenomenon motivated us to propose SuperDisco, an algorithm that discovers super-class representations for long-tailed recognition using a graph model. We learn to construct the super-class graph to guide the representation learning to deal with long-tailed distributions. Through message passing on the super-class graph, image representations are rectified and refined by attending to the most relevant entities based on the semantic similarity among their super-classes. Moreover, we propose to meta-learn the super-class graph under the supervision of a prototype graph constructed from a small amount of imbalanced data. By doing so, we obtain a more robust super-class graph that further improves the long-tailed recognition performance. The consistent state-of-the-art experiments on the long-tailed CIFAR-100, ImageNet, Places, and iNaturalist demonstrate the benefit of the discovered super-class graph for dealing with long-tailed distributions. | https://openaccess.thecvf.com/content/CVPR2023/papers/Du_SuperDisco_Super-Class_Discovery_Improves_Visual_Recognition_for_the_Long-Tail_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Du_SuperDisco_Super-Class_Discovery_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00101 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Du_SuperDisco_Super-Class_Discovery_Improves_Visual_Recognition_for_the_Long-Tail_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Du_SuperDisco_Super-Class_Discovery_Improves_Visual_Recognition_for_the_Long-Tail_CVPR_2023_paper.html | CVPR 2023 | null |
DualRefine: Self-Supervised Depth and Pose Estimation Through Iterative Epipolar Sampling and Refinement Toward Equilibrium | Antyanta Bangunharcana, Ahmed Magd, Kyung-Soo Kim | Self-supervised multi-frame depth estimation achieves high accuracy by computing matching costs of pixel correspondences between adjacent frames, injecting geometric information into the network. These pixel-correspondence candidates are computed based on the relative pose estimates between the frames. Accurate pose predictions are essential for precise matching cost computation as they influence the epipolar geometry. Furthermore, improved depth estimates can, in turn, be used to align pose estimates. Inspired by traditional structure-from-motion (SfM) principles, we propose the DualRefine model, which tightly couples depth and pose estimation through a feedback loop. Our novel update pipeline uses a deep equilibrium model framework to iteratively refine depth estimates and a hidden state of feature maps by computing local matching costs based on epipolar geometry. Importantly, we used the refined depth estimates and feature maps to compute pose updates at each step. This update in the pose estimates slowly alters the epipolar geometry during the refinement process. Experimental results on the KITTI dataset demonstrate competitive depth prediction and odometry prediction performance surpassing published self-supervised baselines. The code is available at https://github.com/antabangun/DualRefine. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bangunharcana_DualRefine_Self-Supervised_Depth_and_Pose_Estimation_Through_Iterative_Epipolar_Sampling_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bangunharcana_DualRefine_Self-Supervised_Depth_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.03560 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bangunharcana_DualRefine_Self-Supervised_Depth_and_Pose_Estimation_Through_Iterative_Epipolar_Sampling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bangunharcana_DualRefine_Self-Supervised_Depth_and_Pose_Estimation_Through_Iterative_Epipolar_Sampling_CVPR_2023_paper.html | CVPR 2023 | null |
Improving Generalization of Meta-Learning With Inverted Regularization at Inner-Level | Lianzhe Wang, Shiji Zhou, Shanghang Zhang, Xu Chu, Heng Chang, Wenwu Zhu | Despite the broad interest in meta-learning, the generalization problem remains one of the significant challenges in this field. Existing works focus on meta-generalization to unseen tasks at the meta-level by regularizing the meta-loss, while ignoring that adapted models may not generalize to the task domains at the adaptation level. In this paper, we propose a new regularization mechanism for meta-learning -- Minimax-Meta Regularization, which employs inverted regularization at the inner loop and ordinary regularization at the outer loop during training. In particular, the inner inverted regularization makes the adapted model more difficult to generalize to task domains; thus, optimizing the outer-loop loss forces the meta-model to learn meta-knowledge with better generalization. Theoretically, we prove that inverted regularization improves the meta-testing performance by reducing generalization errors. We conduct extensive experiments on the representative scenarios, and the results show that our method consistently improves the performance of meta-learning algorithms. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Improving_Generalization_of_Meta-Learning_With_Inverted_Regularization_at_Inner-Level_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Improving_Generalization_of_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Improving_Generalization_of_Meta-Learning_With_Inverted_Regularization_at_Inner-Level_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Improving_Generalization_of_Meta-Learning_With_Inverted_Regularization_at_Inner-Level_CVPR_2023_paper.html | CVPR 2023 | null |
SmallCap: Lightweight Image Captioning Prompted With Retrieval Augmentation | Rita Ramos, Bruno Martins, Desmond Elliott, Yova Kementchedjhieva | Recent advances in image captioning have focused on scaling the data and model size, substantially increasing the cost of pre-training and finetuning. As an alternative to large models, we present SmallCap, which generates a caption conditioned on an input image and related captions retrieved from a datastore. Our model is lightweight and fast to train as the only learned parameters are in newly introduced cross-attention layers between a pre-trained CLIP encoder and GPT-2 decoder. SmallCap can transfer to new domains without additional finetuning and can exploit large-scale data in a training-free fashion since the contents of the datastore can be readily replaced. Our experiments show that SmallCap, trained only on COCO, has competitive performance on this benchmark, and also transfers to other domains without retraining, solely through retrieval from target-domain data. Further improvement is achieved through the training-free exploitation of diverse human-labeled and web data, which proves effective for a range of domains, including the nocaps benchmark, designed to test generalization to unseen visual concepts. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ramos_SmallCap_Lightweight_Image_Captioning_Prompted_With_Retrieval_Augmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ramos_SmallCap_Lightweight_Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2209.15323 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ramos_SmallCap_Lightweight_Image_Captioning_Prompted_With_Retrieval_Augmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ramos_SmallCap_Lightweight_Image_Captioning_Prompted_With_Retrieval_Augmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Unifying Layout Generation With a Decoupled Diffusion Model | Mude Hui, Zhizheng Zhang, Xiaoyi Zhang, Wenxuan Xie, Yuwang Wang, Yan Lu | Layout generation aims to synthesize realistic graphic scenes consisting of elements with different attributes including category, size, position, and between-element relation. It is a crucial task for reducing the burden on heavy-duty graphic design works for formatted scenes, e.g., publications, documents, and user interfaces (UIs). Diverse application scenarios impose a big challenge in unifying various layout generation subtasks, including conditional and unconditional generation. In this paper, we propose a Layout Diffusion Generative Model (LDGM) to achieve such unification with a single decoupled diffusion model. LDGM views a layout of arbitrary missing or coarse element attributes as an intermediate diffusion status from a completed layout. Since different attributes have their individual semantics and characteristics, we propose to decouple the diffusion processes for them to improve the diversity of training samples and learn the reverse process jointly to exploit global-scope contexts for facilitating generation. As a result, our LDGM can generate layouts either from scratch or conditional on arbitrary available attributes. Extensive qualitative and quantitative experiments demonstrate our proposed LDGM outperforms existing layout generation models in both functionality and performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hui_Unifying_Layout_Generation_With_a_Decoupled_Diffusion_Model_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hui_Unifying_Layout_Generation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.05049 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hui_Unifying_Layout_Generation_With_a_Decoupled_Diffusion_Model_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hui_Unifying_Layout_Generation_With_a_Decoupled_Diffusion_Model_CVPR_2023_paper.html | CVPR 2023 | null |
Im2Hands: Learning Attentive Implicit Representation of Interacting Two-Hand Shapes | Jihyun Lee, Minhyuk Sung, Honggyu Choi, Tae-Kyun Kim | We present Implicit Two Hands (Im2Hands), the first neural implicit representation of two interacting hands. Unlike existing methods on two-hand reconstruction that rely on a parametric hand model and/or low-resolution meshes, Im2Hands can produce fine-grained geometry of two hands with high hand-to-hand and hand-to-image coherency. To handle the shape complexity and interaction context between two hands, Im2Hands models the occupancy volume of two hands -- conditioned on an RGB image and coarse 3D keypoints -- by two novel attention-based modules responsible for (1) initial occupancy estimation and (2) context-aware occupancy refinement, respectively. Im2Hands first learns per-hand neural articulated occupancy in the canonical space designed for each hand using query-image attention. It then refines the initial two-hand occupancy in the posed space to enhance the coherency between the two hand shapes using query-anchor attention. In addition, we introduce an optional keypoint refinement module to enable robust two-hand shape estimation from predicted hand keypoints in a single-image reconstruction scenario. We experimentally demonstrate the effectiveness of Im2Hands on two-hand reconstruction in comparison to related methods, where ours achieves state-of-the-art results. Our code is publicly available at https://github.com/jyunlee/Im2Hands. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lee_Im2Hands_Learning_Attentive_Implicit_Representation_of_Interacting_Two-Hand_Shapes_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lee_Im2Hands_Learning_Attentive_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.14348 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Im2Hands_Learning_Attentive_Implicit_Representation_of_Interacting_Two-Hand_Shapes_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Im2Hands_Learning_Attentive_Implicit_Representation_of_Interacting_Two-Hand_Shapes_CVPR_2023_paper.html | CVPR 2023 | null |
Long-Term Visual Localization With Mobile Sensors | Shen Yan, Yu Liu, Long Wang, Zehong Shen, Zhen Peng, Haomin Liu, Maojun Zhang, Guofeng Zhang, Xiaowei Zhou | Despite the remarkable advances in image matching and pose estimation, image-based localization of a camera in a temporally-varying outdoor environment is still a challenging problem due to huge appearance disparity between query and reference images caused by illumination, seasonal and structural changes. In this work, we propose to leverage additional sensors on a mobile phone, mainly GPS, compass, and gravity sensor, to solve this challenging problem. We show that these mobile sensors provide decent initial poses and effective constraints to reduce the searching space in image matching and final pose estimation. With the initial pose, we are also able to devise a direct 2D-3D matching network to efficiently establish 2D-3D correspondences instead of tedious 2D-2D matching in existing systems. As no public dataset exists for the studied problem, we collect a new dataset that provides a variety of mobile sensor data and significant scene appearance variations, and develop a system to acquire ground-truth poses for query images. We benchmark our method as well as several state-of-the-art baselines and demonstrate the effectiveness of the proposed approach. Our code and dataset are available on the project page: https://zju3dv.github.io/sensloc/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yan_Long-Term_Visual_Localization_With_Mobile_Sensors_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.07691 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_Long-Term_Visual_Localization_With_Mobile_Sensors_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_Long-Term_Visual_Localization_With_Mobile_Sensors_CVPR_2023_paper.html | CVPR 2023 | null |
Data-Efficient Large Scale Place Recognition With Graded Similarity Supervision | María Leyva-Vallina, Nicola Strisciuglio, Nicolai Petkov | Visual place recognition (VPR) is a fundamental task of computer vision for visual localization. Existing methods are trained using image pairs that either depict the same place or not. Such a binary indication does not consider continuous relations of similarity between images of the same place taken from different positions, determined by the continuous nature of camera pose. The binary similarity induces a noisy supervision signal into the training of VPR methods, which stall in local minima and require expensive hard mining algorithms to guarantee convergence. Motivated by the fact that two images of the same place only partially share visual cues due to camera pose differences, we deploy an automatic re-annotation strategy to re-label VPR datasets. We compute graded similarity labels for image pairs based on available localization metadata. Furthermore, we propose a new Generalized Contrastive Loss (GCL) that uses graded similarity labels for training contrastive networks. We demonstrate that the use of the new labels and GCL allow to dispense from hard-pair mining, and to train image descriptors that perform better in VPR by nearest neighbor search, obtaining superior or comparable results than methods that require expensive hard-pair mining and re-ranking techniques. | https://openaccess.thecvf.com/content/CVPR2023/papers/Leyva-Vallina_Data-Efficient_Large_Scale_Place_Recognition_With_Graded_Similarity_Supervision_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Leyva-Vallina_Data-Efficient_Large_Scale_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11739 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Leyva-Vallina_Data-Efficient_Large_Scale_Place_Recognition_With_Graded_Similarity_Supervision_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Leyva-Vallina_Data-Efficient_Large_Scale_Place_Recognition_With_Graded_Similarity_Supervision_CVPR_2023_paper.html | CVPR 2023 | null |
Dynamic Neural Network for Multi-Task Learning Searching Across Diverse Network Topologies | Wonhyeok Choi, Sunghoon Im | In this paper, we present a new MTL framework that searches for structures optimized for multiple tasks with diverse graph topologies and shares features among tasks. We design a restricted DAG-based central network with read-in/read-out layers to build topologically diverse task-adaptive structures while limiting search space and time. We search for a single optimized network that serves as multiple task adaptive sub-networks using our three-stage training process. To make the network compact and discretized, we propose a flow-based reduction algorithm and a squeeze loss used in the training process. We evaluate our optimized network on various public MTL datasets and show ours achieves state-of-the-art performance. An extensive ablation study experimentally validates the effectiveness of the sub-module and schemes in our framework. | https://openaccess.thecvf.com/content/CVPR2023/papers/Choi_Dynamic_Neural_Network_for_Multi-Task_Learning_Searching_Across_Diverse_Network_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Choi_Dynamic_Neural_Network_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.06856 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Choi_Dynamic_Neural_Network_for_Multi-Task_Learning_Searching_Across_Diverse_Network_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Choi_Dynamic_Neural_Network_for_Multi-Task_Learning_Searching_Across_Diverse_Network_CVPR_2023_paper.html | CVPR 2023 | null |
Relightable Neural Human Assets From Multi-View Gradient Illuminations | Taotao Zhou, Kai He, Di Wu, Teng Xu, Qixuan Zhang, Kuixiang Shao, Wenzheng Chen, Lan Xu, Jingyi Yu | Human modeling and relighting are two fundamental problems in computer vision and graphics, where high-quality datasets can largely facilitate related research. However, most existing human datasets only provide multi-view human images captured under the same illumination. Although valuable for modeling tasks, they are not readily used in relighting problems. To promote research in both fields, in this paper, we present UltraStage, a new 3D human dataset that contains more than 2,000 high-quality human assets captured under both multi-view and multi-illumination settings. Specifically, for each example, we provide 32 surrounding views illuminated with one white light and two gradient illuminations. In addition to regular multi-view images, gradient illuminations help recover detailed surface normal and spatially-varying material maps, enabling various relighting applications. Inspired by recent advances in neural representation, we further interpret each example into a neural human asset which allows novel view synthesis under arbitrary lighting conditions. We show our neural human assets can achieve extremely high capture performance and are capable of representing fine details such as facial wrinkles and cloth folds. We also validate UltraStage in single image relighting tasks, training neural networks with virtual relighted data from neural assets and demonstrating realistic rendering improvements over prior arts. UltraStage will be publicly available to the community to stimulate significant future developments in various human modeling and rendering tasks. The dataset is available at https://miaoing.github.io/RNHA. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Relightable_Neural_Human_Assets_From_Multi-View_Gradient_Illuminations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_Relightable_Neural_Human_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.07648 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Relightable_Neural_Human_Assets_From_Multi-View_Gradient_Illuminations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Relightable_Neural_Human_Assets_From_Multi-View_Gradient_Illuminations_CVPR_2023_paper.html | CVPR 2023 | null |
Probing Sentiment-Oriented Pre-Training Inspired by Human Sentiment Perception Mechanism | Tinglei Feng, Jiaxuan Liu, Jufeng Yang | Pre-training of deep convolutional neural networks (DCNNs) plays a crucial role in the field of visual sentiment analysis (VSA). Most proposed methods employ the off-the-shelf backbones pre-trained on large-scale object classification datasets (i.e., ImageNet). While it boosts performance for a big margin against initializing model states from random, we argue that DCNNs simply pre-trained on ImageNet may excessively focus on recognizing objects, but failed to provide high-level concepts in terms of sentiment. To address this long-term overlooked problem, we propose a sentiment-oriented pre-training method that is built upon human visual sentiment perception (VSP) mechanism. Specifically, we factorize the process of VSP into three steps, namely stimuli taking, holistic organizing, and high-level perceiving. From imitating each VSP step, a total of three models are separately pre-trained via our devised sentiment-aware tasks that contribute to excavating sentiment-discriminated representations. Moreover, along with our elaborated multi-model amalgamation strategy, the prior knowledge learned from each perception step can be effectively transferred into a single target model, yielding substantial performance gains. Finally, we verify the superiorities of our proposed method over extensive experiments, covering mainstream VSA tasks from single-label learning (SLL), multi-label learning (MLL), to label distribution learning (LDL). Experiment results demonstrate that our proposed method leads to unanimous improvements in these downstream tasks. Our code is released on https://github.com/tinglyfeng/sentiment_pretraining | https://openaccess.thecvf.com/content/CVPR2023/papers/Feng_Probing_Sentiment-Oriented_Pre-Training_Inspired_by_Human_Sentiment_Perception_Mechanism_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Probing_Sentiment-Oriented_Pre-Training_Inspired_by_Human_Sentiment_Perception_Mechanism_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Probing_Sentiment-Oriented_Pre-Training_Inspired_by_Human_Sentiment_Perception_Mechanism_CVPR_2023_paper.html | CVPR 2023 | null |
Imitation Learning As State Matching via Differentiable Physics | Siwei Chen, Xiao Ma, Zhongwen Xu | Existing imitation learning (IL) methods such as inverse reinforcement learning (IRL) usually have a double-loop training process, alternating between learning a reward function and a policy and tend to suffer long training time and high variance. In this work, we identify the benefits of differentiable physics simulators and propose a new IL method, i.e., Imitation Learning via Differentiable Physics (ILD), which gets rid of the double-loop design and achieves significant improvements in final performance, convergence speed, and stability. The proposed ILD incorporates the differentiable physics simulator as a physics prior into its computational graph for policy learning. It unrolls the dynamics by sampling actions from a parameterized policy, simply minimizing the distance between the expert trajectory and the agent trajectory, and back-propagating the gradient into the policy via temporal physics operators. With the physics prior, ILD policies can not only be transferable to unseen environment specifications but also yield higher final performance on a variety of tasks. In addition, ILD naturally forms a single-loop structure, which significantly improves the stability and training speed. To simplify the complex optimization landscape induced by temporal physics operations, ILD dynamically selects the learning objectives for each state during optimization. In our experiments, we show that ILD outperforms state-of-the-art methods in a variety of continuous control tasks with Brax, requiring only one expert demonstration. In addition, ILD can be applied to challenging deformable object manipulation tasks and can be generalized to unseen configurations. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Imitation_Learning_As_State_Matching_via_Differentiable_Physics_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Imitation_Learning_As_State_Matching_via_Differentiable_Physics_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Imitation_Learning_As_State_Matching_via_Differentiable_Physics_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Imitation_Learning_As_State_Matching_via_Differentiable_Physics_CVPR_2023_paper.html | CVPR 2023 | null |
OpenMix: Exploring Outlier Samples for Misclassification Detection | Fei Zhu, Zhen Cheng, Xu-Yao Zhang, Cheng-Lin Liu | Reliable confidence estimation for deep neural classifiers is a challenging yet fundamental requirement in high-stakes applications. Unfortunately, modern deep neural networks are often overconfident for their erroneous predictions. In this work, we exploit the easily available outlier samples, i.e., unlabeled samples coming from non-target classes, for helping detect misclassification errors. Particularly, we find that the well-known Outlier Exposure, which is powerful in detecting out-of-distribution (OOD) samples from unknown classes, does not provide any gain in identifying misclassification errors. Based on these observations, we propose a novel method called OpenMix, which incorporates open-world knowledge by learning to reject uncertain pseudo-samples generated via outlier transformation. OpenMix significantly improves confidence reliability under various scenarios, establishing a strong and unified framework for detecting both misclassified samples from known classes and OOD samples from unknown classes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_OpenMix_Exploring_Outlier_Samples_for_Misclassification_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_OpenMix_Exploring_Outlier_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.17093 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_OpenMix_Exploring_Outlier_Samples_for_Misclassification_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_OpenMix_Exploring_Outlier_Samples_for_Misclassification_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Multivariate, Multi-Frequency and Multimodal: Rethinking Graph Neural Networks for Emotion Recognition in Conversation | Feiyu Chen, Jie Shao, Shuyuan Zhu, Heng Tao Shen | Complex relationships of high arity across modality and context dimensions is a critical challenge in the Emotion Recognition in Conversation (ERC) task. Yet, previous works tend to encode multimodal and contextual relationships in a loosely-coupled manner, which may harm relationship modelling. Recently, Graph Neural Networks (GNN) which show advantages in capturing data relations, offer a new solution for ERC. However, existing GNN-based ERC models fail to address some general limits of GNNs, including assuming pairwise formulation and erasing high-frequency signals, which may be trivial for many applications but crucial for the ERC task. In this paper, we propose a GNN-based model that explores multivariate relationships and captures the varying importance of emotion discrepancy and commonality by valuing multi-frequency signals. We empower GNNs to better capture the inherent relationships among utterances and deliver more sufficient multimodal and contextual modelling. Experimental results show that our proposed method outperforms previous state-of-the-art works on two popular multimodal ERC datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Multivariate_Multi-Frequency_and_Multimodal_Rethinking_Graph_Neural_Networks_for_Emotion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Multivariate_Multi-Frequency_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Multivariate_Multi-Frequency_and_Multimodal_Rethinking_Graph_Neural_Networks_for_Emotion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Multivariate_Multi-Frequency_and_Multimodal_Rethinking_Graph_Neural_Networks_for_Emotion_CVPR_2023_paper.html | CVPR 2023 | null |
Weakly Supervised Class-Agnostic Motion Prediction for Autonomous Driving | Ruibo Li, Hanyu Shi, Ziang Fu, Zhe Wang, Guosheng Lin | Understanding the motion behavior of dynamic environments is vital for autonomous driving, leading to increasing attention in class-agnostic motion prediction in LiDAR point clouds. Outdoor scenes can often be decomposed into mobile foregrounds and static backgrounds, which enables us to associate motion understanding with scene parsing. Based on this observation, we study a novel weakly supervised motion prediction paradigm, where fully or partially (1%, 0.1%) annotated foreground/background binary masks rather than expensive motion annotations are used for supervision. To this end, we propose a two-stage weakly supervised approach, where the segmentation model trained with the incomplete binary masks in Stage1 will facilitate the self-supervised learning of the motion prediction network in Stage2 by estimating possible moving foregrounds in advance. Furthermore, for robust self-supervised motion learning, we design a Consistency-aware Chamfer Distance loss by exploiting multi-frame information and explicitly suppressing potential outliers. Comprehensive experiments show that, with fully or partially binary masks as supervision, our weakly supervised models surpass the self-supervised models by a large margin and perform on par with some supervised ones. This further demonstrates that our approach achieves a good compromise between annotation effort and performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Weakly_Supervised_Class-Agnostic_Motion_Prediction_for_Autonomous_Driving_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Weakly_Supervised_Class-Agnostic_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Weakly_Supervised_Class-Agnostic_Motion_Prediction_for_Autonomous_Driving_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Weakly_Supervised_Class-Agnostic_Motion_Prediction_for_Autonomous_Driving_CVPR_2023_paper.html | CVPR 2023 | null |
TOPLight: Lightweight Neural Networks With Task-Oriented Pretraining for Visible-Infrared Recognition | Hao Yu, Xu Cheng, Wei Peng | Visible-infrared recognition (VI recognition) is a challenging task due to the enormous visual difference across heterogeneous images. Most existing works achieve promising results by transfer learning, such as pretraining on the ImageNet, based on advanced neural architectures like ResNet and ViT. However, such methods ignore the negative influence of the pretrained colour prior knowledge, as well as their heavy computational burden makes them hard to deploy in actual scenarios with limited resources. In this paper, we propose a novel task-oriented pretrained lightweight neural network (TOPLight) for VI recognition. Specifically, the TOPLight method simulates the domain conflict and sample variations with the proposed fake domain loss in the pretraining stage, which guides the network to learn how to handle those difficulties, such that a more general modality-shared feature representation is learned for the heterogeneous images. Moreover, an effective fine-grained dependency reconstruction module (FDR) is developed to discover substantial pattern dependencies shared in two modalities. Extensive experiments on VI person re-identification and VI face recognition datasets demonstrate the superiority of the proposed TOPLight, which significantly outperforms the current state of the arts while demanding fewer computational resources. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_TOPLight_Lightweight_Neural_Networks_With_Task-Oriented_Pretraining_for_Visible-Infrared_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_TOPLight_Lightweight_Neural_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_TOPLight_Lightweight_Neural_Networks_With_Task-Oriented_Pretraining_for_Visible-Infrared_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_TOPLight_Lightweight_Neural_Networks_With_Task-Oriented_Pretraining_for_Visible-Infrared_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
DeFeeNet: Consecutive 3D Human Motion Prediction With Deviation Feedback | Xiaoning Sun, Huaijiang Sun, Bin Li, Dong Wei, Weiqing Li, Jianfeng Lu | Let us rethink the real-world scenarios that require human motion prediction techniques, such as human-robot collaboration. Current works simplify the task of predicting human motions into a one-off process of forecasting a short future sequence (usually no longer than 1 second) based on a historical observed one. However, such simplification may fail to meet practical needs due to the neglect of the fact that motion prediction in real applications is not an isolated "observe then predict" unit, but a consecutive process composed of many rounds of such unit, semi-overlapped along the entire sequence. As time goes on, the predicted part of previous round has its corresponding ground truth observable in the new round, but their deviation in-between is neither exploited nor able to be captured by existing isolated learning fashion. In this paper, we propose DeFeeNet, a simple yet effective network that can be added on existing one-off prediction models to realize deviation perception and feedback when applied to consecutive motion prediction task. At each prediction round, the deviation generated by previous unit is first encoded by our DeFeeNet, and then incorporated into the existing predictor to enable a deviation-aware prediction manner, which, for the first time, allows for information transmit across adjacent prediction units. We design two versions of DeFeeNet as MLP-based and GRU-based, respectively. On Human3.6M and more complicated BABEL, experimental results indicate that our proposed network improves consecutive human motion prediction performance regardless of the basic model. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_DeFeeNet_Consecutive_3D_Human_Motion_Prediction_With_Deviation_Feedback_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sun_DeFeeNet_Consecutive_3D_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.04496 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_DeFeeNet_Consecutive_3D_Human_Motion_Prediction_With_Deviation_Feedback_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_DeFeeNet_Consecutive_3D_Human_Motion_Prediction_With_Deviation_Feedback_CVPR_2023_paper.html | CVPR 2023 | null |
Where We Are and What We're Looking At: Query Based Worldwide Image Geo-Localization Using Hierarchies and Scenes | Brandon Clark, Alec Kerrigan, Parth Parag Kulkarni, Vicente Vivanco Cepeda, Mubarak Shah | Determining the exact latitude and longitude that a photo was taken is a useful and widely applicable task, yet it remains exceptionally difficult despite the accelerated progress of other computer vision tasks. Most previous approaches have opted to learn single representations of query images, which are then classified at different levels of geographic granularity. These approaches fail to exploit the different visual cues that give context to different hierarchies, such as the country, state, and city level. To this end, we introduce an end-to-end transformer-based architecture that exploits the relationship between different geographic levels (which we refer to as hierarchies) and the corresponding visual scene information in an image through hierarchical cross-attention. We achieve this by learning a query for each geographic hierarchy and scene type. Furthermore, we learn a separate representation for different environmental scenes, as different scenes in the same location are often defined by completely different visual features. We achieve state of the art accuracy on 4 standard geo-localization datasets : Im2GPS, Im2GPS3k, YFCC4k, and YFCC26k, as well as qualitatively demonstrate how our method learns different representations for different visual hierarchies and scenes, which has not been demonstrated in the previous methods. Above previous testing datasets mostly consist of iconic landmarks or images taken from social media, which makes the dataset a simple memory task, or makes it biased towards certain places. To address this issue we introduce a much harder testing dataset, Google-World-Streets-15k, comprised of images taken from Google Streetview covering the whole planet and present state of the art results. Our code can be found at https://github.com/AHKerrigan/GeoGuessNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Clark_Where_We_Are_and_What_Were_Looking_At_Query_Based_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Clark_Where_We_Are_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Clark_Where_We_Are_and_What_Were_Looking_At_Query_Based_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Clark_Where_We_Are_and_What_Were_Looking_At_Query_Based_CVPR_2023_paper.html | CVPR 2023 | null |
Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection | Muhammad Akhtar Munir, Muhammad Haris Khan, Salman Khan, Fahad Shahbaz Khan | Deep neural networks (DNNs) have enabled astounding progress in several vision-based problems. Despite showing high predictive accuracy, recently, several works have revealed that they tend to provide overconfident predictions and thus are poorly calibrated. The majority of the works addressing the miscalibration of DNNs fall under the scope of classification and consider only in-domain predictions. However, there is little to no progress in studying the calibration of DNN-based object detection models, which are central to many vision-based safety-critical applications. In this paper, inspired by the train-time calibration methods, we propose a novel auxiliary loss formulation that explicitly aims to align the class confidence of bounding boxes with the accurateness of predictions (i.e. precision). Since the original formulation of our loss depends on the counts of true positives and false positives in a minibatch, we develop a differentiable proxy of our loss that can be used during training with other application-specific loss functions. We perform extensive experiments on challenging in-domain and out-domain scenarios with six benchmark datasets including MS-COCO, Cityscapes, Sim10k, and BDD100k. Our results reveal that our train-time loss surpasses strong calibration baselines in reducing calibration error for both in and out-domain scenarios. Our source code and pre-trained models are available at https://github.com/akhtarvision/bpc_calibration | https://openaccess.thecvf.com/content/CVPR2023/papers/Munir_Bridging_Precision_and_Confidence_A_Train-Time_Loss_for_Calibrating_Object_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Munir_Bridging_Precision_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14404 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Munir_Bridging_Precision_and_Confidence_A_Train-Time_Loss_for_Calibrating_Object_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Munir_Bridging_Precision_and_Confidence_A_Train-Time_Loss_for_Calibrating_Object_CVPR_2023_paper.html | CVPR 2023 | null |
DyLiN: Making Light Field Networks Dynamic | Heng Yu, Joel Julin, Zoltán Á. Milacski, Koichiro Niinuma, László A. Jeni | Light Field Networks, the re-formulations of radiance fields to oriented rays, are magnitudes faster than their coordinate network counterparts, and provide higher fidelity with respect to representing 3D structures from 2D observations. They would be well suited for generic scene representation and manipulation, but suffer from one problem: they are limited to holistic and static scenes. In this paper, we propose the Dynamic Light Field Network (DyLiN) method that can handle non-rigid deformations, including topological changes. We learn a deformation field from input rays to canonical rays, and lift them into a higher dimensional space to handle discontinuities. We further introduce CoDyLiN, which augments DyLiN with controllable attribute inputs. We train both models via knowledge distillation from pretrained dynamic radiance fields. We evaluated DyLiN using both synthetic and real world datasets that include various non-rigid deformations. DyLiN qualitatively outperformed and quantitatively matched state-of-the-art methods in terms of visual fidelity, while being 25 - 71x computationally faster. We also tested CoDyLiN on attribute annotated data and it surpassed its teacher model. Project page: https://dylin2023.github.io. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_DyLiN_Making_Light_Field_Networks_Dynamic_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_DyLiN_Making_Light_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14243 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_DyLiN_Making_Light_Field_Networks_Dynamic_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_DyLiN_Making_Light_Field_Networks_Dynamic_CVPR_2023_paper.html | CVPR 2023 | null |
Critical Learning Periods for Multisensory Integration in Deep Networks | Michael Kleinman, Alessandro Achille, Stefano Soatto | We show that the ability of a neural network to integrate information from diverse sources hinges critically on being exposed to properly correlated signals during the early phases of training. Interfering with the learning process during this initial stage can permanently impair the development of a skill, both in artificial and biological systems where the phenomenon is known as a critical learning period. We show that critical periods arise from the complex and unstable early transient dynamics, which are decisive of final performance of the trained system and their learned representations. This evidence challenges the view, engendered by analysis of wide and shallow networks, that early learning dynamics of neural networks are simple, akin to those of a linear model. Indeed, we show that even deep linear networks exhibit critical learning periods for multi-source integration, while shallow networks do not. To better understand how the internal representations change according to disturbances or sensory deficits, we introduce a new measure of source sensitivity, which allows us to track the inhibition and integration of sources during training. Our analysis of inhibition suggests cross-source reconstruction as a natural auxiliary training objective, and indeed we show that architectures trained with cross-sensor reconstruction objectives are remarkably more resilient to critical periods. Our findings suggest that the recent success in self-supervised multi-modal training compared to previous supervised efforts may be in part due to more robust learning dynamics and not solely due to better architectures and/or more data. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kleinman_Critical_Learning_Periods_for_Multisensory_Integration_in_Deep_Networks_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kleinman_Critical_Learning_Periods_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2210.04643 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kleinman_Critical_Learning_Periods_for_Multisensory_Integration_in_Deep_Networks_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kleinman_Critical_Learning_Periods_for_Multisensory_Integration_in_Deep_Networks_CVPR_2023_paper.html | CVPR 2023 | null |
Human Guided Ground-Truth Generation for Realistic Image Super-Resolution | Du Chen, Jie Liang, Xindong Zhang, Ming Liu, Hui Zeng, Lei Zhang | How to generate the ground-truth (GT) image is a critical issue for training realistic image super-resolution (Real-ISR) models. Existing methods mostly take a set of high-resolution (HR) images as GTs and apply various degradations to simulate their low-resolution (LR) counterparts. Though great progress has been achieved, such an LR-HR pair generation scheme has several limitations. First, the perceptual quality of HR images may not be high enough, limiting the quality of Real-ISR outputs. Second, existing schemes do not consider much human perception in GT generation, and the trained models tend to produce over-smoothed results or unpleasant artifacts. With the above considerations, we propose a human guided GT generation scheme. We first elaborately train multiple image enhancement models to improve the perceptual quality of HR images, and enable one LR image having multiple HR counterparts. Human subjects are then involved to annotate the high quality regions among the enhanced HR images as GTs, and label the regions with unpleasant artifacts as negative samples. A human guided GT image dataset with both positive and negative samples is then constructed, and a loss function is proposed to train the Real-ISR models. Experiments show that the Real-ISR models trained on our dataset can produce perceptually more realistic results with less artifacts. Dataset and codes can be found at https://github.com/ChrisDud0257/HGGT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Human_Guided_Ground-Truth_Generation_for_Realistic_Image_Super-Resolution_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Human_Guided_Ground-Truth_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13069 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Human_Guided_Ground-Truth_Generation_for_Realistic_Image_Super-Resolution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Human_Guided_Ground-Truth_Generation_for_Realistic_Image_Super-Resolution_CVPR_2023_paper.html | CVPR 2023 | null |
GarmentTracking: Category-Level Garment Pose Tracking | Han Xue, Wenqiang Xu, Jieyi Zhang, Tutian Tang, Yutong Li, Wenxin Du, Ruolin Ye, Cewu Lu | Garments are important to humans. A visual system that can estimate and track the complete garment pose can be useful for many downstream tasks and real-world applications. In this work, we present a complete package to address the category-level garment pose tracking task: (1) A recording system VR-Garment, with which users can manipulate virtual garment models in simulation through a VR interface. (2) A large-scale dataset VR-Folding, with complex garment pose configurations in manipulation like flattening and folding. (3) An end-to-end online tracking framework GarmentTracking, which predicts complete garment pose both in canonical space and task space given a point cloud sequence. Extensive experiments demonstrate that the proposed GarmentTracking achieves great performance even when the garment has large non-rigid deformation. It outperforms the baseline approach on both speed and accuracy. We hope our proposed solution can serve as a platform for future research. Codes and datasets are available in https://garment-tracking.robotflow.ai. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xue_GarmentTracking_Category-Level_Garment_Pose_Tracking_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xue_GarmentTracking_Category-Level_Garment_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.13913 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xue_GarmentTracking_Category-Level_Garment_Pose_Tracking_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xue_GarmentTracking_Category-Level_Garment_Pose_Tracking_CVPR_2023_paper.html | CVPR 2023 | null |
Mask DINO: Towards a Unified Transformer-Based Framework for Object Detection and Segmentation | Feng Li, Hao Zhang, Huaizhe Xu, Shilong Liu, Lei Zhang, Lionel M. Ni, Heung-Yeung Shum | In this paper we present Mask DINO, a unified object detection and segmentation framework. Mask DINO extends DINO (DETR with Improved Denoising Anchor Boxes) by adding a mask prediction branch which supports all image segmentation tasks (instance, panoptic, and semantic). It makes use of the query embeddings from DINO to dot-product a high-resolution pixel embedding map to predict a set of binary masks. Some key components in DINO are extended for segmentation through a shared architecture and training process. Mask DINO is simple, efficient, scalable, and benefits from joint large-scale detection and segmentation datasets. Our experiments show that Mask DINO significantly outperforms all existing specialized segmentation methods, both on a ResNet-50 backbone and a pre-trained model with SwinL backbone. Notably, Mask DINO establishes the best results to date on instance segmentation (54.5 AP on COCO), panoptic segmentation (59.4 PQ on COCO), and semantic segmentation (60.8 mIoU on ADE20K) among models under one billion parameters. We will release the code after the blind review. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Mask_DINO_Towards_a_Unified_Transformer-Based_Framework_for_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Mask_DINO_Towards_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2206.02777 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Mask_DINO_Towards_a_Unified_Transformer-Based_Framework_for_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Mask_DINO_Towards_a_Unified_Transformer-Based_Framework_for_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Align and Attend: Multimodal Summarization With Dual Contrastive Losses | Bo He, Jun Wang, Jielin Qiu, Trung Bui, Abhinav Shrivastava, Zhaowen Wang | The goal of multimodal summarization is to extract the most important information from different modalities to form summaries. Unlike unimodal summarization, the multimodal summarization task explicitly leverages cross-modal information to help generate more reliable and high-quality summaries. However, existing methods fail to leverage the temporal correspondence between different modalities and ignore the intrinsic correlation between different samples. To address this issue, we introduce Align and Attend Multimodal Summarization (A2Summ), a unified multimodal transformer-based model which can effectively align and attend the multimodal input. In addition, we propose two novel contrastive losses to model both inter-sample and intra-sample correlations. Extensive experiments on two standard video summarization datasets (TVSum and SumMe) and two multimodal summarization datasets (Daily Mail and CNN) demonstrate the superiority of A2Summ, achieving state-of-the-art performances on all datasets. Moreover, we collected a large-scale multimodal summarization dataset BLiSS, which contains livestream videos and transcribed texts with annotated summaries. Our code and dataset are publicly available at https://boheumd.github.io/A2Summ/. | https://openaccess.thecvf.com/content/CVPR2023/papers/He_Align_and_Attend_Multimodal_Summarization_With_Dual_Contrastive_Losses_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/He_Align_and_Attend_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.07284 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/He_Align_and_Attend_Multimodal_Summarization_With_Dual_Contrastive_Losses_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/He_Align_and_Attend_Multimodal_Summarization_With_Dual_Contrastive_Losses_CVPR_2023_paper.html | CVPR 2023 | null |
SinGRAF: Learning a 3D Generative Radiance Field for a Single Scene | Minjung Son, Jeong Joon Park, Leonidas Guibas, Gordon Wetzstein | Generative models have shown great promise in synthesizing photorealistic 3D objects, but they require large amounts of training data. We introduce SinGRAF, a 3D-aware generative model that is trained with a few input images of a single scene. Once trained, SinGRAF generates different realizations of this 3D scene that preserve the appearance of the input while varying scene layout. For this purpose, we build on recent progress in 3D GAN architectures and introduce a novel progressive-scale patch discrimination approach during training. With several experiments, we demonstrate that the results produced by SinGRAF outperform the closest related works in both quality and diversity by a large margin. | https://openaccess.thecvf.com/content/CVPR2023/papers/Son_SinGRAF_Learning_a_3D_Generative_Radiance_Field_for_a_Single_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Son_SinGRAF_Learning_a_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2211.17260 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Son_SinGRAF_Learning_a_3D_Generative_Radiance_Field_for_a_Single_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Son_SinGRAF_Learning_a_3D_Generative_Radiance_Field_for_a_Single_CVPR_2023_paper.html | CVPR 2023 | null |
Self-Supervised AutoFlow | Hsin-Ping Huang, Charles Herrmann, Junhwa Hur, Erika Lu, Kyle Sargent, Austin Stone, Ming-Hsuan Yang, Deqing Sun | Recently, AutoFlow has shown promising results on learning a training set for optical flow, but requires ground truth labels in the target domain to compute its search metric. Observing a strong correlation between the ground truth search metric and self-supervised losses, we introduce self-supervised AutoFlow to handle real-world videos without ground truth labels. Using self-supervised loss as the search metric, our self-supervised AutoFlow performs on par with AutoFlow on Sintel and KITTI where ground truth is available, and performs better on the real-world DAVIS dataset. We further explore using self-supervised AutoFlow in the (semi-)supervised setting and obtain competitive results against the state of the art. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Self-Supervised_AutoFlow_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Self-Supervised_AutoFlow_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.01762 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Self-Supervised_AutoFlow_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Self-Supervised_AutoFlow_CVPR_2023_paper.html | CVPR 2023 | null |
MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recovery | Duowen Chen, Yunhao Bai, Wei Shen, Qingli Li, Lequan Yu, Yan Wang | We propose a novel teacher-student model for semi-supervised multi-organ segmentation. In the teacher-student model, data augmentation is usually adopted on unlabeled data to regularize the consistent training between teacher and student. We start from a key perspective that fixed relative locations and variable sizes of different organs can provide distribution information where a multi-organ CT scan is drawn. Thus, we treat the prior anatomy as a strong tool to guide the data augmentation and reduce the mismatch between labeled and unlabeled images for semi-supervised learning. More specifically, we propose a data augmentation strategy based on partition-and-recovery N^3 cubes cross- and within- labeled and unlabeled images. Our strategy encourages unlabeled images to learn organ semantics in relative locations from the labeled images (cross-branch) and enhances the learning ability for small organs (within-branch). For within-branch, we further propose to refine the quality of pseudo labels by blending the learned representations from small cubes to incorporate local attributes. Our method is termed as MagicNet, since it treats the CT volume as a magic-cube and N^3-cube partition-and-recovery process matches with the rule of playing a magic-cube. Extensive experiments on two public CT multi-organ datasets demonstrate the effectiveness of MagicNet, and noticeably outperforms state-of-the-art semi-supervised medical image segmentation approaches, with +7% DSC improvement on MACT dataset with 10% labeled images. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_MagicNet_Semi-Supervised_Multi-Organ_Segmentation_via_Magic-Cube_Partition_and_Recovery_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_MagicNet_Semi-Supervised_Multi-Organ_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.14310 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_MagicNet_Semi-Supervised_Multi-Organ_Segmentation_via_Magic-Cube_Partition_and_Recovery_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_MagicNet_Semi-Supervised_Multi-Organ_Segmentation_via_Magic-Cube_Partition_and_Recovery_CVPR_2023_paper.html | CVPR 2023 | null |
Neuralangelo: High-Fidelity Neural Surface Reconstruction | Zhaoshuo Li, Thomas Müller, Alex Evans, Russell H. Taylor, Mathias Unberath, Ming-Yu Liu, Chen-Hsuan Lin | Neural surface reconstruction has been shown to be powerful for recovering dense 3D surfaces via image-based neural rendering. However, current methods struggle to recover detailed structures of real-world scenes. To address the issue, we present Neuralangelo, which combines the representation power of multi-resolution 3D hash grids with neural surface rendering. Two key ingredients enable our approach: (1) numerical gradients for computing higher-order derivatives as a smoothing operation and (2) coarse-to-fine optimization on the hash grids controlling different levels of details. Even without auxiliary inputs such as depth, Neuralangelo can effectively recover dense 3D surface structures from multi-view images with fidelity significantly surpassing previous methods, enabling detailed large-scale scene reconstruction from RGB video captures. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Neuralangelo_High-Fidelity_Neural_Surface_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Neuralangelo_High-Fidelity_Neural_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Neuralangelo_High-Fidelity_Neural_Surface_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Neuralangelo_High-Fidelity_Neural_Surface_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
Re-GAN: Data-Efficient GANs Training via Architectural Reconfiguration | Divya Saxena, Jiannong Cao, Jiahao Xu, Tarun Kulshrestha | Training Generative Adversarial Networks (GANs) on high-fidelity images usually requires a vast number of training images. Recent research on GAN tickets reveals that dense GANs models contain sparse sub-networks or "lottery tickets" that, when trained separately, yield better results under limited data. However, finding GANs tickets requires an expensive process of train-prune-retrain. In this paper, we propose Re-GAN, a data-efficient GANs training that dynamically reconfigures GANs architecture during training to explore different sub-network structures in training time. Our method repeatedly prunes unimportant connections to regularize GANs network and regrows them to reduce the risk of prematurely pruning important connections. Re-GAN stabilizes the GANs models with less data and offers an alternative to the existing GANs tickets and progressive growing methods. We demonstrate that Re-GAN is a generic training methodology which achieves stability on datasets of varying sizes, domains, and resolutions (CIFAR-10, Tiny-ImageNet, and multiple few-shot generation datasets) as well as different GANs architectures (SNGAN, ProGAN, StyleGAN2 and AutoGAN). Re-GAN also improves performance when combined with the recent augmentation approaches. Moreover, Re-GAN requires fewer floating-point operations (FLOPs) and less training time by removing the unimportant connections during GANs training while maintaining comparable or even generating higher-quality samples. When compared to state-of-the-art StyleGAN2, our method outperforms without requiring any additional fine-tuning step. Code can be found at this link: https://github.com/IntellicentAI-Lab/Re-GAN | https://openaccess.thecvf.com/content/CVPR2023/papers/Saxena_Re-GAN_Data-Efficient_GANs_Training_via_Architectural_Reconfiguration_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Saxena_Re-GAN_Data-Efficient_GANs_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Saxena_Re-GAN_Data-Efficient_GANs_Training_via_Architectural_Reconfiguration_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Saxena_Re-GAN_Data-Efficient_GANs_Training_via_Architectural_Reconfiguration_CVPR_2023_paper.html | CVPR 2023 | null |
Dimensionality-Varying Diffusion Process | Han Zhang, Ruili Feng, Zhantao Yang, Lianghua Huang, Yu Liu, Yifei Zhang, Yujun Shen, Deli Zhao, Jingren Zhou, Fan Cheng | Diffusion models, which learn to reverse a signal destruction process to generate new data, typically require the signal at each step to have the same dimension. We argue that, considering the spatial redundancy in image signals, there is no need to maintain a high dimensionality in the evolution process, especially in the early generation phase. To this end, we make a theoretical generalization of the forward diffusion process via signal decomposition. Concretely, we manage to decompose an image into multiple orthogonal components and control the attenuation of each component when perturbing the image. That way, along with the noise strength increasing, we are able to diminish those inconsequential components and thus use a lower-dimensional signal to represent the source, barely losing information. Such a reformulation allows to vary dimensions in both training and inference of diffusion models. Extensive experiments on a range of datasets suggest that our approach substantially reduces the computational cost and achieves on-par or even better synthesis performance compared to baseline methods. We also show that our strategy facilitates high-resolution image synthesis and improves FID of diffusion model trained on FFHQ at 1024x1024 resolution from 52.40 to 10.46. Code is available at https://github.com/damo-vilab/dvdp. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Dimensionality-Varying_Diffusion_Process_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Dimensionality-Varying_Diffusion_Process_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.16032 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Dimensionality-Varying_Diffusion_Process_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Dimensionality-Varying_Diffusion_Process_CVPR_2023_paper.html | CVPR 2023 | null |
FAME-ViL: Multi-Tasking Vision-Language Model for Heterogeneous Fashion Tasks | Xiao Han, Xiatian Zhu, Licheng Yu, Li Zhang, Yi-Zhe Song, Tao Xiang | In the fashion domain, there exists a variety of vision-and-language (V+L) tasks, including cross-modal retrieval, text-guided image retrieval, multi-modal classification, and image captioning. They differ drastically in each individual input/output format and dataset size. It has been common to design a task-specific model and fine-tune it independently from a pre-trained V+L model (e.g., CLIP). This results in parameter inefficiency and inability to exploit inter-task relatedness. To address such issues, we propose a novel FAshion-focused Multi-task Efficient learning method for Vision-and-Language tasks (FAME-ViL) in this work. Compared with existing approaches, FAME-ViL applies a single model for multiple heterogeneous fashion tasks, therefore being much more parameter-efficient. It is enabled by two novel components: (1) a task-versatile architecture with cross-attention adapters and task-specific adapters integrated into a unified V+L model, and (2) a stable and effective multi-task training strategy that supports learning from heterogeneous data and prevents negative transfer. Extensive experiments on four fashion tasks show that our FAME-ViL can save 61.5% of parameters over alternatives, while significantly outperforming the conventional independently trained single-task models. Code is available at https://github.com/BrandonHanx/FAME-ViL. | https://openaccess.thecvf.com/content/CVPR2023/papers/Han_FAME-ViL_Multi-Tasking_Vision-Language_Model_for_Heterogeneous_Fashion_Tasks_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Han_FAME-ViL_Multi-Tasking_Vision-Language_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Han_FAME-ViL_Multi-Tasking_Vision-Language_Model_for_Heterogeneous_Fashion_Tasks_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Han_FAME-ViL_Multi-Tasking_Vision-Language_Model_for_Heterogeneous_Fashion_Tasks_CVPR_2023_paper.html | CVPR 2023 | null |
Neural Intrinsic Embedding for Non-Rigid Point Cloud Matching | null | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Neural_Intrinsic_Embedding_for_Non-Rigid_Point_Cloud_Matching_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Neural_Intrinsic_Embedding_for_Non-Rigid_Point_Cloud_Matching_CVPR_2023_paper.html | CVPR 2023 | null |
Rate Gradient Approximation Attack Threats Deep Spiking Neural Networks | Tong Bu, Jianhao Ding, Zecheng Hao, Zhaofei Yu | Spiking Neural Networks (SNNs) have attracted significant attention due to their energy-efficient properties and potential application on neuromorphic hardware. State-of-the-art SNNs are typically composed of simple Leaky Integrate-and-Fire (LIF) neurons and have become comparable to ANNs in image classification tasks on large-scale datasets. However, the robustness of these deep SNNs has not yet been fully uncovered. In this paper, we first experimentally observe that layers in these SNNs mostly communicate by rate coding. Based on this rate coding property, we develop a novel rate coding SNN-specified attack method, Rate Gradient Approximation Attack (RGA). We generalize the RGA attack to SNNs composed of LIF neurons with different leaky parameters and input encoding by designing surrogate gradients. In addition, we develop the time-extended enhancement to generate more effective adversarial examples. The experiment results indicate that our proposed RGA attack is more effective than the previous attack and is less sensitive to neuron hyperparameters. We also conclude from the experiment that rate-coded SNN composed of LIF neurons is not secure, which calls for exploring training methods for SNNs composed of complex neurons and other neuronal codings. Code is available at https://github.com/putshua/SNN_attack_RGA | https://openaccess.thecvf.com/content/CVPR2023/papers/Bu_Rate_Gradient_Approximation_Attack_Threats_Deep_Spiking_Neural_Networks_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bu_Rate_Gradient_Approximation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bu_Rate_Gradient_Approximation_Attack_Threats_Deep_Spiking_Neural_Networks_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bu_Rate_Gradient_Approximation_Attack_Threats_Deep_Spiking_Neural_Networks_CVPR_2023_paper.html | CVPR 2023 | null |
Few-Shot Geometry-Aware Keypoint Localization | Xingzhe He, Gaurav Bharaj, David Ferman, Helge Rhodin, Pablo Garrido | Supervised keypoint localization methods rely on large manually labeled image datasets, where objects can deform, articulate, or occlude. However, creating such large keypoint labels is time-consuming and costly, and is often error-prone due to inconsistent labeling. Thus, we desire an approach that can learn keypoint localization with fewer yet consistently annotated images. To this end, we present a novel formulation that learns to localize semantically consistent keypoint definitions, even for occluded regions, for varying object categories. We use a few user-labeled 2D images as input examples, which are extended via self-supervision using a larger unlabeled dataset. Unlike unsupervised methods, the few-shot images act as semantic shape constraints for object localization. Furthermore, we introduce 3D geometry-aware constraints to uplift keypoints, achieving more accurate 2D localization. Our general-purpose formulation paves the way for semantically conditioned generative modeling and attains competitive or state-of-the-art accuracy on several datasets, including human faces, eyes, animals, cars, and never-before-seen mouth interior (teeth) localization tasks, not attempted by the previous few-shot methods. Project page: https://xingzhehe.github.io/FewShot3DKP/ | https://openaccess.thecvf.com/content/CVPR2023/papers/He_Few-Shot_Geometry-Aware_Keypoint_Localization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/He_Few-Shot_Geometry-Aware_Keypoint_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.17216 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/He_Few-Shot_Geometry-Aware_Keypoint_Localization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/He_Few-Shot_Geometry-Aware_Keypoint_Localization_CVPR_2023_paper.html | CVPR 2023 | null |
RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generation | Titas Anciukevičius, Zexiang Xu, Matthew Fisher, Paul Henderson, Hakan Bilen, Niloy J. Mitra, Paul Guerrero | Diffusion models currently achieve state-of-the-art performance for both conditional and unconditional image generation. However, so far, image diffusion models do not support tasks required for 3D understanding, such as view-consistent 3D generation or single-view object reconstruction. In this paper, we present RenderDiffusion, the first diffusion model for 3D generation and inference, trained using only monocular 2D supervision. Central to our method is a novel image denoising architecture that generates and renders an intermediate three-dimensional representation of a scene in each denoising step. This enforces a strong inductive structure within the diffusion process, providing a 3D consistent representation while only requiring 2D supervision. The resulting 3D representation can be rendered from any view. We evaluate RenderDiffusion on FFHQ, AFHQ, ShapeNet and CLEVR datasets, showing competitive performance for generation of 3D scenes and inference of 3D scenes from 2D images. Additionally, our diffusion-based approach allows us to use 2D inpainting to edit 3D scenes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Anciukevicius_RenderDiffusion_Image_Diffusion_for_3D_Reconstruction_Inpainting_and_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Anciukevicius_RenderDiffusion_Image_Diffusion_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Anciukevicius_RenderDiffusion_Image_Diffusion_for_3D_Reconstruction_Inpainting_and_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Anciukevicius_RenderDiffusion_Image_Diffusion_for_3D_Reconstruction_Inpainting_and_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
Adaptive Data-Free Quantization | Biao Qian, Yang Wang, Richang Hong, Meng Wang | Data-free quantization (DFQ) recovers the performance of quantized network (Q) without the original data, but generates the fake sample via a generator (G) by learning from full-precision network (P), which, however, is totally independent of Q, overlooking the adaptability of the knowledge from generated samples, i.e., informative or not to the learning process of Q, resulting into the overflow of generalization error. Building on this, several critical questions -- how to measure the sample adaptability to Q under varied bit-width scenarios? whether the largest adaptability is the best? how to generate the samples with adaptive adaptability to improve Q's generalization? To answer the above questions, in this paper, we propose an Adaptive Data-Free Quantization (AdaDFQ) method, which revisits DFQ from a zero-sum game perspective upon the sample adaptability between two players -- a generator and a quantized network. Following this viewpoint, we further define the disagreement and agreement samples to form two boundaries, where the margin between two boundaries is optimized to adaptively regulate the adaptability of generated samples to Q, so as to address the over-and-under fitting issues. Our AdaDFQ reveals: 1) the largest adaptability is NOT the best for sample generation to benefit Q's generalization; 2) the knowledge of the generated sample should not be informative to Q only, but also related to the category and distribution information of the training data for P. The theoretical and empirical analysis validate the advantages of AdaDFQ over the state-of-the-arts. Our code is available at https://github.com/hfutqian/AdaDFQ. | https://openaccess.thecvf.com/content/CVPR2023/papers/Qian_Adaptive_Data-Free_Quantization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qian_Adaptive_Data-Free_Quantization_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.06869 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qian_Adaptive_Data-Free_Quantization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qian_Adaptive_Data-Free_Quantization_CVPR_2023_paper.html | CVPR 2023 | null |
Neural Vector Fields: Implicit Representation by Explicit Learning | Xianghui Yang, Guosheng Lin, Zhenghao Chen, Luping Zhou | Deep neural networks (DNNs) are widely applied for nowadays 3D surface reconstruction tasks and such methods can be further divided into two categories, which respectively warp templates explicitly by moving vertices or represent 3D surfaces implicitly as signed or unsigned distance functions. Taking advantage of both advanced explicit learning process and powerful representation ability of implicit functions, we propose a novel 3D representation method, Neural Vector Fields (NVF). It not only adopts the explicit learning process to manipulate meshes directly, but also leverages the implicit representation of unsigned distance functions (UDFs) to break the barriers in resolution and topology. Specifically, our method first predicts the displacements from queries towards the surface and models the shapes as Vector Fields. Rather than relying on network differentiation to obtain direction fields as most existing UDF-based methods, the produced vector fields encode the distance and direction fields both and mitigate the ambiguity at "ridge" points, such that the calculation of direction fields is straightforward and differentiation-free. The differentiation-free characteristic enables us to further learn a shape codebook via Vector Quantization, which encodes the cross-object priors, accelerates the training procedure, and boosts model generalization on cross-category reconstruction. The extensive experiments on surface reconstruction benchmarks indicate that our method outperforms those state-of-the-art methods in different evaluation scenarios including watertight vs non-watertight shapes, category-specific vs category-agnostic reconstruction, category-unseen reconstruction, and cross-domain reconstruction. Our code is released at https://github.com/Wi-sc/NVF. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Neural_Vector_Fields_Implicit_Representation_by_Explicit_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Neural_Vector_Fields_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.04341 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Neural_Vector_Fields_Implicit_Representation_by_Explicit_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Neural_Vector_Fields_Implicit_Representation_by_Explicit_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures | Gal Metzer, Elad Richardson, Or Patashnik, Raja Giryes, Daniel Cohen-Or | Text-guided image generation has progressed rapidly in recent years, inspiring major breakthroughs in text-guided shape generation. Recently, it has been shown that using score distillation, one can successfully text-guide a NeRF model to generate a 3D object. We adapt the score distillation to the publicly available, and computationally efficient, Latent Diffusion Models, which apply the entire diffusion process in a compact latent space of a pretrained autoencoder. As NeRFs operate in image space, a naive solution for guiding them with latent score distillation would require encoding to the latent space at each guidance step. Instead, we propose to bring the NeRF to the latent space, resulting in a Latent-NeRF. Analyzing our Latent-NeRF, we show that while Text-to-3D models can generate impressive results, they are inherently unconstrained and may lack the ability to guide or enforce a specific 3D structure. To assist and direct the 3D generation, we propose to guide our Latent-NeRF using a Sketch-Shape: an abstract geometry that defines the coarse structure of the desired object. Then, we present means to integrate such a constraint directly into a Latent-NeRF. This unique combination of text and shape guidance allows for increased control over the generation process. We also show that latent score distillation can be successfully applied directly on 3D meshes. This allows for generating high-quality textures on a given geometry. Our experiments validate the power of our different forms of guidance and the efficiency of using latent rendering. | https://openaccess.thecvf.com/content/CVPR2023/papers/Metzer_Latent-NeRF_for_Shape-Guided_Generation_of_3D_Shapes_and_Textures_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2211.07600 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Metzer_Latent-NeRF_for_Shape-Guided_Generation_of_3D_Shapes_and_Textures_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Metzer_Latent-NeRF_for_Shape-Guided_Generation_of_3D_Shapes_and_Textures_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Generative Structure Prior for Blind Text Image Super-Resolution | Xiaoming Li, Wangmeng Zuo, Chen Change Loy | Blind text image super-resolution (SR) is challenging as one needs to cope with diverse font styles and unknown degradation. To address the problem, existing methods perform character recognition in parallel to regularize the SR task, either through a loss constraint or intermediate feature condition. Nonetheless, the high-level prior could still fail when encountering severe degradation. The problem is further compounded given characters of complex structures, e.g., Chinese characters that combine multiple pictographic or ideographic symbols into a single character. In this work, we present a novel prior that focuses more on the character structure. In particular, we learn to encapsulate rich and diverse structures in a StyleGAN and exploit such generative structure priors for restoration. To restrict the generative space of StyleGAN so that it obeys the structure of characters yet remains flexible in handling different font styles, we store the discrete features for each character in a codebook . The code subsequently drives the StyleGAN to generate high-resolution structural details to aid text SR. Compared to priors based on character recognition, the proposed structure prior exerts stronger character-specific guidance to restore faithful and precise strokes of a designated character. Extensive experiments on synthetic and real datasets demonstrate the compelling performance of the proposed generative structure prior in facilitating robust text SR. Our code is available at https://github.com/csxmli2016/MARCONet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Learning_Generative_Structure_Prior_for_Blind_Text_Image_Super-Resolution_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Learning_Generative_Structure_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14726 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Learning_Generative_Structure_Prior_for_Blind_Text_Image_Super-Resolution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Learning_Generative_Structure_Prior_for_Blind_Text_Image_Super-Resolution_CVPR_2023_paper.html | CVPR 2023 | null |
Overcoming the Trade-Off Between Accuracy and Plausibility in 3D Hand Shape Reconstruction | Ziwei Yu, Chen Li, Linlin Yang, Xiaoxu Zheng, Michael Bi Mi, Gim Hee Lee, Angela Yao | Direct mesh fitting for 3D hand shape reconstruction estimates highly accurate meshes. However, the resulting meshes are prone to artifacts and do not appear as plausible hand shapes. Conversely, parametric models like MANO ensure plausible hand shapes but are not as accurate as the non-parametric methods. In this work, we introduce a novel weakly-supervised hand shape estimation framework that integrates non-parametric mesh fitting with MANO models in an end-to-end fashion. Our joint model overcomes the tradeoff in accuracy and plausibility to yield well-aligned and high-quality 3D meshes, especially in challenging two-hand and hand-object interaction scenarios. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Overcoming_the_Trade-Off_Between_Accuracy_and_Plausibility_in_3D_Hand_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_Overcoming_the_Trade-Off_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.00646 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Overcoming_the_Trade-Off_Between_Accuracy_and_Plausibility_in_3D_Hand_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Overcoming_the_Trade-Off_Between_Accuracy_and_Plausibility_in_3D_Hand_CVPR_2023_paper.html | CVPR 2023 | null |
Open-Vocabulary Attribute Detection | María A. Bravo, Sudhanshu Mittal, Simon Ging, Thomas Brox | Vision-language modeling has enabled open-vocabulary tasks where predictions can be queried using any text prompt in a zero-shot manner. Existing open-vocabulary tasks focus on object classes, whereas research on object attributes is limited due to the lack of a reliable attribute-focused evaluation benchmark. This paper introduces the Open-Vocabulary Attribute Detection (OVAD) task and the corresponding OVAD benchmark. The objective of the novel task and benchmark is to probe object-level attribute information learned by vision-language models. To this end, we created a clean and densely annotated test set covering 117 attribute classes on the 80 object classes of MS COCO. It includes positive and negative annotations, which enables open-vocabulary evaluation. Overall, the benchmark consists of 1.4 million annotations. For reference, we provide a first baseline method for open-vocabulary attribute detection. Moreover, we demonstrate the benchmark's value by studying the attribute detection performance of several foundation models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bravo_Open-Vocabulary_Attribute_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bravo_Open-Vocabulary_Attribute_Detection_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.12914 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bravo_Open-Vocabulary_Attribute_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bravo_Open-Vocabulary_Attribute_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
PEFAT: Boosting Semi-Supervised Medical Image Classification via Pseudo-Loss Estimation and Feature Adversarial Training | Qingjie Zeng, Yutong Xie, Zilin Lu, Yong Xia | Pseudo-labeling approaches have been proven beneficial for semi-supervised learning (SSL) schemes in computer vision and medical imaging. Most works are dedicated to finding samples with high-confidence pseudo-labels from the perspective of model predicted probability. Whereas this way may lead to the inclusion of incorrectly pseudo-labeled data if the threshold is not carefully adjusted. In addition, low-confidence probability samples are frequently disregarded and not employed to their full potential. In this paper, we propose a novel Pseudo-loss Estimation and Feature Adversarial Training semi-supervised framework, termed as PEFAT, to boost the performance of multi-class and multi-label medical image classification from the point of loss distribution modeling and adversarial training. Specifically, we develop a trustworthy data selection scheme to split a high-quality pseudo-labeled set, inspired by the dividable pseudo-loss assumption that clean data tend to show lower loss while noise data is the opposite. Instead of directly discarding these samples with low-quality pseudo-labels, we present a novel regularization approach to learn discriminate information from them via injecting adversarial noises at the feature-level to smooth the decision boundary. Experimental results on three medical and two natural image benchmarks validate that our PEFAT can achieve a promising performance and surpass other state-of-the-art methods. The code is available at https://github.com/maxwell0027/PEFAT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zeng_PEFAT_Boosting_Semi-Supervised_Medical_Image_Classification_via_Pseudo-Loss_Estimation_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zeng_PEFAT_Boosting_Semi-Supervised_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zeng_PEFAT_Boosting_Semi-Supervised_Medical_Image_Classification_via_Pseudo-Loss_Estimation_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zeng_PEFAT_Boosting_Semi-Supervised_Medical_Image_Classification_via_Pseudo-Loss_Estimation_and_CVPR_2023_paper.html | CVPR 2023 | null |
TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Joint Perception and Prediction in Vision-Centric Autonomous Driving | Shaoheng Fang, Zi Wang, Yiqi Zhong, Junhao Ge, Siheng Chen | Vision-centric joint perception and prediction (PnP) has become an emerging trend in autonomous driving research. It predicts the future states of the traffic participants in the surrounding environment from raw RGB images. However, it is still a critical challenge to synchronize features obtained at multiple camera views and timestamps due to inevitable geometric distortions and further exploit those spatial-temporal features. To address this issue, we propose a temporal bird's-eye-view pyramid transformer (TBP-Former) for vision-centric PnP, which includes two novel designs. First, a pose-synchronized BEV encoder is proposed to map raw image inputs with any camera pose at any time to a shared and synchronized BEV space for better spatial-temporal synchronization. Second, a spatial-temporal pyramid transformer is introduced to comprehensively extract multi-scale BEV features and predict future BEV states with the support of spatial priors. Extensive experiments on nuScenes dataset show that our proposed framework overall outperforms all state-of-the-art vision-based prediction methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fang_TBP-Former_Learning_Temporal_Birds-Eye-View_Pyramid_for_Joint_Perception_and_Prediction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fang_TBP-Former_Learning_Temporal_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fang_TBP-Former_Learning_Temporal_Birds-Eye-View_Pyramid_for_Joint_Perception_and_Prediction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fang_TBP-Former_Learning_Temporal_Birds-Eye-View_Pyramid_for_Joint_Perception_and_Prediction_CVPR_2023_paper.html | CVPR 2023 | null |
Ground-Truth Free Meta-Learning for Deep Compressive Sampling | Xinran Qin, Yuhui Quan, Tongyao Pang, Hui Ji | Deep learning has become an important tool for reconstructing images in compressive sampling (CS). This paper proposes a ground-truth (GT) free meta-learning method for CS, which leverages both external and internal learning for unsupervised high-quality image reconstruction. The proposed method first trains a deep model via external meta-learning using only CS measurements, and then efficiently adapts the trained model to a test sample for further improvement by exploiting its internal characteristics. The meta-learning and model adaptation are built on an improved Stein's unbiased risk estimator (iSURE) that provides efficient computation and effective guidance for accurate prediction in the range space of the adjoint of the measurement matrix. To further improve the learning on the null space of the measurement matrix, a modified model-agnostic meta-learning scheme is proposed, along with a null-space-consistent loss and a bias-adaptive deep unrolling network to improve and accelerate model adaption in test time. Experimental results have demonstrated that the proposed GT-free method performs well, and can even compete with supervised learning-based methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Qin_Ground-Truth_Free_Meta-Learning_for_Deep_Compressive_Sampling_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qin_Ground-Truth_Free_Meta-Learning_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qin_Ground-Truth_Free_Meta-Learning_for_Deep_Compressive_Sampling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qin_Ground-Truth_Free_Meta-Learning_for_Deep_Compressive_Sampling_CVPR_2023_paper.html | CVPR 2023 | null |
SHS-Net: Learning Signed Hyper Surfaces for Oriented Normal Estimation of Point Clouds | Qing Li, Huifang Feng, Kanle Shi, Yue Gao, Yi Fang, Yu-Shen Liu, Zhizhong Han | We propose a novel method called SHS-Net for oriented normal estimation of point clouds by learning signed hyper surfaces, which can accurately predict normals with global consistent orientation from various point clouds. Almost all existing methods estimate oriented normals through a two-stage pipeline, i.e., unoriented normal estimation and normal orientation, and each step is implemented by a separate algorithm. However, previous methods are sensitive to parameter settings, resulting in poor results from point clouds with noise, density variations and complex geometries. In this work, we introduce signed hyper surfaces (SHS), which are parameterized by multi-layer perceptron (MLP) layers, to learn to estimate oriented normals from point clouds in an end-to-end manner. The signed hyper surfaces are implicitly learned in a high-dimensional feature space where the local and global information is aggregated. Specifically, we introduce a patch encoding module and a shape encoding module to encode a 3D point cloud into a local latent code and a global latent code, respectively. Then, an attention-weighted normal prediction module is proposed as a decoder, which takes the local and global latent codes as input to predict oriented normals. Experimental results show that our SHS-Net outperforms the state-of-the-art methods in both unoriented and oriented normal estimation on the widely used benchmarks. The code, data and pretrained models are available at https://github.com/LeoQLi/SHS-Net. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_SHS-Net_Learning_Signed_Hyper_Surfaces_for_Oriented_Normal_Estimation_of_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_SHS-Net_Learning_Signed_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_SHS-Net_Learning_Signed_Hyper_Surfaces_for_Oriented_Normal_Estimation_of_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_SHS-Net_Learning_Signed_Hyper_Surfaces_for_Oriented_Normal_Estimation_of_CVPR_2023_paper.html | CVPR 2023 | null |
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