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VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models | Ajay Jain, Amber Xie, Pieter Abbeel | Diffusion models have shown impressive results in text-to-image synthesis. Using massive datasets of captioned images, diffusion models learn to generate raster images of highly diverse objects and scenes. However, designers frequently use vector representations of images like Scalable Vector Graphics (SVGs) for digital icons, graphics and stickers. Vector graphics can be scaled to any size, and are compact. In this work, we show that a text-conditioned diffusion model trained on pixel representations of images can be used to generate SVG-exportable vector graphics. We do so without access to large datasets of captioned SVGs. Instead, inspired by recent work on text-to-3D synthesis, we vectorize a text-to-image diffusion sample and fine-tune with a Score Distillation Sampling loss. By optimizing a differentiable vector graphics rasterizer, our method distills abstract semantic knowledge out of a pretrained diffusion model. By constraining the vector representation, we can also generate coherent pixel art and sketches. Our approach, VectorFusion, produces more coherent graphics than prior works that optimize CLIP, a contrastive image-text model. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jain_VectorFusion_Text-to-SVG_by_Abstracting_Pixel-Based_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jain_VectorFusion_Text-to-SVG_by_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.11319 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jain_VectorFusion_Text-to-SVG_by_Abstracting_Pixel-Based_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jain_VectorFusion_Text-to-SVG_by_Abstracting_Pixel-Based_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
The Dialog Must Go On: Improving Visual Dialog via Generative Self-Training | Gi-Cheon Kang, Sungdong Kim, Jin-Hwa Kim, Donghyun Kwak, Byoung-Tak Zhang | Visual dialog (VisDial) is a task of answering a sequence of questions grounded in an image, using the dialog history as context. Prior work has trained the dialog agents solely on VisDial data via supervised learning or leveraged pre-training on related vision-and-language datasets. This paper presents a semi-supervised learning approach for visually-grounded dialog, called Generative Self-Training (GST), to leverage unlabeled images on the Web. Specifically, GST first retrieves in-domain images through out-of-distribution detection and generates synthetic dialogs regarding the images via multimodal conditional text generation. GST then trains a dialog agent on the synthetic and the original VisDial data. As a result, GST scales the amount of training data up to an order of magnitude that of VisDial (1.2M to 12.9M QA data). For robust training of the synthetic dialogs, we also propose perplexity-based data selection and multimodal consistency regularization. Evaluation on VisDial v1.0 and v0.9 datasets shows that GST achieves new state-of-the-art results on both datasets. We further observe the robustness of GST against both visual and textual adversarial attacks. Finally, GST yields strong performance gains in the low-data regime. Code is available at https://github.com/gicheonkang/gst-visdial. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kang_The_Dialog_Must_Go_On_Improving_Visual_Dialog_via_Generative_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kang_The_Dialog_Must_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2205.12502 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kang_The_Dialog_Must_Go_On_Improving_Visual_Dialog_via_Generative_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kang_The_Dialog_Must_Go_On_Improving_Visual_Dialog_via_Generative_CVPR_2023_paper.html | CVPR 2023 | null |
Binarizing Sparse Convolutional Networks for Efficient Point Cloud Analysis | Xiuwei Xu, Ziwei Wang, Jie Zhou, Jiwen Lu | In this paper, we propose binary sparse convolutional networks called BSC-Net for efficient point cloud analysis. We empirically observe that sparse convolution operation causes larger quantization errors than standard convolution. However, conventional network quantization methods directly binarize the weights and activations in sparse convolution, resulting in performance drop due to the significant quantization loss. On the contrary, we search the optimal subset of convolution operation that activates the sparse convolution at various locations for quantization error alleviation, and the performance gap between real-valued and binary sparse convolutional networks is closed without complexity overhead. Specifically, we first present the shifted sparse convolution that fuses the information in the receptive field for the active sites that match the pre-defined positions. Then we employ the differentiable search strategies to discover the optimal opsitions for active site matching in the shifted sparse convolution, and the quantization errors are significantly alleviated for efficient point cloud analysis. For fair evaluation of the proposed method, we empirically select the recently advances that are beneficial for sparse convolution network binarization to construct a strong baseline. The experimental results on ScanNet and NYU Depth v2 show that our BSC-Net achieves significant improvement upon our srtong baseline and outperforms the state-of-the-art network binarization methods by a remarkable margin without additional computation overhead for binarizing sparse convolutional networks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Binarizing_Sparse_Convolutional_Networks_for_Efficient_Point_Cloud_Analysis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Binarizing_Sparse_Convolutional_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15493 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Binarizing_Sparse_Convolutional_Networks_for_Efficient_Point_Cloud_Analysis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Binarizing_Sparse_Convolutional_Networks_for_Efficient_Point_Cloud_Analysis_CVPR_2023_paper.html | CVPR 2023 | null |
Transformer-Based Learned Optimization | Erik Gärtner, Luke Metz, Mykhaylo Andriluka, C. Daniel Freeman, Cristian Sminchisescu | We propose a new approach to learned optimization where we represent the computation of an optimizer's update step using a neural network. The parameters of the optimizer are then learned by training on a set of optimization tasks with the objective to perform minimization efficiently. Our innovation is a new neural network architecture, Optimus, for the learned optimizer inspired by the classic BFGS algorithm. As in BFGS, we estimate a preconditioning matrix as a sum of rank-one updates but use a Transformer-based neural network to predict these updates jointly with the step length and direction. In contrast to several recent learned optimization-based approaches, our formulation allows for conditioning across the dimensions of the parameter space of the target problem while remaining applicable to optimization tasks of variable dimensionality without retraining. We demonstrate the advantages of our approach on a benchmark composed of objective functions traditionally used for the evaluation of optimization algorithms, as well as on the real world-task of physics-based visual reconstruction of articulated 3d human motion. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gartner_Transformer-Based_Learned_Optimization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gartner_Transformer-Based_Learned_Optimization_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gartner_Transformer-Based_Learned_Optimization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gartner_Transformer-Based_Learned_Optimization_CVPR_2023_paper.html | CVPR 2023 | null |
Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models | Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, Tom Goldstein | Cutting-edge diffusion models produce images with high quality and customizability, enabling them to be used for commercial art and graphic design purposes. But do diffusion models create unique works of art, or are they replicating content directly from their training sets? In this work, we study image retrieval frameworks that enable us to compare generated images with training samples and detect when content has been replicated. Applying our frameworks to diffusion models trained on multiple datasets including Oxford flowers, Celeb-A, ImageNet, and LAION, we discuss how factors such as training set size impact rates of content replication. We also identify cases where diffusion models, including the popular Stable Diffusion model, blatantly copy from their training data. | https://openaccess.thecvf.com/content/CVPR2023/papers/Somepalli_Diffusion_Art_or_Digital_Forgery_Investigating_Data_Replication_in_Diffusion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Somepalli_Diffusion_Art_or_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.03860 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Somepalli_Diffusion_Art_or_Digital_Forgery_Investigating_Data_Replication_in_Diffusion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Somepalli_Diffusion_Art_or_Digital_Forgery_Investigating_Data_Replication_in_Diffusion_CVPR_2023_paper.html | CVPR 2023 | null |
Neuralizer: General Neuroimage Analysis Without Re-Training | Steffen Czolbe, Adrian V. Dalca | Neuroimage processing tasks like segmentation, reconstruction, and registration are central to the study of neuroscience. Robust deep learning strategies and architectures used to solve these tasks are often similar. Yet, when presented with a new task or a dataset with different visual characteristics, practitioners most often need to train a new model, or fine-tune an existing one. This is a time-consuming process that poses a substantial barrier for the thousands of neuroscientists and clinical researchers who often lack the resources or machine-learning expertise to train deep learning models. In practice, this leads to a lack of adoption of deep learning, and neuroscience tools being dominated by classical frameworks. We introduce Neuralizer, a single model that generalizes to previously unseen neuroimaging tasks and modalities without the need for re-training or fine-tuning. Tasks do not have to be known a priori, and generalization happens in a single forward pass during inference. The model can solve processing tasks across multiple image modalities, acquisition methods, and datasets, and generalize to tasks and modalities it has not been trained on. Our experiments on coronal slices show that when few annotated subjects are available, our multi-task network outperforms task-specific baselines without training on the task. | https://openaccess.thecvf.com/content/CVPR2023/papers/Czolbe_Neuralizer_General_Neuroimage_Analysis_Without_Re-Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Czolbe_Neuralizer_General_Neuroimage_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.02644 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Czolbe_Neuralizer_General_Neuroimage_Analysis_Without_Re-Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Czolbe_Neuralizer_General_Neuroimage_Analysis_Without_Re-Training_CVPR_2023_paper.html | CVPR 2023 | null |
Quantum-Inspired Spectral-Spatial Pyramid Network for Hyperspectral Image Classification | Jie Zhang, Yongshan Zhang, Yicong Zhou | Hyperspectral image (HSI) classification aims at assigning a unique label for every pixel to identify categories of different land covers. Existing deep learning models for HSIs are usually performed in a traditional learning paradigm. Being emerging machines, quantum computers are limited in the noisy intermediate-scale quantum (NISQ) era. The quantum theory offers a new paradigm for designing deep learning models. Motivated by the quantum circuit (QC) model, we propose a quantum-inspired spectral-spatial network (QSSN) for HSI feature extraction. The proposed QSSN consists of a phase-prediction module (PPM) and a measurement-like fusion module (MFM) inspired from quantum theory to dynamically fuse spectral and spatial information. Specifically, QSSN uses a quantum representation to represent an HSI cuboid and extracts joint spectral-spatial features using MFM. An HSI cuboid and its phases predicted by PPM are used in the quantum representation. Using QSSN as the building block, we propose an end-to-end quantum-inspired spectral-spatial pyramid network (QSSPN) for HSI feature extraction and classification. In this pyramid framework, QSSPN progressively learns feature representations by cascading QSSN blocks and performs classification with a softmax classifier. It is the first attempt to introduce quantum theory in HSI processing model design. Substantial experiments are conducted on three HSI datasets to verify the superiority of the proposed QSSPN framework over the state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Quantum-Inspired_Spectral-Spatial_Pyramid_Network_for_Hyperspectral_Image_Classification_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Quantum-Inspired_Spectral-Spatial_Pyramid_Network_for_Hyperspectral_Image_Classification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Quantum-Inspired_Spectral-Spatial_Pyramid_Network_for_Hyperspectral_Image_Classification_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial Attacks | Simin Li, Shuning Zhang, Gujun Chen, Dong Wang, Pu Feng, Jiakai Wang, Aishan Liu, Xin Yi, Xianglong Liu | Physical world adversarial attack is a highly practical and threatening attack, which fools real world deep learning systems by generating conspicuous and maliciously crafted real world artifacts. In physical world attacks, evaluating naturalness is highly emphasized since human can easily detect and remove unnatural attacks. However, current studies evaluate naturalness in a case-by-case fashion, which suffers from errors, bias and inconsistencies. In this paper, we take the first step to benchmark and assess visual naturalness of physical world attacks, taking autonomous driving scenario as the first attempt. First, to benchmark attack naturalness, we contribute the first Physical Attack Naturalness (PAN) dataset with human rating and gaze. PAN verifies several insights for the first time: naturalness is (disparately) affected by contextual features (i.e., environmental and semantic variations) and correlates with behavioral feature (i.e., gaze signal). Second, to automatically assess attack naturalness that aligns with human ratings, we further introduce Dual Prior Alignment (DPA) network, which aims to embed human knowledge into model reasoning process. Specifically, DPA imitates human reasoning in naturalness assessment by rating prior alignment and mimics human gaze behavior by attentive prior alignment. We hope our work fosters researches to improve and automatically assess naturalness of physical world attacks. Our code and exemplar data can be found at https://github.com/zhangsn-19/PAN. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Towards_Benchmarking_and_Assessing_Visual_Naturalness_of_Physical_World_Adversarial_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Towards_Benchmarking_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Towards_Benchmarking_and_Assessing_Visual_Naturalness_of_Physical_World_Adversarial_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Towards_Benchmarking_and_Assessing_Visual_Naturalness_of_Physical_World_Adversarial_CVPR_2023_paper.html | CVPR 2023 | null |
Visual Prompt Multi-Modal Tracking | Jiawen Zhu, Simiao Lai, Xin Chen, Dong Wang, Huchuan Lu | Visible-modal object tracking gives rise to a series of downstream multi-modal tracking tributaries. To inherit the powerful representations of the foundation model, a natural modus operandi for multi-modal tracking is full fine-tuning on the RGB-based parameters. Albeit effective, this manner is not optimal due to the scarcity of downstream data and poor transferability, etc. In this paper, inspired by the recent success of the prompt learning in language models, we develop Visual Prompt multi-modal Tracking (ViPT), which learns the modal-relevant prompts to adapt the frozen pre-trained foundation model to various downstream multimodal tracking tasks. ViPT finds a better way to stimulate the knowledge of the RGB-based model that is pre-trained at scale, meanwhile only introducing a few trainable parameters (less than 1% of model parameters). ViPT outperforms the full fine-tuning paradigm on multiple downstream tracking tasks including RGB+Depth, RGB+Thermal, and RGB+Event tracking. Extensive experiments show the potential of visual prompt learning for multi-modal tracking, and ViPT can achieve state-of-the-art performance while satisfying parameter efficiency. Code and models are available at https://github.com/jiawen-zhu/ViPT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_Visual_Prompt_Multi-Modal_Tracking_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.10826 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Visual_Prompt_Multi-Modal_Tracking_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Visual_Prompt_Multi-Modal_Tracking_CVPR_2023_paper.html | CVPR 2023 | null |
Self-Supervised Representation Learning for CAD | Benjamin T. Jones, Michael Hu, Milin Kodnongbua, Vladimir G. Kim, Adriana Schulz | Virtually every object in the modern world was created, modified, analyzed and optimized using computer aided design (CAD) tools. An active CAD research area is the use of data-driven machine learning methods to learn from the massive repositories of geometric and program representations. However, the lack of labeled data in CAD's native format, i.e., the parametric boundary representation (B-Rep), poses an obstacle at present difficult to overcome. Several datasets of mechanical parts in B-Rep format have recently been released for machine learning research. However, large-scale databases are mostly unlabeled, and labeled datasets are small. Additionally, task-specific label sets are rare and costly to annotate. This work proposes to leverage unlabeled CAD geometry on supervised learning tasks. We learn a novel, hybrid implicit/explicit surface representation for B-Rep geometry. Further, we show that this pre-training both significantly improves few-shot learning performance and achieves state-of-the-art performance on several current B-Rep benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jones_Self-Supervised_Representation_Learning_for_CAD_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jones_Self-Supervised_Representation_Learning_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jones_Self-Supervised_Representation_Learning_for_CAD_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jones_Self-Supervised_Representation_Learning_for_CAD_CVPR_2023_paper.html | CVPR 2023 | null |
DETRs With Hybrid Matching | Ding Jia, Yuhui Yuan, Haodi He, Xiaopei Wu, Haojun Yu, Weihong Lin, Lei Sun, Chao Zhang, Han Hu | One-to-one set matching is a key design for DETR to establish its end-to-end capability, so that object detection does not require a hand-crafted NMS (non-maximum suppression) to remove duplicate detections. This end-to-end signature is important for the versatility of DETR, and it has been generalized to broader vision tasks. However, we note that there are few queries assigned as positive samples and the one-to-one set matching significantly reduces the training efficacy of positive samples. We propose a simple yet effective method based on a hybrid matching scheme that combines the original one-to-one matching branch with an auxiliary one-to-many matching branch during training. Our hybrid strategy has been shown to significantly improve accuracy. In inference, only the original one-to-one match branch is used, thus maintaining the end-to-end merit and the same inference efficiency of DETR. The method is named H-DETR, and it shows that a wide range of representative DETR methods can be consistently improved across a wide range of visual tasks, including Deformable-DETR, PETRv2, PETR, and TransTrack, among others. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jia_DETRs_With_Hybrid_Matching_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2207.13080 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jia_DETRs_With_Hybrid_Matching_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jia_DETRs_With_Hybrid_Matching_CVPR_2023_paper.html | CVPR 2023 | null |
Dealing With Cross-Task Class Discrimination in Online Continual Learning | Yiduo Guo, Bing Liu, Dongyan Zhao | Existing continual learning (CL) research regards catastrophic forgetting (CF) as almost the only challenge. This paper argues for another challenge in class-incremental learning (CIL), which we call cross-task class discrimination (CTCD), i.e., how to establish decision boundaries between the classes of the new task and old tasks with no (or limited) access to the old task data. CTCD is implicitly and partially dealt with by replay-based methods. A replay method saves a small amount of data (replay data) from previous tasks. When a batch of current task data arrives, the system jointly trains the new data and some sampled replay data. The replay data enables the system to partially learn the decision boundaries between the new classes and the old classes as the amount of the saved data is small. However, this paper argues that the replay approach also has a dynamic training bias issue which reduces the effectiveness of the replay data in solving the CTCD problem. A novel optimization objective with a gradient-based adaptive method is proposed to dynamically deal with the problem in the online CL process. Experimental results show that the new method achieves much better results in online CL. | https://openaccess.thecvf.com/content/CVPR2023/papers/Guo_Dealing_With_Cross-Task_Class_Discrimination_in_Online_Continual_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Guo_Dealing_With_Cross-Task_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_Dealing_With_Cross-Task_Class_Discrimination_in_Online_Continual_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_Dealing_With_Cross-Task_Class_Discrimination_in_Online_Continual_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Angelic Patches for Improving Third-Party Object Detector Performance | Wenwen Si, Shuo Li, Sangdon Park, Insup Lee, Osbert Bastani | Deep learning models have shown extreme vulnerability to simple perturbations and spatial transformations. In this work, we explore whether we can adopt the characteristics of adversarial attack methods to help improve perturbation robustness for object detection. We study a class of realistic object detection settings wherein the target objects have control over their appearance. To this end, we propose a reversed Fast Gradient Sign Method (FGSM) to obtain these angelic patches that significantly increase the detection probability, even without pre-knowledge of the perturbations. In detail, we apply the patch to each object instance simultaneously, strengthen not only classification but also bounding box accuracy. Experiments demonstrate the efficacy of the partial-covering patch in solving the complex bounding box problem. More importantly, the performance is also transferable to different detection models even under severe affine transformations and deformable shapes. To our knowledge, we are the first (object detection) patch that achieves both cross-model and multiple-patch efficacy. We observed average accuracy improvements of 30% in the real-world experiments, which brings large social value. Our code is available at: https://github.com/averysi224/angelic_patches. | https://openaccess.thecvf.com/content/CVPR2023/papers/Si_Angelic_Patches_for_Improving_Third-Party_Object_Detector_Performance_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Si_Angelic_Patches_for_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Si_Angelic_Patches_for_Improving_Third-Party_Object_Detector_Performance_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Si_Angelic_Patches_for_Improving_Third-Party_Object_Detector_Performance_CVPR_2023_paper.html | CVPR 2023 | null |
UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy | Yinzhen Xu, Weikang Wan, Jialiang Zhang, Haoran Liu, Zikang Shan, Hao Shen, Ruicheng Wang, Haoran Geng, Yijia Weng, Jiayi Chen, Tengyu Liu, Li Yi, He Wang | In this work, we tackle the problem of learning universal robotic dexterous grasping from a point cloud observation under a table-top setting. The goal is to grasp and lift up objects in high-quality and diverse ways and generalize across hundreds of categories and even the unseen. Inspired by successful pipelines used in parallel gripper grasping, we split the task into two stages: 1) grasp proposal (pose) generation and 2) goal-conditioned grasp execution. For the first stage, we propose a novel probabilistic model of grasp pose conditioned on the point cloud observation that factorizes rotation from translation and articulation. Trained on our synthesized large-scale dexterous grasp dataset, this model enables us to sample diverse and high-quality dexterous grasp poses for the object point cloud. For the second stage, we propose to replace the motion planning used in parallel gripper grasping with a goal-conditioned grasp policy, due to the complexity involved in dexterous grasping execution. Note that it is very challenging to learn this highly generalizable grasp policy that only takes realistic inputs without oracle states. We thus propose several important innovations, including state canonicalization, object curriculum, and teacher-student distillation. Integrating the two stages, our final pipeline becomes the first to achieve universal generalization for dexterous grasping, demonstrating an average success rate of more than 60% on thousands of object instances, which significantly outperforms all baselines, meanwhile showing only a minimal generalization gap. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_UniDexGrasp_Universal_Robotic_Dexterous_Grasping_via_Learning_Diverse_Proposal_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_UniDexGrasp_Universal_Robotic_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.00938 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_UniDexGrasp_Universal_Robotic_Dexterous_Grasping_via_Learning_Diverse_Proposal_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_UniDexGrasp_Universal_Robotic_Dexterous_Grasping_via_Learning_Diverse_Proposal_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
A Rotation-Translation-Decoupled Solution for Robust and Efficient Visual-Inertial Initialization | Yijia He, Bo Xu, Zhanpeng Ouyang, Hongdong Li | We propose a novel visual-inertial odometry (VIO) initialization method, which decouples rotation and translation estimation, and achieves higher efficiency and better robustness. Existing loosely-coupled VIO-initialization methods suffer from poor stability of visual structure-from-motion (SfM), whereas those tightly-coupled methods often ignore the gyroscope bias in the closed-form solution, resulting in limited accuracy. Moreover, the aforementioned two classes of methods are computationally expensive, because 3D point clouds need to be reconstructed simultaneously. In contrast, our new method fully combines inertial and visual measurements for both rotational and translational initialization. First, a rotation-only solution is designed for gyroscope bias estimation, which tightly couples the gyroscope and camera observations. Second, the initial velocity and gravity vector are solved with linear translation constraints in a globally optimal fashion and without reconstructing 3D point clouds. Extensive experiments have demonstrated that our method is 8 72 times faster (w.r.t. a 10-frame set) than the state-of-the-art methods, and also presents significantly higher robustness and accuracy. The source code is available at https://github.com/boxuLibrary/drt-vio-init. | https://openaccess.thecvf.com/content/CVPR2023/papers/He_A_Rotation-Translation-Decoupled_Solution_for_Robust_and_Efficient_Visual-Inertial_Initialization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/He_A_Rotation-Translation-Decoupled_Solution_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/He_A_Rotation-Translation-Decoupled_Solution_for_Robust_and_Efficient_Visual-Inertial_Initialization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/He_A_Rotation-Translation-Decoupled_Solution_for_Robust_and_Efficient_Visual-Inertial_Initialization_CVPR_2023_paper.html | CVPR 2023 | null |
GIVL: Improving Geographical Inclusivity of Vision-Language Models With Pre-Training Methods | Da Yin, Feng Gao, Govind Thattai, Michael Johnston, Kai-Wei Chang | A key goal for the advancement of AI is to develop technologies that serve the needs not just of one group but of all communities regardless of their geographical region. In fact, a significant proportion of knowledge is locally shared by people from certain regions but may not apply equally in other regions because of cultural differences. If a model is unaware of regional characteristics, it may lead to performance disparity across regions and result in bias against underrepresented groups. We propose GIVL, a Geographically Inclusive Vision-and-Language Pre-trained model. There are two attributes of geo-diverse visual concepts which can help to learn geo-diverse knowledge: 1) concepts under similar categories have unique knowledge and visual characteristics, 2) concepts with similar visual features may fall in completely different categories. Motivated by the attributes, we design new pre-training objectives Image-Knowledge Matching (IKM) and Image Edit Checking (IEC) to pre-train GIVL. Compared with similar-size models pre-trained with similar scale of data, GIVL achieves state-of-the-art (SOTA) and more balanced performance on geo-diverse V&L tasks. Code and data are released at https://github.com/WadeYin9712/GIVL. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yin_GIVL_Improving_Geographical_Inclusivity_of_Vision-Language_Models_With_Pre-Training_Methods_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yin_GIVL_Improving_Geographical_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.01893 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yin_GIVL_Improving_Geographical_Inclusivity_of_Vision-Language_Models_With_Pre-Training_Methods_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yin_GIVL_Improving_Geographical_Inclusivity_of_Vision-Language_Models_With_Pre-Training_Methods_CVPR_2023_paper.html | CVPR 2023 | null |
Bi3D: Bi-Domain Active Learning for Cross-Domain 3D Object Detection | Jiakang Yuan, Bo Zhang, Xiangchao Yan, Tao Chen, Botian Shi, Yikang Li, Yu Qiao | Unsupervised Domain Adaptation (UDA) technique has been explored in 3D cross-domain tasks recently. Though preliminary progress has been made, the performance gap between the UDA-based 3D model and the supervised one trained with fully annotated target domain is still large. This motivates us to consider selecting partial-yet-important target data and labeling them at a minimum cost, to achieve a good trade-off between high performance and low annotation cost. To this end, we propose a Bi-domain active learning approach, namely Bi3D, to solve the cross-domain 3D object detection task. The Bi3D first develops a domainness-aware source sampling strategy, which identifies target-domain-like samples from the source domain to avoid the model being interfered by irrelevant source data. Then a diversity-based target sampling strategy is developed, which selects the most informative subset of target domain to improve the model adaptability to the target domain using as little annotation budget as possible. Experiments are conducted on typical cross-domain adaptation scenarios including cross-LiDAR-beam, cross-country, and cross-sensor, where Bi3D achieves a promising target-domain detection accuracy (89.63% on KITTI) compared with UDA-based work (84.29%), even surpassing the detector trained on the full set of the labeled target domain (88.98%). | https://openaccess.thecvf.com/content/CVPR2023/papers/Yuan_Bi3D_Bi-Domain_Active_Learning_for_Cross-Domain_3D_Object_Detection_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.05886 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yuan_Bi3D_Bi-Domain_Active_Learning_for_Cross-Domain_3D_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yuan_Bi3D_Bi-Domain_Active_Learning_for_Cross-Domain_3D_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Fast Adaptation of Pretrained Contrastive Models for Multi-Channel Video-Language Retrieval | Xudong Lin, Simran Tiwari, Shiyuan Huang, Manling Li, Mike Zheng Shou, Heng Ji, Shih-Fu Chang | Multi-channel video-language retrieval require models to understand information from different channels (e.g. video+question, video+speech) to correctly link a video with a textual response or query. Fortunately, contrastive multimodal models are shown to be highly effective at aligning entities in images/videos and text, e.g., CLIP; text contrastive models are extensively studied recently for their strong ability of producing discriminative sentence embeddings, e.g., SimCSE. However, there is not a clear way to quickly adapt these two lines to multi-channel video-language retrieval with limited data and resources. In this paper, we identify a principled model design space with two axes: how to represent videos and how to fuse video and text information. Based on categorization of recent methods, we investigate the options of representing videos using continuous feature vectors or discrete text tokens; for the fusion method, we explore the use of a multimodal transformer or a pretrained contrastive text model. We extensively evaluate the four combinations on five video-language datasets. We surprisingly find that discrete text tokens coupled with a pretrained contrastive text model yields the best performance, which can even outperform state-of-the-art on the iVQA and How2QA datasets without additional training on millions of video-text data. Further analysis shows that this is because representing videos as text tokens captures the key visual information and text tokens are naturally aligned with text models that are strong retrievers after the contrastive pretraining process. All the empirical analysis establishes a solid foundation for future research on affordable and upgradable multimodal intelligence. The code will be released at https://github.com/XudongLinthu/upgradable-multimodal-intelligence to facilitate future research. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Towards_Fast_Adaptation_of_Pretrained_Contrastive_Models_for_Multi-Channel_Video-Language_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lin_Towards_Fast_Adaptation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2206.02082 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Towards_Fast_Adaptation_of_Pretrained_Contrastive_Models_for_Multi-Channel_Video-Language_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Towards_Fast_Adaptation_of_Pretrained_Contrastive_Models_for_Multi-Channel_Video-Language_CVPR_2023_paper.html | CVPR 2023 | null |
Mask-Free OVIS: Open-Vocabulary Instance Segmentation Without Manual Mask Annotations | Vibashan VS, Ning Yu, Chen Xing, Can Qin, Mingfei Gao, Juan Carlos Niebles, Vishal M. Patel, Ran Xu | Existing instance segmentation models learn task-specific information using manual mask annotations from base (training) categories. These mask annotations require tremendous human effort, limiting the scalability to annotate novel (new) categories. To alleviate this problem, Open-Vocabulary (OV) methods leverage large-scale image-caption pairs and vision-language models to learn novel categories. In summary, an OV method learns task-specific information using strong supervision from base annotations and novel category information using weak supervision from image-captions pairs. This difference between strong and weak supervision leads to overfitting on base categories, resulting in poor generalization towards novel categories. In this work, we overcome this issue by learning both base and novel categories from pseudo-mask annotations generated by the vision-language model in a weakly supervised manner using our proposed Mask-free OVIS pipeline. Our method automatically generates pseudo-mask annotations by leveraging the localization ability of a pre-trained vision-language model for objects present in image-caption pairs. The generated pseudo-mask annotations are then used to supervise an instance segmentation model, freeing the entire pipeline from any labour-expensive instance-level annotations and overfitting. Our extensive experiments show that our method trained with just pseudo-masks significantly improves the mAP scores on the MS-COCO dataset and OpenImages dataset compared to the recent state-of-the-art methods trained with manual masks. Codes and models are provided in https://vibashan.github.io/ovis-web/. | https://openaccess.thecvf.com/content/CVPR2023/papers/VS_Mask-Free_OVIS_Open-Vocabulary_Instance_Segmentation_Without_Manual_Mask_Annotations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/VS_Mask-Free_OVIS_Open-Vocabulary_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16891 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/VS_Mask-Free_OVIS_Open-Vocabulary_Instance_Segmentation_Without_Manual_Mask_Annotations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/VS_Mask-Free_OVIS_Open-Vocabulary_Instance_Segmentation_Without_Manual_Mask_Annotations_CVPR_2023_paper.html | CVPR 2023 | null |
Complete-to-Partial 4D Distillation for Self-Supervised Point Cloud Sequence Representation Learning | null | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Complete-to-Partial_4D_Distillation_for_Self-Supervised_Point_Cloud_Sequence_Representation_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Complete-to-Partial_4D_Distillation_for_Self-Supervised_Point_Cloud_Sequence_Representation_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects | Bowen Wen, Jonathan Tremblay, Valts Blukis, Stephen Tyree, Thomas Müller, Alex Evans, Dieter Fox, Jan Kautz, Stan Birchfield | We present a near real-time (10Hz) method for 6-DoF tracking of an unknown object from a monocular RGBD video sequence, while simultaneously performing neural 3D reconstruction of the object. Our method works for arbitrary rigid objects, even when visual texture is largely absent. The object is assumed to be segmented in the first frame only. No additional information is required, and no assumption is made about the interaction agent. Key to our method is a Neural Object Field that is learned concurrently with a pose graph optimization process in order to robustly accumulate information into a consistent 3D representation capturing both geometry and appearance. A dynamic pool of posed memory frames is automatically maintained to facilitate communication between these threads. Our approach handles challenging sequences with large pose changes, partial and full occlusion, untextured surfaces, and specular highlights. We show results on HO3D, YCBInEOAT, and BEHAVE datasets, demonstrating that our method significantly outperforms existing approaches. Project page: https://bundlesdf.github.io/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Wen_BundleSDF_Neural_6-DoF_Tracking_and_3D_Reconstruction_of_Unknown_Objects_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wen_BundleSDF_Neural_6-DoF_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wen_BundleSDF_Neural_6-DoF_Tracking_and_3D_Reconstruction_of_Unknown_Objects_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wen_BundleSDF_Neural_6-DoF_Tracking_and_3D_Reconstruction_of_Unknown_Objects_CVPR_2023_paper.html | CVPR 2023 | null |
Multi-Modal Gait Recognition via Effective Spatial-Temporal Feature Fusion | Yufeng Cui, Yimei Kang | Gait recognition is a biometric technology that identifies people by their walking patterns. The silhouettes-based method and the skeletons-based method are the two most popular approaches. However, the silhouette data are easily affected by clothing occlusion, and the skeleton data lack body shape information. To obtain a more robust and comprehensive gait representation for recognition, we propose a transformer-based gait recognition framework called MMGaitFormer, which effectively fuses and aggregates the spatial-temporal information from the skeletons and silhouettes. Specifically, a Spatial Fusion Module (SFM) and a Temporal Fusion Module (TFM) are proposed for effective spatial-level and temporal-level feature fusion, respectively. The SFM performs fine-grained body parts spatial fusion and guides the alignment of each part of the silhouette and each joint of the skeleton through the attention mechanism. The TFM performs temporal modeling through Cycle Position Embedding (CPE) and fuses temporal information of two modalities. Experiments demonstrate that our MMGaitFormer achieves state-of-the-art performance on popular gait datasets. For the most challenging "CL" (i.e., walking in different clothes) condition in CASIA-B, our method achieves a rank-1 accuracy of 94.8%, which outperforms the state-of-the-art single-modal methods by a large margin. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cui_Multi-Modal_Gait_Recognition_via_Effective_Spatial-Temporal_Feature_Fusion_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cui_Multi-Modal_Gait_Recognition_via_Effective_Spatial-Temporal_Feature_Fusion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cui_Multi-Modal_Gait_Recognition_via_Effective_Spatial-Temporal_Feature_Fusion_CVPR_2023_paper.html | CVPR 2023 | null |
Crowd3D: Towards Hundreds of People Reconstruction From a Single Image | null | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2023/html/Wen_Crowd3D_Towards_Hundreds_of_People_Reconstruction_From_a_Single_Image_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wen_Crowd3D_Towards_Hundreds_of_People_Reconstruction_From_a_Single_Image_CVPR_2023_paper.html | CVPR 2023 | null |
Highly Confident Local Structure Based Consensus Graph Learning for Incomplete Multi-View Clustering | Jie Wen, Chengliang Liu, Gehui Xu, Zhihao Wu, Chao Huang, Lunke Fei, Yong Xu | Graph-based multi-view clustering has attracted extensive attention because of the powerful clustering-structure representation ability and noise robustness. Considering the reality of a large amount of incomplete data, in this paper, we propose a simple but effective method for incomplete multi-view clustering based on consensus graph learning, termed as HCLS_CGL. Unlike existing methods that utilize graph constructed from raw data to aid in the learning of consistent representation, our method directly learns a consensus graph across views for clustering. Specifically, we design a novel confidence graph and embed it to form a confidence structure driven consensus graph learning model. Our confidence graph is based on an intuitive similar-nearest-neighbor hypothesis, which does not require any additional information and can help the model to obtain a high-quality consensus graph for better clustering. Numerous experiments are performed to confirm the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wen_Highly_Confident_Local_Structure_Based_Consensus_Graph_Learning_for_Incomplete_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wen_Highly_Confident_Local_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wen_Highly_Confident_Local_Structure_Based_Consensus_Graph_Learning_for_Incomplete_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wen_Highly_Confident_Local_Structure_Based_Consensus_Graph_Learning_for_Incomplete_CVPR_2023_paper.html | CVPR 2023 | null |
Humans As Light Bulbs: 3D Human Reconstruction From Thermal Reflection | Ruoshi Liu, Carl Vondrick | The relatively hot temperature of the human body causes people to turn into long-wave infrared light sources. Since this emitted light has a larger wavelength than visible light, many surfaces in typical scenes act as infrared mirrors with strong specular reflections. We exploit the thermal reflections of a person onto objects in order to locate their position and reconstruct their pose, even if they are not visible to a normal camera. We propose an analysis-by-synthesis framework that jointly models the objects, people, and their thermal reflections, which allows us to combine generative models with differentiable rendering of reflections. Quantitative and qualitative experiments show our approach works in highly challenging cases, such as with curved mirrors or when the person is completely unseen by a normal camera. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Humans_As_Light_Bulbs_3D_Human_Reconstruction_From_Thermal_Reflection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Humans_As_Light_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.01652 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Humans_As_Light_Bulbs_3D_Human_Reconstruction_From_Thermal_Reflection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Humans_As_Light_Bulbs_3D_Human_Reconstruction_From_Thermal_Reflection_CVPR_2023_paper.html | CVPR 2023 | null |
Hierarchical Discriminative Learning Improves Visual Representations of Biomedical Microscopy | null | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Hierarchical_Discriminative_Learning_Improves_Visual_Representations_of_Biomedical_Microscopy_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Hierarchical_Discriminative_Learning_Improves_Visual_Representations_of_Biomedical_Microscopy_CVPR_2023_paper.html | CVPR 2023 | null |
ProD: Prompting-To-Disentangle Domain Knowledge for Cross-Domain Few-Shot Image Classification | Tianyi Ma, Yifan Sun, Zongxin Yang, Yi Yang | This paper considers few-shot image classification under the cross-domain scenario, where the train-to-test domain gap compromises classification accuracy. To mitigate the domain gap, we propose a prompting-to-disentangle (ProD) method through a novel exploration with the prompting mechanism. ProD adopts the popular multi-domain training scheme and extracts the backbone feature with a standard Convolutional Neural Network. Based on these two common practices, the key point of ProD is using the prompting mechanism in the transformer to disentangle the domain-general (DG) and domain-specific (DS) knowledge from the backbone feature. Specifically, ProD concatenates a DG and a DS prompt to the backbone feature and feeds them into a lightweight transformer. The DG prompt is learnable and shared by all the training domains, while the DS prompt is generated from the domain-of-interest on the fly. As a result, the transformer outputs DG and DS features in parallel with the two prompts, yielding the disentangling effect. We show that: 1) Simply sharing a single DG prompt for all the training domains already improves generalization towards the novel test domain. 2) The cross-domain generalization can be further reinforced by making the DG prompt neutral towards the training domains. 3) When inference, the DS prompt is generated from the support samples and can capture test domain knowledge through the prompting mechanism. Combining all three benefits, ProD significantly improves cross-domain few-shot classification. For instance, on CUB, ProD improves the 5-way 5-shot accuracy from 73.56% (baseline) to 79.19%, setting a new state of the art. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ma_ProD_Prompting-To-Disentangle_Domain_Knowledge_for_Cross-Domain_Few-Shot_Image_Classification_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ma_ProD_Prompting-To-Disentangle_Domain_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_ProD_Prompting-To-Disentangle_Domain_Knowledge_for_Cross-Domain_Few-Shot_Image_Classification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_ProD_Prompting-To-Disentangle_Domain_Knowledge_for_Cross-Domain_Few-Shot_Image_Classification_CVPR_2023_paper.html | CVPR 2023 | null |
CafeBoost: Causal Feature Boost To Eliminate Task-Induced Bias for Class Incremental Learning | Benliu Qiu, Hongliang Li, Haitao Wen, Heqian Qiu, Lanxiao Wang, Fanman Meng, Qingbo Wu, Lili Pan | Continual learning requires a model to incrementally learn a sequence of tasks and aims to predict well on all the learned tasks so far, which notoriously suffers from the catastrophic forgetting problem. In this paper, we find a new type of bias appearing in continual learning, coined as task-induced bias. We place continual learning into a causal framework, based on which we find the task-induced bias is reduced naturally by two underlying mechanisms in task and domain incremental learning. However, these mechanisms do not exist in class incremental learning (CIL), in which each task contains a unique subset of classes. To eliminate the task-induced bias in CIL, we devise a causal intervention operation so as to cut off the causal path that causes the task-induced bias, and then implement it as a causal debias module that transforms biased features into unbiased ones. In addition, we propose a training pipeline to incorporate the novel module into existing methods and jointly optimize the entire architecture. Our overall approach does not rely on data replay, and is simple and convenient to plug into existing methods. Extensive empirical study on CIFAR-100 and ImageNet shows that our approach can improve accuracy and reduce forgetting of well-established methods by a large margin. | https://openaccess.thecvf.com/content/CVPR2023/papers/Qiu_CafeBoost_Causal_Feature_Boost_To_Eliminate_Task-Induced_Bias_for_Class_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qiu_CafeBoost_Causal_Feature_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qiu_CafeBoost_Causal_Feature_Boost_To_Eliminate_Task-Induced_Bias_for_Class_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qiu_CafeBoost_Causal_Feature_Boost_To_Eliminate_Task-Induced_Bias_for_Class_CVPR_2023_paper.html | CVPR 2023 | null |
A-La-Carte Prompt Tuning (APT): Combining Distinct Data via Composable Prompting | Benjamin Bowman, Alessandro Achille, Luca Zancato, Matthew Trager, Pramuditha Perera, Giovanni Paolini, Stefano Soatto | We introduce A-la-carte Prompt Tuning (APT), a transformer-based scheme to tune prompts on distinct data so that they can be arbitrarily composed at inference time. The individual prompts can be trained in isolation, possibly on different devices, at different times, and on different distributions or domains. Furthermore each prompt only contains information about the subset of data it was exposed to during training. During inference, models can be assembled based on arbitrary selections of data sources, which we call a-la-carte learning. A-la-carte learning enables constructing bespoke models specific to each user's individual access rights and preferences. We can add or remove information from the model by simply adding or removing the corresponding prompts without retraining from scratch. We demonstrate that a-la-carte built models achieve accuracy within 5% of models trained on the union of the respective sources, with comparable cost in terms of training and inference time. For the continual learning benchmarks Split CIFAR-100 and CORe50, we achieve state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bowman_A-La-Carte_Prompt_Tuning_APT_Combining_Distinct_Data_via_Composable_Prompting_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bowman_A-La-Carte_Prompt_Tuning_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bowman_A-La-Carte_Prompt_Tuning_APT_Combining_Distinct_Data_via_Composable_Prompting_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bowman_A-La-Carte_Prompt_Tuning_APT_Combining_Distinct_Data_via_Composable_Prompting_CVPR_2023_paper.html | CVPR 2023 | null |
ImageNet-E: Benchmarking Neural Network Robustness via Attribute Editing | Xiaodan Li, Yuefeng Chen, Yao Zhu, Shuhui Wang, Rong Zhang, Hui Xue | Recent studies have shown that higher accuracy on ImageNet usually leads to better robustness against different corruptions. In this paper, instead of following the traditional research paradigm that investigates new out-of-distribution corruptions or perturbations deep models may encounter, we conduct model debugging in in-distribution data to explore which object attributes a model may be sensitive to. To achieve this goal, we create a toolkit for object editing with controls of backgrounds, sizes, positions, and directions, and create a rigorous benchmark named ImageNet-E(diting) for evaluating the image classifier robustness in terms of object attributes. With our ImageNet-E, we evaluate the performance of current deep learning models, including both convolutional neural networks and vision transformers. We find that most models are quite sensitive to attribute changes. An imperceptible change in the background can lead to an average of 9.23% drop on top-1 accuracy. We also evaluate some robust models including both adversarially trained models and other robust trained models and find that some models show worse robustness against attribute changes than vanilla models. Based on these findings, we discover ways to enhance attribute robustness with preprocessing, architecture designs, and training strategies. We hope this work can provide some insights to the community and open up a new avenue for research in robust computer vision. The code and dataset will be publicly available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_ImageNet-E_Benchmarking_Neural_Network_Robustness_via_Attribute_Editing_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_ImageNet-E_Benchmarking_Neural_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_ImageNet-E_Benchmarking_Neural_Network_Robustness_via_Attribute_Editing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_ImageNet-E_Benchmarking_Neural_Network_Robustness_via_Attribute_Editing_CVPR_2023_paper.html | CVPR 2023 | null |
Learning With Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning | Zeyin Song, Yifan Zhao, Yujun Shi, Peixi Peng, Li Yuan, Yonghong Tian | Few-shot class-incremental learning (FSCIL) aims at learning to classify new classes continually from limited samples without forgetting the old classes. The mainstream framework tackling FSCIL is first to adopt the cross-entropy (CE) loss for training at the base session, then freeze the feature extractor to adapt to new classes. However, in this work, we find that the CE loss is not ideal for the base session training as it suffers poor class separation in terms of representations, which further degrades generalization to novel classes. One tempting method to mitigate this problem is to apply an additional naive supervised contrastive learning (SCL) in the base session. Unfortunately, we find that although SCL can create a slightly better representation separation among different base classes, it still struggles to separate base classes and new classes. Inspired by the observations made, we propose Semantic-Aware Virtual Contrastive model (SAVC), a novel method that facilitates separation between new classes and base classes by introducing virtual classes to SCL. These virtual classes, which are generated via pre-defined transformations, not only act as placeholders for unseen classes in the representation space but also provide diverse semantic information. By learning to recognize and contrast in the fantasy space fostered by virtual classes, our SAVC significantly boosts base class separation and novel class generalization, achieving new state-of-the-art performance on the three widely-used FSCIL benchmark datasets. Code is available at: https://github.com/zysong0113/SAVC. | https://openaccess.thecvf.com/content/CVPR2023/papers/Song_Learning_With_Fantasy_Semantic-Aware_Virtual_Contrastive_Constraint_for_Few-Shot_Class-Incremental_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Song_Learning_With_Fantasy_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00426 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Song_Learning_With_Fantasy_Semantic-Aware_Virtual_Contrastive_Constraint_for_Few-Shot_Class-Incremental_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Song_Learning_With_Fantasy_Semantic-Aware_Virtual_Contrastive_Constraint_for_Few-Shot_Class-Incremental_CVPR_2023_paper.html | CVPR 2023 | null |
ViLEM: Visual-Language Error Modeling for Image-Text Retrieval | Yuxin Chen, Zongyang Ma, Ziqi Zhang, Zhongang Qi, Chunfeng Yuan, Ying Shan, Bing Li, Weiming Hu, Xiaohu Qie, Jianping Wu | Dominant pre-training works for image-text retrieval adopt "dual-encoder" architecture to enable high efficiency, where two encoders are used to extract image and text representations and contrastive learning is employed for global alignment. However, coarse-grained global alignment ignores detailed semantic associations between image and text. In this work, we propose a novel proxy task, named Visual-Language Error Modeling (ViLEM), to inject detailed image-text association into "dual-encoder" model by "proofreading" each word in the text against the corresponding image. Specifically, we first edit the image-paired text to automatically generate diverse plausible negative texts with pre-trained language models. ViLEM then enforces the model to discriminate the correctness of each word in the plausible negative texts and further correct the wrong words via resorting to image information. Furthermore, we propose a multi-granularity interaction framework to perform ViLEM via interacting text features with both global and local image features, which associates local text semantics with both high-level visual context and multi-level local visual information. Our method surpasses state-of-the-art "dual-encoder" methods by a large margin on the image-text retrieval task and significantly improves discriminativeness to local textual semantics. Our model can also generalize well to video-text retrieval. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_ViLEM_Visual-Language_Error_Modeling_for_Image-Text_Retrieval_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_ViLEM_Visual-Language_Error_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_ViLEM_Visual-Language_Error_Modeling_for_Image-Text_Retrieval_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_ViLEM_Visual-Language_Error_Modeling_for_Image-Text_Retrieval_CVPR_2023_paper.html | CVPR 2023 | null |
Egocentric Auditory Attention Localization in Conversations | Fiona Ryan, Hao Jiang, Abhinav Shukla, James M. Rehg, Vamsi Krishna Ithapu | In a noisy conversation environment such as a dinner party, people often exhibit selective auditory attention, or the ability to focus on a particular speaker while tuning out others. Recognizing who somebody is listening to in a conversation is essential for developing technologies that can understand social behavior and devices that can augment human hearing by amplifying particular sound sources. The computer vision and audio research communities have made great strides towards recognizing sound sources and speakers in scenes. In this work, we take a step further by focusing on the problem of localizing auditory attention targets in egocentric video, or detecting who in a camera wearer's field of view they are listening to. To tackle the new and challenging Selective Auditory Attention Localization problem, we propose an end-to-end deep learning approach that uses egocentric video and multichannel audio to predict the heatmap of the camera wearer's auditory attention. Our approach leverages spatiotemporal audiovisual features and holistic reasoning about the scene to make predictions, and outperforms a set of baselines on a challenging multi-speaker conversation dataset. Project page: https://fkryan.github.io/saal | https://openaccess.thecvf.com/content/CVPR2023/papers/Ryan_Egocentric_Auditory_Attention_Localization_in_Conversations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ryan_Egocentric_Auditory_Attention_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.16024 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ryan_Egocentric_Auditory_Attention_Localization_in_Conversations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ryan_Egocentric_Auditory_Attention_Localization_in_Conversations_CVPR_2023_paper.html | CVPR 2023 | null |
Texture-Guided Saliency Distilling for Unsupervised Salient Object Detection | Huajun Zhou, Bo Qiao, Lingxiao Yang, Jianhuang Lai, Xiaohua Xie | Deep Learning-based Unsupervised Salient Object Detection (USOD) mainly relies on the noisy saliency pseudo labels that have been generated from traditional handcraft methods or pre-trained networks. To cope with the noisy labels problem, a class of methods focus on only easy samples with reliable labels but ignore valuable knowledge in hard samples. In this paper, we propose a novel USOD method to mine rich and accurate saliency knowledge from both easy and hard samples. First, we propose a Confidence-aware Saliency Distilling (CSD) strategy that scores samples conditioned on samples' confidences, which guides the model to distill saliency knowledge from easy samples to hard samples progressively. Second, we propose a Boundary-aware Texture Matching (BTM) strategy to refine the boundaries of noisy labels by matching the textures around the predicted boundaries. Extensive experiments on RGB, RGB-D, RGB-T, and video SOD benchmarks prove that our method achieves state-of-the-art USOD performance. Code is available at www.github.com/moothes/A2S-v2. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Texture-Guided_Saliency_Distilling_for_Unsupervised_Salient_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_Texture-Guided_Saliency_Distilling_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2207.05921 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Texture-Guided_Saliency_Distilling_for_Unsupervised_Salient_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Texture-Guided_Saliency_Distilling_for_Unsupervised_Salient_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
AltFreezing for More General Video Face Forgery Detection | Zhendong Wang, Jianmin Bao, Wengang Zhou, Weilun Wang, Houqiang Li | Existing face forgery detection models try to discriminate fake images by detecting only spatial artifacts (e.g., generative artifacts, blending) or mainly temporal artifacts (e.g., flickering, discontinuity). They may experience significant performance degradation when facing out-domain artifacts. In this paper, we propose to capture both spatial and temporal artifacts in one model for face forgery detection. A simple idea is to leverage a spatiotemporal model (3D ConvNet). However, we find that it may easily rely on one type of artifact and ignore the other. To address this issue, we present a novel training strategy called AltFreezing for more general face forgery detection. The AltFreezing aims to encourage the model to detect both spatial and temporal artifacts. It divides the weights of a spatiotemporal network into two groups: spatial- and temporal-related. Then the two groups of weights are alternately frozen during the training process so that the model can learn spatial and temporal features to distinguish real or fake videos. Furthermore, we introduce various video-level data augmentation methods to improve the generalization capability of the forgery detection model. Extensive experiments show that our framework outperforms existing methods in terms of generalization to unseen manipulations and datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_AltFreezing_for_More_General_Video_Face_Forgery_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_AltFreezing_for_More_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_AltFreezing_for_More_General_Video_Face_Forgery_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_AltFreezing_for_More_General_Video_Face_Forgery_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Cascaded Local Implicit Transformer for Arbitrary-Scale Super-Resolution | Hao-Wei Chen, Yu-Syuan Xu, Min-Fong Hong, Yi-Min Tsai, Hsien-Kai Kuo, Chun-Yi Lee | Implicit neural representation demonstrates promising ability in representing images with arbitrary resolutions recently. In this paper, we present Local Implicit Transformer (LIT) that integrates attention mechanism and frequency encoding technique into local implicit image function. We design a cross-scale local attention block to effectively aggregate local features and a local frequency encoding block to combine positional encoding with Fourier domain information for constructing high-resolution (HR) images. To further improve representative power, we propose Cascaded LIT (CLIT) exploiting multi-scale features along with cumulative training strategy that gradually increase the upsampling factors for training. We have performed extensive experiments to validate the effectiveness of these components and analyze the variants of the training strategy. The qualitative and quantitative results demonstrated that LIT and CLIT achieve favorable results and outperform the previous works within arbitrary super-resolution tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Cascaded_Local_Implicit_Transformer_for_Arbitrary-Scale_Super-Resolution_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.16513 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Cascaded_Local_Implicit_Transformer_for_Arbitrary-Scale_Super-Resolution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Cascaded_Local_Implicit_Transformer_for_Arbitrary-Scale_Super-Resolution_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Partial Correlation Based Deep Visual Representation for Image Classification | Saimunur Rahman, Piotr Koniusz, Lei Wang, Luping Zhou, Peyman Moghadam, Changming Sun | Visual representation based on covariance matrix has demonstrates its efficacy for image classification by characterising the pairwise correlation of different channels in convolutional feature maps. However, pairwise correlation will become misleading once there is another channel correlating with both channels of interest, resulting in the "confounding" effect. For this case, "partial correlation" which removes the confounding effect shall be estimated instead. Nevertheless, reliably estimating partial correlation requires to solve a symmetric positive definite matrix optimisation, known as sparse inverse covariance estimation (SICE). How to incorporate this process into CNN remains an open issue. In this work, we formulate SICE as a novel structured layer of CNN. To ensure end-to-end trainability, we develop an iterative method to solve the above matrix optimisation during forward and backward propagation steps. Our work obtains a partial correlation based deep visual representation and mitigates the small sample problem often encountered by covariance matrix estimation in CNN. Computationally, our model can be effectively trained with GPU and works well with a large number of channels of advanced CNNs. Experiments show the efficacy and superior classification performance of our deep visual representation compared to covariance matrix based counterparts. | https://openaccess.thecvf.com/content/CVPR2023/papers/Rahman_Learning_Partial_Correlation_Based_Deep_Visual_Representation_for_Image_Classification_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Rahman_Learning_Partial_Correlation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.11597 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Rahman_Learning_Partial_Correlation_Based_Deep_Visual_Representation_for_Image_Classification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Rahman_Learning_Partial_Correlation_Based_Deep_Visual_Representation_for_Image_Classification_CVPR_2023_paper.html | CVPR 2023 | null |
Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon Prediction | Shaofei Cai, Zihao Wang, Xiaojian Ma, Anji Liu, Yitao Liang | We study the problem of learning goal-conditioned policies in Minecraft, a popular, widely accessible yet challenging open-ended environment for developing human-level multi-task agents. We first identify two main challenges of learning such policies: 1) the indistinguishability of tasks from the state distribution, due to the vast scene diversity, and 2) the non-stationary nature of environment dynamics caused by the partial observability. To tackle the first challenge, we propose Goal-Sensitive Backbone (GSB) for the policy to encourage the emergence of goal-relevant visual state representations. To tackle the second challenge, the policy is further fueled by an adaptive horizon prediction module that helps alleviate the learning uncertainty brought by the non-stationary dynamics. Experiments on 20 Minecraft tasks show that our method significantly outperforms the best baseline so far; in many of them, we double the performance. Our ablation and exploratory studies then explain how our approach beat the counterparts and also unveil the surprising bonus of zero-shot generalization to new scenes (biomes). We hope our agent could help shed some light on learning goal-conditioned, multi-task agents in challenging, open-ended environments like Minecraft. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cai_Open-World_Multi-Task_Control_Through_Goal-Aware_Representation_Learning_and_Adaptive_Horizon_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cai_Open-World_Multi-Task_Control_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.10034 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cai_Open-World_Multi-Task_Control_Through_Goal-Aware_Representation_Learning_and_Adaptive_Horizon_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cai_Open-World_Multi-Task_Control_Through_Goal-Aware_Representation_Learning_and_Adaptive_Horizon_CVPR_2023_paper.html | CVPR 2023 | null |
MoDi: Unconditional Motion Synthesis From Diverse Data | Sigal Raab, Inbal Leibovitch, Peizhuo Li, Kfir Aberman, Olga Sorkine-Hornung, Daniel Cohen-Or | The emergence of neural networks has revolutionized the field of motion synthesis. Yet, learning to unconditionally synthesize motions from a given distribution remains challenging, especially when the motions are highly diverse. In this work, we present MoDi -- a generative model trained in an unsupervised setting from an extremely diverse, unstructured and unlabeled dataset. During inference, MoDi can synthesize high-quality, diverse motions. Despite the lack of any structure in the dataset, our model yields a well-behaved and highly structured latent space, which can be semantically clustered, constituting a strong motion prior that facilitates various applications including semantic editing and crowd animation. In addition, we present an encoder that inverts real motions into MoDi's natural motion manifold, issuing solutions to various ill-posed challenges such as completion from prefix and spatial editing. Our qualitative and quantitative experiments achieve state-of-the-art results that outperform recent SOTA techniques. Code and trained models are available at https://sigal-raab.github.io/MoDi. | https://openaccess.thecvf.com/content/CVPR2023/papers/Raab_MoDi_Unconditional_Motion_Synthesis_From_Diverse_Data_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Raab_MoDi_Unconditional_Motion_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2206.08010 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Raab_MoDi_Unconditional_Motion_Synthesis_From_Diverse_Data_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Raab_MoDi_Unconditional_Motion_Synthesis_From_Diverse_Data_CVPR_2023_paper.html | CVPR 2023 | null |
Visual Localization Using Imperfect 3D Models From the Internet | Vojtech Panek, Zuzana Kukelova, Torsten Sattler | Visual localization is a core component in many applications, including augmented reality (AR). Localization algorithms compute the camera pose of a query image w.r.t. a scene representation, which is typically built from images. This often requires capturing and storing large amounts of data, followed by running Structure-from-Motion (SfM) algorithms. An interesting, and underexplored, source of data for building scene representations are 3D models that are readily available on the Internet, e.g., hand-drawn CAD models, 3D models generated from building footprints, or from aerial images. These models allow to perform visual localization right away without the time-consuming scene capturing and model building steps. Yet, it also comes with challenges as the available 3D models are often imperfect reflections of reality. E.g., the models might only have generic or no textures at all, might only provide a simple approximation of the scene geometry, or might be stretched. This paper studies how the imperfections of these models affect localization accuracy. We create a new benchmark for this task and provide a detailed experimental evaluation based on multiple 3D models per scene. We show that 3D models from the Internet show promise as an easy-to-obtain scene representation. At the same time, there is significant room for improvement for visual localization pipelines. To foster research on this interesting and challenging task, we release our benchmark at v-pnk.github.io/cadloc. | https://openaccess.thecvf.com/content/CVPR2023/papers/Panek_Visual_Localization_Using_Imperfect_3D_Models_From_the_Internet_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Panek_Visual_Localization_Using_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.05947 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Panek_Visual_Localization_Using_Imperfect_3D_Models_From_the_Internet_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Panek_Visual_Localization_Using_Imperfect_3D_Models_From_the_Internet_CVPR_2023_paper.html | CVPR 2023 | null |
Network-Free, Unsupervised Semantic Segmentation With Synthetic Images | Qianli Feng, Raghudeep Gadde, Wentong Liao, Eduard Ramon, Aleix Martinez | We derive a method that yields highly accurate semantic segmentation maps without the use of any additional neural network, layers, manually annotated training data, or supervised training. Our method is based on the observation that the correlation of a set of pixels belonging to the same semantic segment do not change when generating synthetic variants of an image using the style mixing approach in GANs. We show how we can use GAN inversion to accurately semantically segment synthetic and real photos as well as generate large training image-semantic segmentation mask pairs for downstream tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Feng_Network-Free_Unsupervised_Semantic_Segmentation_With_Synthetic_Images_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Feng_Network-Free_Unsupervised_Semantic_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Network-Free_Unsupervised_Semantic_Segmentation_With_Synthetic_Images_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Network-Free_Unsupervised_Semantic_Segmentation_With_Synthetic_Images_CVPR_2023_paper.html | CVPR 2023 | null |
Hierarchical Dense Correlation Distillation for Few-Shot Segmentation | Bohao Peng, Zhuotao Tian, Xiaoyang Wu, Chengyao Wang, Shu Liu, Jingyong Su, Jiaya Jia | Few-shot semantic segmentation (FSS) aims to form class-agnostic models segmenting unseen classes with only a handful of annotations. Previous methods limited to the semantic feature and prototype representation suffer from coarse segmentation granularity and train-set overfitting. In this work, we design Hierarchically Decoupled Matching Network (HDMNet) mining pixel-level support correlation based on the transformer architecture. The self-attention modules are used to assist in establishing hierarchical dense features, as a means to accomplish the cascade matching between query and support features. Moreover, we propose a matching module to reduce train-set overfitting and introduce correlation distillation leveraging semantic correspondence from coarse resolution to boost fine-grained segmentation. Our method performs decently in experiments. We achieve 50.0% mIoU on COCO-5i dataset one-shot setting and 56.0% on five-shot segmentation, respectively. The code is available on the project website. | https://openaccess.thecvf.com/content/CVPR2023/papers/Peng_Hierarchical_Dense_Correlation_Distillation_for_Few-Shot_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Peng_Hierarchical_Dense_Correlation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14652 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Peng_Hierarchical_Dense_Correlation_Distillation_for_Few-Shot_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Peng_Hierarchical_Dense_Correlation_Distillation_for_Few-Shot_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
PVO: Panoptic Visual Odometry | Weicai Ye, Xinyue Lan, Shuo Chen, Yuhang Ming, Xingyuan Yu, Hujun Bao, Zhaopeng Cui, Guofeng Zhang | We present PVO, a novel panoptic visual odometry framework to achieve more comprehensive modeling of the scene motion, geometry, and panoptic segmentation information. Our PVO models visual odometry (VO) and video panoptic segmentation (VPS) in a unified view, which makes the two tasks mutually beneficial. Specifically, we introduce a panoptic update module into the VO Module with the guidance of image panoptic segmentation. This Panoptic-Enhanced VO Module can alleviate the impact of dynamic objects in the camera pose estimation with a panoptic-aware dynamic mask. On the other hand, the VO-Enhanced VPS Module also improves the segmentation accuracy by fusing the panoptic segmentation result of the current frame on the fly to the adjacent frames, using geometric information such as camera pose, depth, and optical flow obtained from the VO Module. These two modules contribute to each other through recurrent iterative optimization. Extensive experiments demonstrate that PVO outperforms state-of-the-art methods in both visual odometry and video panoptic segmentation tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ye_PVO_Panoptic_Visual_Odometry_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ye_PVO_Panoptic_Visual_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2207.01610 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ye_PVO_Panoptic_Visual_Odometry_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ye_PVO_Panoptic_Visual_Odometry_CVPR_2023_paper.html | CVPR 2023 | null |
Generative Diffusion Prior for Unified Image Restoration and Enhancement | Ben Fei, Zhaoyang Lyu, Liang Pan, Junzhe Zhang, Weidong Yang, Tianyue Luo, Bo Zhang, Bo Dai | Existing image restoration methods mostly leverage the posterior distribution of natural images. However, they often assume known degradation and also require supervised training, which restricts their adaptation to complex real applications. In this work, we propose the Generative Diffusion Prior (GDP) to effectively model the posterior distributions in an unsupervised sampling manner. GDP utilizes a pre-train denoising diffusion generative model (DDPM) for solving linear inverse, non-linear, or blind problems. Specifically, GDP systematically explores a protocol of conditional guidance, which is verified more practical than the commonly used guidance way. Furthermore, GDP is strength at optimizing the parameters of degradation model during denoising process, achieving blind image restoration. Besides, we devise hierarchical guidance and patch-based methods, enabling the GDP to generate images of arbitrary resolutions. Experimentally, we demonstrate GDP's versatility on several image datasets for linear problems, such as super-resolution, deblurring, inpainting, and colorization, as well as non-linear and blind issues, such as low-light enhancement and HDR image recovery. GDP outperforms the current leading unsupervised methods on the diverse benchmarks in reconstruction quality and perceptual quality. Moreover, GDP also generalizes well for natural images or synthesized images with arbitrary sizes from various tasks out of the distribution of the ImageNet training set. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fei_Generative_Diffusion_Prior_for_Unified_Image_Restoration_and_Enhancement_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fei_Generative_Diffusion_Prior_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01247 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fei_Generative_Diffusion_Prior_for_Unified_Image_Restoration_and_Enhancement_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fei_Generative_Diffusion_Prior_for_Unified_Image_Restoration_and_Enhancement_CVPR_2023_paper.html | CVPR 2023 | null |
Real-Time Controllable Denoising for Image and Video | Zhaoyang Zhang, Yitong Jiang, Wenqi Shao, Xiaogang Wang, Ping Luo, Kaimo Lin, Jinwei Gu | Controllable image denoising aims to generate clean samples with human perceptual priors and balance sharpness and smoothness. In traditional filter-based denoising methods, this can be easily achieved by adjusting the filtering strength. However, for NN (Neural Network)-based models, adjusting the final denoising strength requires performing network inference each time, making it almost impossible for real-time user interaction. In this paper, we introduce Real-time Controllable Denoising (RCD), the first deep image and video denoising pipeline that provides a fully controllable user interface to edit arbitrary denoising levels in real-time with only one-time network inference. Unlike existing controllable denoising methods that require multiple denoisers and training stages, RCD replaces the last output layer (which usually outputs a single noise map) of an existing CNN-based model with a lightweight module that outputs multiple noise maps. We propose a novel Noise Decorrelation process to enforce the orthogonality of the noise feature maps, allowing arbitrary noise level control through noise map interpolation. This process is network-free and does not require network inference. Our experiments show that RCD can enable real-time editable image and video denoising for various existing heavy-weight models without sacrificing their original performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Real-Time_Controllable_Denoising_for_Image_and_Video_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Real-Time_Controllable_Denoising_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.16425 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Real-Time_Controllable_Denoising_for_Image_and_Video_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Real-Time_Controllable_Denoising_for_Image_and_Video_CVPR_2023_paper.html | CVPR 2023 | null |
ISBNet: A 3D Point Cloud Instance Segmentation Network With Instance-Aware Sampling and Box-Aware Dynamic Convolution | Tuan Duc Ngo, Binh-Son Hua, Khoi Nguyen | Existing 3D instance segmentation methods are predominated by the bottom-up design -- manually fine-tuned algorithm to group points into clusters followed by a refinement network. However, by relying on the quality of the clusters, these methods generate susceptible results when (1) nearby objects with the same semantic class are packed together, or (2) large objects with loosely connected regions. To address these limitations, we introduce ISBNet, a novel cluster-free method that represents instances as kernels and decodes instance masks via dynamic convolution. To efficiently generate high-recall and discriminative kernels, we propose a simple strategy named Instance-aware Farthest Point Sampling to sample candidates and leverage the local aggregation layer inspired by PointNet++ to encode candidate features. Moreover, we show that predicting and leveraging the 3D axis-aligned bounding boxes in the dynamic convolution further boosts performance. Our method set new state-of-the-art results on ScanNetV2 (55.9), S3DIS (60.8), and STPLS3D (49.2) in terms of AP and retains fast inference time (237ms per scene on ScanNetV2). The source code and trained models are available at https://github.com/VinAIResearch/ISBNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ngo_ISBNet_A_3D_Point_Cloud_Instance_Segmentation_Network_With_Instance-Aware_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ngo_ISBNet_A_3D_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.00246 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ngo_ISBNet_A_3D_Point_Cloud_Instance_Segmentation_Network_With_Instance-Aware_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ngo_ISBNet_A_3D_Point_Cloud_Instance_Segmentation_Network_With_Instance-Aware_CVPR_2023_paper.html | CVPR 2023 | null |
Hi4D: 4D Instance Segmentation of Close Human Interaction | Yifei Yin, Chen Guo, Manuel Kaufmann, Juan Jose Zarate, Jie Song, Otmar Hilliges | We propose Hi4D, a method and dataset for the auto analysis of physically close human-human interaction under prolonged contact. Robustly disentangling several in-contact subjects is a challenging task due to occlusions and complex shapes. Hence, existing multi-view systems typically fuse 3D surfaces of close subjects into a single, connected mesh. To address this issue we leverage i) individually fitted neural implicit avatars; ii) an alternating optimization scheme that refines pose and surface through periods of close proximity; and iii) thus segment the fused raw scans into individual instances. From these instances we compile Hi4D dataset of 4D textured scans of 20 subject pairs, 100 sequences, and a total of more than 11K frames. Hi4D contains rich interaction-centric annotations in 2D and 3D alongside accurately registered parametric body models. We define varied human pose and shape estimation tasks on this dataset and provide results from state-of-the-art methods on these benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yin_Hi4D_4D_Instance_Segmentation_of_Close_Human_Interaction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yin_Hi4D_4D_Instance_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15380 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yin_Hi4D_4D_Instance_Segmentation_of_Close_Human_Interaction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yin_Hi4D_4D_Instance_Segmentation_of_Close_Human_Interaction_CVPR_2023_paper.html | CVPR 2023 | null |
Hi-LASSIE: High-Fidelity Articulated Shape and Skeleton Discovery From Sparse Image Ensemble | Chun-Han Yao, Wei-Chih Hung, Yuanzhen Li, Michael Rubinstein, Ming-Hsuan Yang, Varun Jampani | Automatically estimating 3D skeleton, shape, camera viewpoints, and part articulation from sparse in-the-wild image ensembles is a severely under-constrained and challenging problem. Most prior methods rely on large-scale image datasets, dense temporal correspondence, or human annotations like camera pose, 2D keypoints, and shape templates. We propose Hi-LASSIE, which performs 3D articulated reconstruction from only 20-30 online images in the wild without any user-defined shape or skeleton templates. We follow the recent work of LASSIE that tackles a similar problem setting and make two significant advances. First, instead of relying on a manually annotated 3D skeleton, we automatically estimate a class-specific skeleton from the selected reference image. Second, we improve the shape reconstructions with novel instance-specific optimization strategies that allow reconstructions to faithful fit on each instance while preserving the class-specific priors learned across all images. Experiments on in-the-wild image ensembles show that Hi-LASSIE obtains higher fidelity state-of-the-art 3D reconstructions despite requiring minimum user input. Project page: chhankyao.github.io/hi-lassie/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Yao_Hi-LASSIE_High-Fidelity_Articulated_Shape_and_Skeleton_Discovery_From_Sparse_Image_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yao_Hi-LASSIE_High-Fidelity_Articulated_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yao_Hi-LASSIE_High-Fidelity_Articulated_Shape_and_Skeleton_Discovery_From_Sparse_Image_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yao_Hi-LASSIE_High-Fidelity_Articulated_Shape_and_Skeleton_Discovery_From_Sparse_Image_CVPR_2023_paper.html | CVPR 2023 | null |
IterativePFN: True Iterative Point Cloud Filtering | Dasith de Silva Edirimuni, Xuequan Lu, Zhiwen Shao, Gang Li, Antonio Robles-Kelly, Ying He | The quality of point clouds is often limited by noise introduced during their capture process. Consequently, a fundamental 3D vision task is the removal of noise, known as point cloud filtering or denoising. State-of-the-art learning based methods focus on training neural networks to infer filtered displacements and directly shift noisy points onto the underlying clean surfaces. In high noise conditions, they iterate the filtering process. However, this iterative filtering is only done at test time and is less effective at ensuring points converge quickly onto the clean surfaces. We propose IterativePFN (iterative point cloud filtering network), which consists of multiple IterationModules that model the true iterative filtering process internally, within a single network. We train our IterativePFN network using a novel loss function that utilizes an adaptive ground truth target at each iteration to capture the relationship between intermediate filtering results during training. This ensures that the filtered results converge faster to the clean surfaces. Our method is able to obtain better performance compared to state-of-the-art methods. The source code can be found at: https://github.com/ddsediri/IterativePFN. | https://openaccess.thecvf.com/content/CVPR2023/papers/de_Silva_Edirimuni_IterativePFN_True_Iterative_Point_Cloud_Filtering_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/de_Silva_Edirimuni_IterativePFN_True_Iterative_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01529 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/de_Silva_Edirimuni_IterativePFN_True_Iterative_Point_Cloud_Filtering_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/de_Silva_Edirimuni_IterativePFN_True_Iterative_Point_Cloud_Filtering_CVPR_2023_paper.html | CVPR 2023 | null |
Computationally Budgeted Continual Learning: What Does Matter? | Ameya Prabhu, Hasan Abed Al Kader Hammoud, Puneet K. Dokania, Philip H.S. Torr, Ser-Nam Lim, Bernard Ghanem, Adel Bibi | Continual Learning (CL) aims to sequentially train models on streams of incoming data that vary in distribution by preserving previous knowledge while adapting to new data. Current CL literature focuses on restricted access to previously seen data, while imposing no constraints on the computational budget for training. This is unreasonable for applications in-the-wild, where systems are primarily constrained by computational and time budgets, not storage. We revisit this problem with a large-scale benchmark and analyze the performance of traditional CL approaches in a compute-constrained setting, where effective memory samples used in training can be implicitly restricted as a consequence of limited computation. We conduct experiments evaluating various CL sampling strategies, distillation losses, and partial fine-tuning on two large-scale datasets, namely ImageNet2K and Continual Google Landmarks V2 in data incremental, class incremental, and time incremental settings. Through extensive experiments amounting to a total of over 1500 GPU-hours, we find that, under compute-constrained setting, traditional CL approaches, with no exception, fail to outperform a simple minimal baseline that samples uniformly from memory. Our conclusions are consistent in a different number of stream time steps, e.g., 20 to 200, and under several computational budgets. This suggests that most existing CL methods are particularly too computationally expensive for realistic budgeted deployment. Code for this project is available at: https://github.com/drimpossible/BudgetCL. | https://openaccess.thecvf.com/content/CVPR2023/papers/Prabhu_Computationally_Budgeted_Continual_Learning_What_Does_Matter_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Prabhu_Computationally_Budgeted_Continual_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11165 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Prabhu_Computationally_Budgeted_Continual_Learning_What_Does_Matter_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Prabhu_Computationally_Budgeted_Continual_Learning_What_Does_Matter_CVPR_2023_paper.html | CVPR 2023 | null |
Decentralized Learning With Multi-Headed Distillation | Andrey Zhmoginov, Mark Sandler, Nolan Miller, Gus Kristiansen, Max Vladymyrov | Decentralized learning with private data is a central problem in machine learning. We propose a novel distillation-based decentralized learning technique that allows multiple agents with private non-iid data to learn from each other, without having to share their data, weights or weight updates. Our approach is communication efficient, utilizes an unlabeled public dataset and uses multiple auxiliary heads for each client, greatly improving training efficiency in the case of heterogeneous data. This approach allows individual models to preserve and enhance performance on their private tasks while also dramatically improving their performance on the global aggregated data distribution. We study the effects of data and model architecture heterogeneity and the impact of the underlying communication graph topology on learning efficiency and show that our agents can significantly improve their performance compared to learning in isolation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhmoginov_Decentralized_Learning_With_Multi-Headed_Distillation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhmoginov_Decentralized_Learning_With_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.15774 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhmoginov_Decentralized_Learning_With_Multi-Headed_Distillation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhmoginov_Decentralized_Learning_With_Multi-Headed_Distillation_CVPR_2023_paper.html | CVPR 2023 | null |
SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection | Tiange Xiang, Yixiao Zhang, Yongyi Lu, Alan L. Yuille, Chaoyi Zhang, Weidong Cai, Zongwei Zhou | Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients. To exploit this structured information, we propose the use of Space-aware Memory Queues for In-painting and Detecting anomalies from radiography images (abbreviated as SQUID). We show that SQUID can taxonomize the ingrained anatomical structures into recurrent patterns; and in the inference, it can identify anomalies (unseen/modified patterns) in the image. SQUID surpasses 13 state-of-the-art methods in unsupervised anomaly detection by at least 5 points on two chest X-ray benchmark datasets measured by the Area Under the Curve (AUC). Additionally, we have created a new dataset (DigitAnatomy), which synthesizes the spatial correlation and consistent shape in chest anatomy. We hope DigitAnatomy can prompt the development, evaluation, and interpretability of anomaly detection methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xiang_SQUID_Deep_Feature_In-Painting_for_Unsupervised_Anomaly_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xiang_SQUID_Deep_Feature_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2111.13495 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xiang_SQUID_Deep_Feature_In-Painting_for_Unsupervised_Anomaly_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xiang_SQUID_Deep_Feature_In-Painting_for_Unsupervised_Anomaly_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
CF-Font: Content Fusion for Few-Shot Font Generation | Chi Wang, Min Zhou, Tiezheng Ge, Yuning Jiang, Hujun Bao, Weiwei Xu | Content and style disentanglement is an effective way to achieve few-shot font generation. It allows to transfer the style of the font image in a source domain to the style defined with a few reference images in a target domain. However, the content feature extracted using a representative font might not be optimal. In light of this, we propose a content fusion module (CFM) to project the content feature into a linear space defined by the content features of basis fonts, which can take the variation of content features caused by different fonts into consideration. Our method also allows to optimize the style representation vector of reference images through a lightweight iterative style-vector refinement (ISR) strategy. Moreover, we treat the 1D projection of a character image as a probability distribution and leverage the distance between two distributions as the reconstruction loss (namely projected character loss, PCL). Compared to L2 or L1 reconstruction loss, the distribution distance pays more attention to the global shape of characters. We have evaluated our method on a dataset of 300 fonts with 6.5k characters each. Experimental results verify that our method outperforms existing state-of-the-art few-shot font generation methods by a large margin. The source code can be found at https://github.com/wangchi95/CF-Font. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_CF-Font_Content_Fusion_for_Few-Shot_Font_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_CF-Font_Content_Fusion_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_CF-Font_Content_Fusion_for_Few-Shot_Font_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_CF-Font_Content_Fusion_for_Few-Shot_Font_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
On the Convergence of IRLS and Its Variants in Outlier-Robust Estimation | Liangzu Peng, Christian Kümmerle, René Vidal | Outlier-robust estimation involves estimating some parameters (e.g., 3D rotations) from data samples in the presence of outliers, and is typically formulated as a non-convex and non-smooth problem. For this problem, the classical method called iteratively reweighted least-squares (IRLS) and its variants have shown impressive performance. This paper makes several contributions towards understanding why these algorithms work so well. First, we incorporate majorization and graduated non-convexity (GNC) into the IRLS framework and prove that the resulting IRLS variant is a convergent method for outlier-robust estimation. Moreover, in the robust regression context with a constant fraction of outliers, we prove this IRLS variant converges to the ground truth at a global linear and local quadratic rate for a random Gaussian feature matrix with high probability. Experiments corroborate our theory and show that the proposed IRLS variant converges within 5-10 iterations for typical problem instances of outlier-robust estimation, while state-of-the-art methods need at least 30 iterations. A basic implementation of our method is provided: https://github.com/liangzu/IRLS-CVPR2023 | https://openaccess.thecvf.com/content/CVPR2023/papers/Peng_On_the_Convergence_of_IRLS_and_Its_Variants_in_Outlier-Robust_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Peng_On_the_Convergence_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Peng_On_the_Convergence_of_IRLS_and_Its_Variants_in_Outlier-Robust_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Peng_On_the_Convergence_of_IRLS_and_Its_Variants_in_Outlier-Robust_CVPR_2023_paper.html | CVPR 2023 | null |
CLIP-S4: Language-Guided Self-Supervised Semantic Segmentation | Wenbin He, Suphanut Jamonnak, Liang Gou, Liu Ren | Existing semantic segmentation approaches are often limited by costly pixel-wise annotations and predefined classes. In this work, we present CLIP-S^4 that leverages self-supervised pixel representation learning and vision-language models to enable various semantic segmentation tasks (e.g., unsupervised, transfer learning, language-driven segmentation) without any human annotations and unknown class information. We first learn pixel embeddings with pixel-segment contrastive learning from different augmented views of images. To further improve the pixel embeddings and enable language-driven semantic segmentation, we design two types of consistency guided by vision-language models: 1) embedding consistency, aligning our pixel embeddings to the joint feature space of a pre-trained vision-language model, CLIP; and 2) semantic consistency, forcing our model to make the same predictions as CLIP over a set of carefully designed target classes with both known and unknown prototypes. Thus, CLIP-S^4 enables a new task of class-free semantic segmentation where no unknown class information is needed during training. As a result, our approach shows consistent and substantial performance improvement over four popular benchmarks compared with the state-of-the-art unsupervised and language-driven semantic segmentation methods. More importantly, our method outperforms these methods on unknown class recognition by a large margin. | https://openaccess.thecvf.com/content/CVPR2023/papers/He_CLIP-S4_Language-Guided_Self-Supervised_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/He_CLIP-S4_Language-Guided_Self-Supervised_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/He_CLIP-S4_Language-Guided_Self-Supervised_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/He_CLIP-S4_Language-Guided_Self-Supervised_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Deep Incomplete Multi-View Clustering With Cross-View Partial Sample and Prototype Alignment | Jiaqi Jin, Siwei Wang, Zhibin Dong, Xinwang Liu, En Zhu | The success of existing multi-view clustering relies on the assumption of sample integrity across multiple views. However, in real-world scenarios, samples of multi-view are partially available due to data corruption or sensor failure, which leads to incomplete multi-view clustering study (IMVC). Although several attempts have been proposed to address IMVC, they suffer from the following drawbacks: i) Existing methods mainly adopt cross-view contrastive learning forcing the representations of each sample across views to be exactly the same, which might ignore view discrepancy and flexibility in representations; ii) Due to the absence of non-observed samples across multiple views, the obtained prototypes of clusters might be unaligned and biased, leading to incorrect fusion. To address the above issues, we propose a Cross-view Partial Sample and Prototype Alignment Network (CPSPAN) for Deep Incomplete Multi-view Clustering. Firstly, unlike existing contrastive-based methods, we adopt pair-observed data alignment as 'proxy supervised signals' to guide instance-to-instance correspondence construction among views. Then, regarding of the shifted prototypes in IMVC, we further propose a prototype alignment module to achieve incomplete distribution calibration across views. Extensive experimental results showcase the effectiveness of our proposed modules, attaining noteworthy performance improvements when compared to existing IMVC competitors on benchmark datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jin_Deep_Incomplete_Multi-View_Clustering_With_Cross-View_Partial_Sample_and_Prototype_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jin_Deep_Incomplete_Multi-View_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15689 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_Deep_Incomplete_Multi-View_Clustering_With_Cross-View_Partial_Sample_and_Prototype_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_Deep_Incomplete_Multi-View_Clustering_With_Cross-View_Partial_Sample_and_Prototype_CVPR_2023_paper.html | CVPR 2023 | null |
A New Comprehensive Benchmark for Semi-Supervised Video Anomaly Detection and Anticipation | Congqi Cao, Yue Lu, Peng Wang, Yanning Zhang | Semi-supervised video anomaly detection (VAD) is a critical task in the intelligent surveillance system. However, an essential type of anomaly in VAD named scene-dependent anomaly has not received the attention of researchers. Moreover, there is no research investigating anomaly anticipation, a more significant task for preventing the occurrence of anomalous events. To this end, we propose a new comprehensive dataset, NWPU Campus, containing 43 scenes, 28 classes of abnormal events, and 16 hours of videos. At present, it is the largest semi-supervised VAD dataset with the largest number of scenes and classes of anomalies, the longest duration, and the only one considering the scene-dependent anomaly. Meanwhile, it is also the first dataset proposed for video anomaly anticipation. We further propose a novel model capable of detecting and anticipating anomalous events simultaneously. Compared with 7 outstanding VAD algorithms in recent years, our method can cope with scene-dependent anomaly detection and anomaly anticipation both well, achieving state-of-the-art performance on ShanghaiTech, CUHK Avenue, IITB Corridor and the newly proposed NWPU Campus datasets consistently. Our dataset and code is available at: https://campusvad.github.io. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cao_A_New_Comprehensive_Benchmark_for_Semi-Supervised_Video_Anomaly_Detection_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cao_A_New_Comprehensive_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_A_New_Comprehensive_Benchmark_for_Semi-Supervised_Video_Anomaly_Detection_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_A_New_Comprehensive_Benchmark_for_Semi-Supervised_Video_Anomaly_Detection_and_CVPR_2023_paper.html | CVPR 2023 | null |
Revisiting Multimodal Representation in Contrastive Learning: From Patch and Token Embeddings to Finite Discrete Tokens | Yuxiao Chen, Jianbo Yuan, Yu Tian, Shijie Geng, Xinyu Li, Ding Zhou, Dimitris N. Metaxas, Hongxia Yang | Contrastive learning-based vision-language pre-training approaches, such as CLIP, have demonstrated great success in many vision-language tasks. These methods achieve cross-modal alignment by encoding a matched image-text pair with similar feature embeddings, which are generated by aggregating information from visual patches and language tokens. However, direct aligning cross-modal information using such representations is challenging, as visual patches and text tokens differ in semantic levels and granularities. To alleviate this issue, we propose a Finite Discrete Tokens (FDT) based multimodal representation. FDT is a set of learnable tokens representing certain visual-semantic concepts. Both images and texts are embedded using shared FDT by first grounding multimodal inputs to FDT space and then aggregating the activated FDT representations. The matched visual and semantic concepts are enforced to be represented by the same set of discrete tokens by a sparse activation constraint. As a result, the granularity gap between the two modalities is reduced. Through both quantitative and qualitative analyses, we demonstrate that using FDT representations in CLIP-style models improves cross-modal alignment and performance in visual recognition and vision-language downstream tasks. Furthermore, we show that our method can learn more comprehensive representations, and the learned FDT capture meaningful cross-modal correspondence, ranging from objects to actions and attributes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Revisiting_Multimodal_Representation_in_Contrastive_Learning_From_Patch_and_Token_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Revisiting_Multimodal_Representation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14865 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Revisiting_Multimodal_Representation_in_Contrastive_Learning_From_Patch_and_Token_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Revisiting_Multimodal_Representation_in_Contrastive_Learning_From_Patch_and_Token_CVPR_2023_paper.html | CVPR 2023 | null |
3Mformer: Multi-Order Multi-Mode Transformer for Skeletal Action Recognition | Lei Wang, Piotr Koniusz | Many skeletal action recognition models use GCNs to represent the human body by 3D body joints connected body parts. GCNs aggregate one- or few-hop graph neighbourhoods, and ignore the dependency between not linked body joints. We propose to form hypergraph to model hyper-edges between graph nodes (e.g., third- and fourth-order hyper-edges capture three and four nodes) which help capture higher-order motion patterns of groups of body joints. We split action sequences into temporal blocks, Higher-order Transformer (HoT) produces embeddings of each temporal block based on (i) the body joints, (ii) pairwise links of body joints and (iii) higher-order hyper-edges of skeleton body joints. We combine such HoT embeddings of hyper-edges of orders 1, ..., r by a novel Multi-order Multi-mode Transformer (3Mformer) with two modules whose order can be exchanged to achieve coupled-mode attention on coupled-mode tokens based on 'channel-temporal block', 'order-channel-body joint', 'channel-hyper-edge (any order)' and 'channel-only' pairs. The first module, called Multi-order Pooling (MP), additionally learns weighted aggregation along the hyper-edge mode, whereas the second module, Temporal block Pooling (TP), aggregates along the temporal block mode. Our end-to-end trainable network yields state-of-the-art results compared to GCN-, transformer- and hypergraph-based counterparts. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_3Mformer_Multi-Order_Multi-Mode_Transformer_for_Skeletal_Action_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_3Mformer_Multi-Order_Multi-Mode_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14474 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_3Mformer_Multi-Order_Multi-Mode_Transformer_for_Skeletal_Action_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_3Mformer_Multi-Order_Multi-Mode_Transformer_for_Skeletal_Action_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
HumanBench: Towards General Human-Centric Perception With Projector Assisted Pretraining | Shixiang Tang, Cheng Chen, Qingsong Xie, Meilin Chen, Yizhou Wang, Yuanzheng Ci, Lei Bai, Feng Zhu, Haiyang Yang, Li Yi, Rui Zhao, Wanli Ouyang | Human-centric perceptions include a variety of vision tasks, which have widespread industrial applications, including surveillance, autonomous driving, and the metaverse. It is desirable to have a general pretrain model for versatile human-centric downstream tasks. This paper forges ahead along this path from the aspects of both benchmark and pretraining methods. Specifically, we propose a HumanBench based on existing datasets to comprehensively evaluate on the common ground the generalization abilities of different pretraining methods on 19 datasets from 6 diverse downstream tasks, including person ReID, pose estimation, human parsing, pedestrian attribute recognition, pedestrian detection, and crowd counting. To learn both coarse-grained and fine-grained knowledge in human bodies, we further propose a Projector AssisTed Hierarchical pretraining method (PATH) to learn diverse knowledge at different granularity levels. Comprehensive evaluations on HumanBench show that our PATH achieves new state-of-the-art results on 17 downstream datasets and on-par results on the other 2 datasets. The code will be publicly at https://github.com/OpenGVLab/HumanBench. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tang_HumanBench_Towards_General_Human-Centric_Perception_With_Projector_Assisted_Pretraining_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tang_HumanBench_Towards_General_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.05675 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_HumanBench_Towards_General_Human-Centric_Perception_With_Projector_Assisted_Pretraining_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_HumanBench_Towards_General_Human-Centric_Perception_With_Projector_Assisted_Pretraining_CVPR_2023_paper.html | CVPR 2023 | null |
Heterogeneous Continual Learning | Divyam Madaan, Hongxu Yin, Wonmin Byeon, Jan Kautz, Pavlo Molchanov | We propose a novel framework and a solution to tackle the continual learning (CL) problem with changing network architectures. Most CL methods focus on adapting a single architecture to a new task/class by modifying its weights. However, with rapid progress in architecture design, the problem of adapting existing solutions to novel architectures becomes relevant. To address this limitation, we propose Heterogeneous Continual Learning (HCL), where a wide range of evolving network architectures emerge continually together with novel data/tasks. As a solution, we build on top of the distillation family of techniques and modify it to a new setting where a weaker model takes the role of a teacher; meanwhile, a new stronger architecture acts as a student. Furthermore, we consider a setup of limited access to previous data and propose Quick Deep Inversion (QDI) to recover prior task visual features to support knowledge transfer. QDI significantly reduces computational costs compared to previous solutions and improves overall performance. In summary, we propose a new setup for CL with a modified knowledge distillation paradigm and design a quick data inversion method to enhance distillation. Our evaluation of various benchmarks shows a significant improvement on accuracy in comparison to state-of-the-art methods over various networks architectures. | https://openaccess.thecvf.com/content/CVPR2023/papers/Madaan_Heterogeneous_Continual_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Madaan_Heterogeneous_Continual_Learning_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Madaan_Heterogeneous_Continual_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Madaan_Heterogeneous_Continual_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Object Pose Estimation With Statistical Guarantees: Conformal Keypoint Detection and Geometric Uncertainty Propagation | Heng Yang, Marco Pavone | The two-stage object pose estimation paradigm first detects semantic keypoints on the image and then estimates the 6D pose by minimizing reprojection errors. Despite performing well on standard benchmarks, existing techniques offer no provable guarantees on the quality and uncertainty of the estimation. In this paper, we inject two fundamental changes, namely conformal keypoint detection and geometric uncertainty propagation, into the two-stage paradigm and propose the first pose estimator that endows an estimation with provable and computable worst-case error bounds. On one hand, conformal keypoint detection applies the statistical machinery of inductive conformal prediction to convert heuristic keypoint detections into circular or elliptical prediction sets that cover the groundtruth keypoints with a user-specified marginal probability (e.g., 90%). Geometric uncertainty propagation, on the other, propagates the geometric constraints on the keypoints to the 6D object pose, leading to a Pose UnceRtainty SEt (PURSE) that guarantees coverage of the groundtruth pose with the same probability. The PURSE, however, is a nonconvex set that does not directly lead to estimated poses and uncertainties. Therefore, we develop RANdom SAmple averaGing (RANSAG) to compute an average pose and apply semidefinite relaxation to upper bound the worst-case errors between the average pose and the groundtruth. On the LineMOD Occlusion dataset we demonstrate: (i) the PURSE covers the groundtruth with valid probabilities; (ii) the worst-case error bounds provide correct uncertainty quantification; and (iii) the average pose achieves better or similar accuracy as representative methods based on sparse keypoints. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Object_Pose_Estimation_With_Statistical_Guarantees_Conformal_Keypoint_Detection_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Object_Pose_Estimation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12246 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Object_Pose_Estimation_With_Statistical_Guarantees_Conformal_Keypoint_Detection_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Object_Pose_Estimation_With_Statistical_Guarantees_Conformal_Keypoint_Detection_and_CVPR_2023_paper.html | CVPR 2023 | null |
Transformer Scale Gate for Semantic Segmentation | Hengcan Shi, Munawar Hayat, Jianfei Cai | Effectively encoding multi-scale contextual information is crucial for accurate semantic segmentation. Most of the existing transformer-based segmentation models combine features across scales without any selection, where features on sub-optimal scales may degrade segmentation outcomes. Leveraging from the inherent properties of Vision Transformers, we propose a simple yet effective module, Transformer Scale Gate (TSG), to optimally combine multi-scale features. TSG exploits cues in self and cross attentions in Vision Transformers for the scale selection. TSG is a highly flexible plug-and-play module, and can easily be incorporated with any encoder-decoder-based hierarchical vision Transformer architecture. Extensive experiments on the Pascal Context, ADE20K and Cityscapes datasets demonstrate that our feature selection strategy achieves consistent gains. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shi_Transformer_Scale_Gate_for_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shi_Transformer_Scale_Gate_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2205.07056 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shi_Transformer_Scale_Gate_for_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shi_Transformer_Scale_Gate_for_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Deep Graph Reprogramming | Yongcheng Jing, Chongbin Yuan, Li Ju, Yiding Yang, Xinchao Wang, Dacheng Tao | In this paper, we explore a novel model reusing task tailored for graph neural networks (GNNs), termed as "deep graph reprogramming". We strive to reprogram a pre-trained GNN, without amending raw node features nor model parameters, to handle a bunch of cross-level downstream tasks in various domains. To this end, we propose an innovative Data Reprogramming paradigm alongside a Model Reprogramming paradigm. The former one aims to address the challenge of diversified graph feature dimensions for various tasks on the input side, while the latter alleviates the dilemma of fixed per-task-per-model behavior on the model side. For data reprogramming, we specifically devise an elaborated Meta-FeatPadding method to deal with heterogeneous input dimensions, and also develop a transductive Edge-Slimming as well as an inductive Meta-GraPadding approach for diverse homogenous samples. Meanwhile, for model reprogramming, we propose a novel task-adaptive Reprogrammable-Aggregator, to endow the frozen model with larger expressive capacities in handling cross-domain tasks. Experiments on fourteen datasets across node/graph classification/regression, 3D object recognition, and distributed action recognition, demonstrate that the proposed methods yield gratifying results, on par with those by re-training from scratch. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jing_Deep_Graph_Reprogramming_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jing_Deep_Graph_Reprogramming_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.14593 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jing_Deep_Graph_Reprogramming_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jing_Deep_Graph_Reprogramming_CVPR_2023_paper.html | CVPR 2023 | null |
Compacting Binary Neural Networks by Sparse Kernel Selection | Yikai Wang, Wenbing Huang, Yinpeng Dong, Fuchun Sun, Anbang Yao | Binary Neural Network (BNN) represents convolution weights with 1-bit values, which enhances the efficiency of storage and computation. This paper is motivated by a previously revealed phenomenon that the binary kernels in successful BNNs are nearly power-law distributed: their values are mostly clustered into a small number of codewords. This phenomenon encourages us to compact typical BNNs and obtain further close performance through learning non-repetitive kernels within a binary kernel subspace. Specifically, we regard the binarization process as kernel grouping in terms of a binary codebook, and our task lies in learning to select a smaller subset of codewords from the full codebook. We then leverage the Gumbel-Sinkhorn technique to approximate the codeword selection process, and develop the Permutation Straight-Through Estimator (PSTE) that is able to not only optimize the selection process end-to-end but also maintain the non-repetitive occupancy of selected codewords. Experiments verify that our method reduces both the model size and bit-wise computational costs, and achieves accuracy improvements compared with state-of-the-art BNNs under comparable budgets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Compacting_Binary_Neural_Networks_by_Sparse_Kernel_Selection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Compacting_Binary_Neural_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14470 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Compacting_Binary_Neural_Networks_by_Sparse_Kernel_Selection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Compacting_Binary_Neural_Networks_by_Sparse_Kernel_Selection_CVPR_2023_paper.html | CVPR 2023 | null |
EMT-NAS:Transferring Architectural Knowledge Between Tasks From Different Datasets | Peng Liao, Yaochu Jin, Wenli Du | The success of multi-task learning (MTL) can largely be attributed to the shared representation of related tasks, allowing the models to better generalise. In deep learning, this is usually achieved by sharing a common neural network architecture and jointly training the weights. However, the joint training of weighting parameters on multiple related tasks may lead to performance degradation, known as negative transfer. To address this issue, this work proposes an evolutionary multi-tasking neural architecture search (EMT-NAS) algorithm to accelerate the search process by transferring architectural knowledge across multiple related tasks. In EMT-NAS, unlike the traditional MTL, the model for each task has a personalised network architecture and its own weights, thus offering the capability of effectively alleviating negative transfer. A fitness re-evaluation method is suggested to alleviate fluctuations in performance evaluations resulting from parameter sharing and the mini-batch gradient descent training method, thereby avoiding losing promising solutions during the search process. To rigorously verify the performance of EMT-NAS, the classification tasks used in the empirical assessments are derived from different datasets, including the CIFAR-10 and CIFAR-100, and four MedMNIST datasets. Extensive comparative experiments on different numbers of tasks demonstrate that EMT-NAS takes 8% and up to 40% on CIFAR and MedMNIST, respectively, less time to find competitive neural architectures than its single-task counterparts. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liao_EMT-NASTransferring_Architectural_Knowledge_Between_Tasks_From_Different_Datasets_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liao_EMT-NASTransferring_Architectural_Knowledge_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liao_EMT-NASTransferring_Architectural_Knowledge_Between_Tasks_From_Different_Datasets_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liao_EMT-NASTransferring_Architectural_Knowledge_Between_Tasks_From_Different_Datasets_CVPR_2023_paper.html | CVPR 2023 | null |
3D-Aware Multi-Class Image-to-Image Translation With NeRFs | Senmao Li, Joost van de Weijer, Yaxing Wang, Fahad Shahbaz Khan, Meiqin Liu, Jian Yang | Recent advances in 3D-aware generative models (3D-aware GANs) combined with Neural Radiance Fields (NeRF) have achieved impressive results. However no prior works investigate 3D-aware GANs for 3D consistent multi-class image-to-image (3D-aware I2I) translation. Naively using 2D-I2I translation methods suffers from unrealistic shape/identity change. To perform 3D-aware multi-class I2I translation, we decouple this learning process into a multi-class 3D-aware GAN step and a 3D-aware I2I translation step. In the first step, we propose two novel techniques: a new conditional architecture and an effective training strategy. In the second step, based on the well-trained multi-class 3D-aware GAN architecture, that preserves view-consistency, we construct a 3D-aware I2I translation system. To further reduce the view-consistency problems, we propose several new techniques, including a U-net-like adaptor network design, a hierarchical representation constrain and a relative regularization loss. In extensive experiments on two datasets, quantitative and qualitative results demonstrate that we successfully perform 3D-aware I2I translation with multi-view consistency. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_3D-Aware_Multi-Class_Image-to-Image_Translation_With_NeRFs_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_3D-Aware_Multi-Class_Image-to-Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15012 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_3D-Aware_Multi-Class_Image-to-Image_Translation_With_NeRFs_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_3D-Aware_Multi-Class_Image-to-Image_Translation_With_NeRFs_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Joint Latent Space EBM Prior Model for Multi-Layer Generator | Jiali Cui, Ying Nian Wu, Tian Han | This paper studies the fundamental problem of learning multi-layer generator models. The multi-layer generator model builds multiple layers of latent variables as a prior model on top of the generator, which benefits learning complex data distribution and hierarchical representations. However, such a prior model usually focuses on modeling inter-layer relations between latent variables by assuming non-informative (conditional) Gaussian distributions, which can be limited in model expressivity. To tackle this issue and learn more expressive prior models, we propose an energy-based model (EBM) on the joint latent space over all layers of latent variables with the multi-layer generator as its backbone. Such joint latent space EBM prior model captures the intra-layer contextual relations at each layer through layer-wise energy terms, and latent variables across different layers are jointly corrected. We develop a joint training scheme via maximum likelihood estimation (MLE), which involves Markov Chain Monte Carlo (MCMC) sampling for both prior and posterior distributions of the latent variables from different layers. To ensure efficient inference and learning, we further propose a variational training scheme where an inference model is used to amortize the costly posterior MCMC sampling. Our experiments demonstrate that the learned model can be expressive in generating high-quality images and capturing hierarchical features for better outlier detection. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cui_Learning_Joint_Latent_Space_EBM_Prior_Model_for_Multi-Layer_Generator_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cui_Learning_Joint_Latent_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cui_Learning_Joint_Latent_Space_EBM_Prior_Model_for_Multi-Layer_Generator_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cui_Learning_Joint_Latent_Space_EBM_Prior_Model_for_Multi-Layer_Generator_CVPR_2023_paper.html | CVPR 2023 | null |
Unsupervised Visible-Infrared Person Re-Identification via Progressive Graph Matching and Alternate Learning | Zesen Wu, Mang Ye | Unsupervised visible-infrared person re-identification is a challenging task due to the large modality gap and the unavailability of cross-modality correspondences. Cross-modality correspondences are very crucial to bridge the modality gap. Some existing works try to mine cross-modality correspondences, but they focus only on local information. They do not fully exploit the global relationship across identities, thus limiting the quality of the mined correspondences. Worse still, the number of clusters of the two modalities is often inconsistent, exacerbating the unreliability of the generated correspondences. In response, we devise a Progressive Graph Matching method to globally mine cross-modality correspondences under cluster imbalance scenarios. PGM formulates correspondences mining as a graph matching process and considers the global information by minimizing the global matching cost, where the matching cost measures the dissimilarity of clusters. Besides, PGM adopts a progressive strategy to address the imbalance issue with multiple dynamic matching processes. Based on PGM, we design an Alternate Cross Contrastive Learning (ACCL) module to reduce the modality gap with the mined cross-modality correspondences, while mitigating the effect of noise in correspondences through an alternate scheme. Extensive experiments demonstrate the reliability of the generated correspondences and the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Unsupervised_Visible-Infrared_Person_Re-Identification_via_Progressive_Graph_Matching_and_Alternate_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_Unsupervised_Visible-Infrared_Person_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Unsupervised_Visible-Infrared_Person_Re-Identification_via_Progressive_Graph_Matching_and_Alternate_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Unsupervised_Visible-Infrared_Person_Re-Identification_via_Progressive_Graph_Matching_and_Alternate_CVPR_2023_paper.html | CVPR 2023 | null |
Hierarchical B-Frame Video Coding Using Two-Layer CANF Without Motion Coding | David Alexandre, Hsueh-Ming Hang, Wen-Hsiao Peng | Typical video compression systems consist of two main modules: motion coding and residual coding. This general architecture is adopted by classical coding schemes (such as international standards H.265 and H.266) and deep learning-based coding schemes. We propose a novel B-frame coding architecture based on two-layer Conditional Augmented Normalization Flows (CANF). It has the striking feature of not transmitting any motion information. Our proposed idea of video compression without motion coding offers a new direction for learned video coding. Our base layer is a low-resolution image compressor that replaces the full-resolution motion compressor. The low-resolution coded image is merged with the warped high-resolution images to generate a high-quality image as a conditioning signal for the enhancement-layer image coding in full resolution. One advantage of this architecture is significantly reduced computational complexity due to eliminating the motion information compressor. In addition, we adopt a skip-mode coding technique to reduce the transmitted latent samples. The rate-distortion performance of our scheme is slightly lower than that of the state-of-the-art learned B-frame coding scheme, B-CANF, but outperforms other learned B-frame coding schemes. However, compared to B-CANF, our scheme saves 45% of multiply-accumulate operations (MACs) for encoding and 27% of MACs for decoding. The code is available at https://nycu-clab.github.io. | https://openaccess.thecvf.com/content/CVPR2023/papers/Alexandre_Hierarchical_B-Frame_Video_Coding_Using_Two-Layer_CANF_Without_Motion_Coding_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Alexandre_Hierarchical_B-Frame_Video_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Alexandre_Hierarchical_B-Frame_Video_Coding_Using_Two-Layer_CANF_Without_Motion_Coding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Alexandre_Hierarchical_B-Frame_Video_Coding_Using_Two-Layer_CANF_Without_Motion_Coding_CVPR_2023_paper.html | CVPR 2023 | null |
Benchmarking Robustness of 3D Object Detection to Common Corruptions | Yinpeng Dong, Caixin Kang, Jinlai Zhang, Zijian Zhu, Yikai Wang, Xiao Yang, Hang Su, Xingxing Wei, Jun Zhu | 3D object detection is an important task in autonomous driving to perceive the surroundings. Despite the excellent performance, the existing 3D detectors lack the robustness to real-world corruptions caused by adverse weathers, sensor noises, etc., provoking concerns about the safety and reliability of autonomous driving systems. To comprehensively and rigorously benchmark the corruption robustness of 3D detectors, in this paper we design 27 types of common corruptions for both LiDAR and camera inputs considering real-world driving scenarios. By synthesizing these corruptions on public datasets, we establish three corruption robustness benchmarks---KITTI-C, nuScenes-C, and Waymo-C. Then, we conduct large-scale experiments on 24 diverse 3D object detection models to evaluate their corruption robustness. Based on the evaluation results, we draw several important findings, including: 1) motion-level corruptions are the most threatening ones that lead to significant performance drop of all models; 2) LiDAR-camera fusion models demonstrate better robustness; 3) camera-only models are extremely vulnerable to image corruptions, showing the indispensability of LiDAR point clouds. We release the benchmarks and codes at https://github.com/thu-ml/3D_Corruptions_AD to be helpful for future studies. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dong_Benchmarking_Robustness_of_3D_Object_Detection_to_Common_Corruptions_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dong_Benchmarking_Robustness_of_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11040 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dong_Benchmarking_Robustness_of_3D_Object_Detection_to_Common_Corruptions_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dong_Benchmarking_Robustness_of_3D_Object_Detection_to_Common_Corruptions_CVPR_2023_paper.html | CVPR 2023 | null |
Unified Mask Embedding and Correspondence Learning for Self-Supervised Video Segmentation | Liulei Li, Wenguan Wang, Tianfei Zhou, Jianwu Li, Yi Yang | The objective of this paper is self-supervised learning of video object segmentation. We develop a unified framework which simultaneously models cross-frame dense correspondence for locally discriminative feature learning and embeds object-level context for target-mask decoding. As a result, it is able to directly learn to perform mask-guided sequential segmentation from unlabeled videos, in contrast to previous efforts usually relying on an oblique solution --- cheaply "copying" labels according to pixel-wise correlations. Concretely, our algorithm alternates between i) clustering video pixels for creating pseudo segmentation labels ex nihilo; and ii) utilizing the pseudo labels to learn mask encoding and decoding for VOS. Unsupervised correspondence learning is further incorporated into this self-taught, mask embedding scheme, so as to ensure the generic nature of the learnt representation and avoid cluster degeneracy. Our algorithm sets state-of-the-arts on two standard benchmarks (i.e., DAVIS17 and YouTube-VOS), narrowing the gap between self- and fully-supervised VOS, in terms of both performance and network architecture design. Our full code will be released. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Unified_Mask_Embedding_and_Correspondence_Learning_for_Self-Supervised_Video_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Unified_Mask_Embedding_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10100 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Unified_Mask_Embedding_and_Correspondence_Learning_for_Self-Supervised_Video_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Unified_Mask_Embedding_and_Correspondence_Learning_for_Self-Supervised_Video_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Seeing Beyond the Brain: Conditional Diffusion Model With Sparse Masked Modeling for Vision Decoding | Zijiao Chen, Jiaxin Qing, Tiange Xiang, Wan Lin Yue, Juan Helen Zhou | Decoding visual stimuli from brain recordings aims to deepen our understanding of the human visual system and build a solid foundation for bridging human and computer vision through the Brain-Computer Interface. However, reconstructing high-quality images with correct semantics from brain recordings is a challenging problem due to the complex underlying representations of brain signals and the scarcity of data annotations. In this work, we present MinD-Vis: Sparse Masked Brain Modeling with Double-Conditioned Latent Diffusion Model for Human Vision Decoding. Firstly, we learn an effective self-supervised representation of fMRI data using mask modeling in a large latent space inspired by the sparse coding of information in the primary visual cortex. Then by augmenting a latent diffusion model with double-conditioning, we show that MinD-Vis can reconstruct highly plausible images with semantically matching details from brain recordings using very few paired annotations. We benchmarked our model qualitatively and quantitatively; the experimental results indicate that our method outperformed state-of-the-art in both semantic mapping (100-way semantic classification) and generation quality (FID) by 66% and 41% respectively. An exhaustive ablation study was also conducted to analyze our framework. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Seeing_Beyond_the_Brain_Conditional_Diffusion_Model_With_Sparse_Masked_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Seeing_Beyond_the_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.06956 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Seeing_Beyond_the_Brain_Conditional_Diffusion_Model_With_Sparse_Masked_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Seeing_Beyond_the_Brain_Conditional_Diffusion_Model_With_Sparse_Masked_CVPR_2023_paper.html | CVPR 2023 | null |
PointAvatar: Deformable Point-Based Head Avatars From Videos | Yufeng Zheng, Wang Yifan, Gordon Wetzstein, Michael J. Black, Otmar Hilliges | The ability to create realistic animatable and relightable head avatars from casual video sequences would open up wide ranging applications in communication and entertainment. Current methods either build on explicit 3D morphable meshes (3DMM) or exploit neural implicit representations. The former are limited by fixed topology, while the latter are non-trivial to deform and inefficient to render. Furthermore, existing approaches entangle lighting and albedo, limiting the ability to re-render the avatar in new environments. In contrast, we propose PointAvatar, a deformable point-based representation that disentangles the source color into intrinsic albedo and normal-dependent shading. We demonstrate that PointAvatar bridges the gap between existing mesh- and implicit representations, combining high-quality geometry and appearance with topological flexibility, ease of deformation and rendering efficiency. We show that our method is able to generate animatable 3D avatars using monocular videos from multiple sources including hand-held smartphones, laptop webcams and internet videos, achieving state-of-the-art quality in challenging cases where previous methods fail, e.g., thin hair strands, while being significantly more efficient in training than competing methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zheng_PointAvatar_Deformable_Point-Based_Head_Avatars_From_Videos_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zheng_PointAvatar_Deformable_Point-Based_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2212.08377 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_PointAvatar_Deformable_Point-Based_Head_Avatars_From_Videos_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_PointAvatar_Deformable_Point-Based_Head_Avatars_From_Videos_CVPR_2023_paper.html | CVPR 2023 | null |
Seeing Through the Glass: Neural 3D Reconstruction of Object Inside a Transparent Container | Jinguang Tong, Sundaram Muthu, Fahira Afzal Maken, Chuong Nguyen, Hongdong Li | In this paper, we define a new problem of recovering the 3D geometry of an object confined in a transparent enclosure. We also propose a novel method for solving this challenging problem. Transparent enclosures pose challenges of multiple light reflections and refractions at the interface between different propagation media e.g. air or glass. These multiple reflections and refractions cause serious image distortions which invalidate the single viewpoint assumption. Hence the 3D geometry of such objects cannot be reliably reconstructed using existing methods, such as traditional structure from motion or modern neural reconstruction methods. We solve this problem by explicitly modeling the scene as two distinct sub-spaces, inside and outside the transparent enclosure. We use an existing neural reconstruction method (NeuS) that implicitly represents the geometry and appearance of the inner subspace. In order to account for complex light interactions, we develop a hybrid rendering strategy that combines volume rendering with ray tracing. We then recover the underlying geometry and appearance of the model by minimizing the difference between the real and rendered images. We evaluate our method on both synthetic and real data. Experiment results show that our method outperforms the state-of-the-art (SOTA) methods. Codes and data will be available at https://github.com/hirotong/ReNeuS | https://openaccess.thecvf.com/content/CVPR2023/papers/Tong_Seeing_Through_the_Glass_Neural_3D_Reconstruction_of_Object_Inside_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tong_Seeing_Through_the_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13805 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tong_Seeing_Through_the_Glass_Neural_3D_Reconstruction_of_Object_Inside_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tong_Seeing_Through_the_Glass_Neural_3D_Reconstruction_of_Object_Inside_CVPR_2023_paper.html | CVPR 2023 | null |
OrienterNet: Visual Localization in 2D Public Maps With Neural Matching | null | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2023/html/Sarlin_OrienterNet_Visual_Localization_in_2D_Public_Maps_With_Neural_Matching_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sarlin_OrienterNet_Visual_Localization_in_2D_Public_Maps_With_Neural_Matching_CVPR_2023_paper.html | CVPR 2023 | null |
PMatch: Paired Masked Image Modeling for Dense Geometric Matching | Shengjie Zhu, Xiaoming Liu | Dense geometric matching determines the dense pixel-wise correspondence between a source and support image corresponding to the same 3D structure. Prior works employ an encoder of transformer blocks to correlate the two-frame features. However, existing monocular pretraining tasks, e.g., image classification, and masked image modeling (MIM), can not pretrain the cross-frame module, yielding less optimal performance. To resolve this, we reformulate the MIM from reconstructing a single masked image to reconstructing a pair of masked images, enabling the pretraining of transformer module. Additionally, we incorporate a decoder into pretraining for improved upsampling results. Further, to be robust to the textureless area, we propose a novel cross-frame global matching module (CFGM). Since the most textureless area is planar surfaces, we propose a homography loss to further regularize its learning. Combined together, we achieve the State-of-The-Art (SoTA) performance on geometric matching. Codes and models are available at https://github.com/ShngJZ/PMatch. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_PMatch_Paired_Masked_Image_Modeling_for_Dense_Geometric_Matching_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_PMatch_Paired_Masked_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.17342 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_PMatch_Paired_Masked_Image_Modeling_for_Dense_Geometric_Matching_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_PMatch_Paired_Masked_Image_Modeling_for_Dense_Geometric_Matching_CVPR_2023_paper.html | CVPR 2023 | null |
Neural Voting Field for Camera-Space 3D Hand Pose Estimation | Lin Huang, Chung-Ching Lin, Kevin Lin, Lin Liang, Lijuan Wang, Junsong Yuan, Zicheng Liu | We present a unified framework for camera-space 3D hand pose estimation from a single RGB image based on 3D implicit representation. As opposed to recent works, most of which first adopt holistic or pixel-level dense regression to obtain relative 3D hand pose and then follow with complex second-stage operations for 3D global root or scale recovery, we propose a novel unified 3D dense regression scheme to estimate camera-space 3D hand pose via dense 3D point-wise voting in camera frustum. Through direct dense modeling in 3D domain inspired by Pixel-aligned Implicit Functions for 3D detailed reconstruction, our proposed Neural Voting Field (NVF) fully models 3D dense local evidence and hand global geometry, helping to alleviate common 2D-to-3D ambiguities. Specifically, for a 3D query point in camera frustum and its pixel-aligned image feature, NVF, represented by a Multi-Layer Perceptron, regresses: (i) its signed distance to the hand surface; (ii) a set of 4D offset vectors (1D voting weight and 3D directional vector to each hand joint). Following a vote-casting scheme, 4D offset vectors from near-surface points are selected to calculate the 3D hand joint coordinates by a weighted average. Experiments demonstrate that NVF outperforms existing state-of-the-art algorithms on FreiHAND dataset for camera-space 3D hand pose estimation. We also adapt NVF to the classic task of root-relative 3D hand pose estimation, for which NVF also obtains state-of-the-art results on HO3D dataset. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Neural_Voting_Field_for_Camera-Space_3D_Hand_Pose_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Neural_Voting_Field_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.04328 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Neural_Voting_Field_for_Camera-Space_3D_Hand_Pose_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Neural_Voting_Field_for_Camera-Space_3D_Hand_Pose_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
STMT: A Spatial-Temporal Mesh Transformer for MoCap-Based Action Recognition | Xiaoyu Zhu, Po-Yao Huang, Junwei Liang, Celso M. de Melo, Alexander G. Hauptmann | We study the problem of human action recognition using motion capture (MoCap) sequences. Unlike existing techniques that take multiple manual steps to derive standardized skeleton representations as model input, we propose a novel Spatial-Temporal Mesh Transformer (STMT) to directly model the mesh sequences. The model uses a hierarchical transformer with intra-frame off-set attention and inter-frame self-attention. The attention mechanism allows the model to freely attend between any two vertex patches to learn non-local relationships in the spatial-temporal domain. Masked vertex modeling and future frame prediction are used as two self-supervised tasks to fully activate the bi-directional and auto-regressive attention in our hierarchical transformer. The proposed method achieves state-of-the-art performance compared to skeleton-based and point-cloud-based models on common MoCap benchmarks. Code is available at https://github.com/zgzxy001/STMT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_STMT_A_Spatial-Temporal_Mesh_Transformer_for_MoCap-Based_Action_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_STMT_A_Spatial-Temporal_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.18177 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_STMT_A_Spatial-Temporal_Mesh_Transformer_for_MoCap-Based_Action_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_STMT_A_Spatial-Temporal_Mesh_Transformer_for_MoCap-Based_Action_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Visual Recognition-Driven Image Restoration for Multiple Degradation With Intrinsic Semantics Recovery | Zizheng Yang, Jie Huang, Jiahao Chang, Man Zhou, Hu Yu, Jinghao Zhang, Feng Zhao | Deep image recognition models suffer a significant performance drop when applied to low-quality images since they are trained on high-quality images. Although many studies have investigated to solve the issue through image restoration or domain adaptation, the former focuses on visual quality rather than recognition quality, while the latter requires semantic annotations for task-specific training. In this paper, to address more practical scenarios, we propose a Visual Recognition-Driven Image Restoration network for multiple degradation, dubbed VRD-IR, to recover high-quality images from various unknown corruption types from the perspective of visual recognition within one model. Concretely, we harmonize the semantic representations of diverse degraded images into a unified space in a dynamic manner, and then optimize them towards intrinsic semantics recovery. Moreover, a prior-ascribing optimization strategy is introduced to encourage VRD-IR to couple with various downstream recognition tasks better. Our VRD-IR is corruption- and recognition-agnostic, and can be inserted into various recognition tasks directly as an image enhancement module. Extensive experiments on multiple image distortions demonstrate that our VRD-IR surpasses existing image restoration methods and show superior performance on diverse high-level tasks, including classification, detection, and person re-identification. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Visual_Recognition-Driven_Image_Restoration_for_Multiple_Degradation_With_Intrinsic_Semantics_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Visual_Recognition-Driven_Image_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Visual_Recognition-Driven_Image_Restoration_for_Multiple_Degradation_With_Intrinsic_Semantics_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Visual_Recognition-Driven_Image_Restoration_for_Multiple_Degradation_With_Intrinsic_Semantics_CVPR_2023_paper.html | CVPR 2023 | null |
High-Fidelity Generalized Emotional Talking Face Generation With Multi-Modal Emotion Space Learning | Chao Xu, Junwei Zhu, Jiangning Zhang, Yue Han, Wenqing Chu, Ying Tai, Chengjie Wang, Zhifeng Xie, Yong Liu | Recently, emotional talking face generation has received considerable attention. However, existing methods only adopt one-hot coding, image, or audio as emotion conditions, thus lacking flexible control in practical applications and failing to handle unseen emotion styles due to limited semantics. They either ignore the one-shot setting or the quality of generated faces. In this paper, we propose a more flexible and generalized framework. Specifically, we supplement the emotion style in text prompts and use an Aligned Multi-modal Emotion encoder to embed the text, image, and audio emotion modality into a unified space, which inherits rich semantic prior from CLIP. Consequently, effective multi-modal emotion space learning helps our method support arbitrary emotion modality during testing and could generalize to unseen emotion styles. Besides, an Emotion-aware Audio-to-3DMM Convertor is proposed to connect the emotion condition and the audio sequence to structural representation. A followed style-based High-fidelity Emotional Face generator is designed to generate arbitrary high-resolution realistic identities. Our texture generator hierarchically learns flow fields and animated faces in a residual manner. Extensive experiments demonstrate the flexibility and generalization of our method in emotion control and the effectiveness of high-quality face synthesis. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_High-Fidelity_Generalized_Emotional_Talking_Face_Generation_With_Multi-Modal_Emotion_Space_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_High-Fidelity_Generalized_Emotional_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.02572 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_High-Fidelity_Generalized_Emotional_Talking_Face_Generation_With_Multi-Modal_Emotion_Space_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_High-Fidelity_Generalized_Emotional_Talking_Face_Generation_With_Multi-Modal_Emotion_Space_CVPR_2023_paper.html | CVPR 2023 | null |
Masked and Adaptive Transformer for Exemplar Based Image Translation | Chang Jiang, Fei Gao, Biao Ma, Yuhao Lin, Nannan Wang, Gang Xu | We present a novel framework for exemplar based image translation. Recent advanced methods for this task mainly focus on establishing cross-domain semantic correspondence, which sequentially dominates image generation in the manner of local style control. Unfortunately, cross domain semantic matching is challenging; and matching errors ultimately degrade the quality of generated images. To overcome this challenge, we improve the accuracy of matching on the one hand, and diminish the role of matching in image generation on the other hand. To achieve the former, we propose a masked and adaptive transformer (MAT) for learning accurate cross-domain correspondence, and executing context-aware feature augmentation. To achieve the latter, we use source features of the input and global style codes of the exemplar, as supplementary information, for decoding an image. Besides, we devise a novel contrastive style learning method, for acquire quality-discriminative style representations, which in turn benefit high-quality image generation. Experimental results show that our method, dubbed MATEBIT, performs considerably better than state-of-the-art methods, in diverse image translation tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jiang_Masked_and_Adaptive_Transformer_for_Exemplar_Based_Image_Translation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jiang_Masked_and_Adaptive_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.17123 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Masked_and_Adaptive_Transformer_for_Exemplar_Based_Image_Translation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Masked_and_Adaptive_Transformer_for_Exemplar_Based_Image_Translation_CVPR_2023_paper.html | CVPR 2023 | null |
Knowledge Combination To Learn Rotated Detection Without Rotated Annotation | Tianyu Zhu, Bryce Ferenczi, Pulak Purkait, Tom Drummond, Hamid Rezatofighi, Anton van den Hengel | Rotated bounding boxes drastically reduce output ambiguity of elongated objects, making it superior to axis-aligned bounding boxes. Despite the effectiveness, rotated detectors are not widely employed. Annotating rotated bounding boxes is such a laborious process that they are not provided in many detection datasets where axis-aligned annotations are used instead. In this paper, we propose a framework that allows the model to predict precise rotated boxes only requiring cheaper axis-aligned annotation of the target dataset. To achieve this, we leverage the fact that neural networks are capable of learning richer representation of the target domain than what is utilized by the task. The under-utilized representation can be exploited to address a more detailed task. Our framework combines task knowledge of an out-of-domain source dataset with stronger annotation and domain knowledge of the target dataset with weaker annotation. A novel assignment process and projection loss are used to enable the co-training on the source and target datasets. As a result, the model is able to solve the more detailed task in the target domain, without additional computation overhead during inference. We extensively evaluate the method on various target datasets including fresh-produce dataset, HRSC2016 and SSDD. Results show that the proposed method consistently performs on par with the fully supervised approach. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_Knowledge_Combination_To_Learn_Rotated_Detection_Without_Rotated_Annotation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_Knowledge_Combination_To_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.02199 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Knowledge_Combination_To_Learn_Rotated_Detection_Without_Rotated_Annotation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Knowledge_Combination_To_Learn_Rotated_Detection_Without_Rotated_Annotation_CVPR_2023_paper.html | CVPR 2023 | null |
Teaching Matters: Investigating the Role of Supervision in Vision Transformers | Matthew Walmer, Saksham Suri, Kamal Gupta, Abhinav Shrivastava | Vision Transformers (ViTs) have gained significant popularity in recent years and have proliferated into many applications. However, their behavior under different learning paradigms is not well explored. We compare ViTs trained through different methods of supervision, and show that they learn a diverse range of behaviors in terms of their attention, representations, and downstream performance. We also discover ViT behaviors that are consistent across supervision, including the emergence of Offset Local Attention Heads. These are self-attention heads that attend to a token adjacent to the current token with a fixed directional offset, a phenomenon that to the best of our knowledge has not been highlighted in any prior work. Our analysis shows that ViTs are highly flexible and learn to process local and global information in different orders depending on their training method. We find that contrastive self-supervised methods learn features that are competitive with explicitly supervised features, and they can even be superior for part-level tasks. We also find that the representations of reconstruction-based models show non-trivial similarity to contrastive self-supervised models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Walmer_Teaching_Matters_Investigating_the_Role_of_Supervision_in_Vision_Transformers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Walmer_Teaching_Matters_Investigating_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.03862 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Walmer_Teaching_Matters_Investigating_the_Role_of_Supervision_in_Vision_Transformers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Walmer_Teaching_Matters_Investigating_the_Role_of_Supervision_in_Vision_Transformers_CVPR_2023_paper.html | CVPR 2023 | null |
Imagic: Text-Based Real Image Editing With Diffusion Models | Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov, Huiwen Chang, Tali Dekel, Inbar Mosseri, Michal Irani | Text-conditioned image editing has recently attracted considerable interest. However, most methods are currently limited to one of the following: specific editing types (e.g., object overlay, style transfer), synthetically generated images, or requiring multiple input images of a common object. In this paper we demonstrate, for the very first time, the ability to apply complex (e.g., non-rigid) text-based semantic edits to a single real image. For example, we can change the posture and composition of one or multiple objects inside an image, while preserving its original characteristics. Our method can make a standing dog sit down, cause a bird to spread its wings, etc. -- each within its single high-resolution user-provided natural image. Contrary to previous work, our proposed method requires only a single input image and a target text (the desired edit). It operates on real images, and does not require any additional inputs (such as image masks or additional views of the object). Our method, called Imagic, leverages a pre-trained text-to-image diffusion model for this task. It produces a text embedding that aligns with both the input image and the target text, while fine-tuning the diffusion model to capture the image-specific appearance. We demonstrate the quality and versatility of Imagic on numerous inputs from various domains, showcasing a plethora of high quality complex semantic image edits, all within a single unified framework. To better assess performance, we introduce TEdBench, a highly challenging image editing benchmark. We conduct a user study, whose findings show that human raters prefer Imagic to previous leading editing methods on TEdBench. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kawar_Imagic_Text-Based_Real_Image_Editing_With_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kawar_Imagic_Text-Based_Real_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2210.09276 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kawar_Imagic_Text-Based_Real_Image_Editing_With_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kawar_Imagic_Text-Based_Real_Image_Editing_With_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Pointersect: Neural Rendering With Cloud-Ray Intersection | Jen-Hao Rick Chang, Wei-Yu Chen, Anurag Ranjan, Kwang Moo Yi, Oncel Tuzel | We propose a novel method that renders point clouds as if they are surfaces. The proposed method is differentiable and requires no scene-specific optimization. This unique capability enables, out-of-the-box, surface normal estimation, rendering room-scale point clouds, inverse rendering, and ray tracing with global illumination. Unlike existing work that focuses on converting point clouds to other representations--e.g., surfaces or implicit functions--our key idea is to directly infer the intersection of a light ray with the underlying surface represented by the given point cloud. Specifically, we train a set transformer that, given a small number of local neighbor points along a light ray, provides the intersection point, the surface normal, and the material blending weights, which are used to render the outcome of this light ray. Localizing the problem into small neighborhoods enables us to train a model with only 48 meshes and apply it to unseen point clouds. Our model achieves higher estimation accuracy than state-of-the-art surface reconstruction and point-cloud rendering methods on three test sets. When applied to room-scale point clouds, without any scene-specific optimization, the model achieves competitive quality with the state-of-the-art novel-view rendering methods. Moreover, we demonstrate ability to render and manipulate Lidar-scanned point clouds such as lighting control and object insertion. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chang_Pointersect_Neural_Rendering_With_Cloud-Ray_Intersection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chang_Pointersect_Neural_Rendering_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.12390 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chang_Pointersect_Neural_Rendering_With_Cloud-Ray_Intersection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chang_Pointersect_Neural_Rendering_With_Cloud-Ray_Intersection_CVPR_2023_paper.html | CVPR 2023 | null |
Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient Vision Transformers | Sifan Long, Zhen Zhao, Jimin Pi, Shengsheng Wang, Jingdong Wang | Vision transformers have achieved significant improvements on various vision tasks but their quadratic interactions between tokens significantly reduce computational efficiency. Many pruning methods have been proposed to remove redundant tokens for efficient vision transformers recently. However, existing studies mainly focus on the token importance to preserve local attentive tokens but completely ignore the global token diversity. In this paper, we emphasize the cruciality of diverse global semantics and propose an efficient token decoupling and merging method that can jointly consider the token importance and diversity for token pruning. According to the class token attention, we decouple the attentive and inattentive tokens. In addition to preserve the most discriminative local tokens, we merge similar inattentive tokens and match homogeneous attentive tokens to maximize the token diversity. Despite its simplicity, our method obtains a promising trade-off between model complexity and classification accuracy. On DeiT-S, our method reduces the FLOPs by 35% with only a 0.2% accuracy drop. Notably, benefiting from maintaining the token diversity, our method can even improve the accuracy of DeiT-T by 0.1% after reducing its FLOPs by 40%. | https://openaccess.thecvf.com/content/CVPR2023/papers/Long_Beyond_Attentive_Tokens_Incorporating_Token_Importance_and_Diversity_for_Efficient_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2211.11315 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Long_Beyond_Attentive_Tokens_Incorporating_Token_Importance_and_Diversity_for_Efficient_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Long_Beyond_Attentive_Tokens_Incorporating_Token_Importance_and_Diversity_for_Efficient_CVPR_2023_paper.html | CVPR 2023 | null |
You Are Catching My Attention: Are Vision Transformers Bad Learners Under Backdoor Attacks? | Zenghui Yuan, Pan Zhou, Kai Zou, Yu Cheng | Vision Transformers (ViTs), which made a splash in the field of computer vision (CV), have shaken the dominance of convolutional neural networks (CNNs). However, in the process of industrializing ViTs, backdoor attacks have brought severe challenges to security. The success of ViTs benefits from the self-attention mechanism. However, compared with CNNs, we find that this mechanism of capturing global information within patches makes ViTs more sensitive to patch-wise triggers. Under such observations, we delicately design a novel backdoor attack framework for ViTs, dubbed BadViT, which utilizes a universal patch-wise trigger to catch the model's attention from patches beneficial for classification to those with triggers, thereby manipulating the mechanism on which ViTs survive to confuse itself. Furthermore, we propose invisible variants of BadViT to increase the stealth of the attack by limiting the strength of the trigger perturbation. Through a large number of experiments, it is proved that BadViT is an efficient backdoor attack method against ViTs, which is less dependent on the number of poisons, with satisfactory convergence, and is transferable for downstream tasks. Furthermore, the risks inside of ViTs to backdoor attacks are also explored from the perspective of existing advanced defense schemes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yuan_You_Are_Catching_My_Attention_Are_Vision_Transformers_Bad_Learners_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yuan_You_Are_Catching_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yuan_You_Are_Catching_My_Attention_Are_Vision_Transformers_Bad_Learners_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yuan_You_Are_Catching_My_Attention_Are_Vision_Transformers_Bad_Learners_CVPR_2023_paper.html | CVPR 2023 | null |
STDLens: Model Hijacking-Resilient Federated Learning for Object Detection | Ka-Ho Chow, Ling Liu, Wenqi Wei, Fatih Ilhan, Yanzhao Wu | Federated Learning (FL) has been gaining popularity as a collaborative learning framework to train deep learning-based object detection models over a distributed population of clients. Despite its advantages, FL is vulnerable to model hijacking. The attacker can control how the object detection system should misbehave by implanting Trojaned gradients using only a small number of compromised clients in the collaborative learning process. This paper introduces STDLens, a principled approach to safeguarding FL against such attacks. We first investigate existing mitigation mechanisms and analyze their failures caused by the inherent errors in spatial clustering analysis on gradients. Based on the insights, we introduce a three-tier forensic framework to identify and expel Trojaned gradients and reclaim the performance over the course of FL. We consider three types of adaptive attacks and demonstrate the robustness of STDLens against advanced adversaries. Extensive experiments show that STDLens can protect FL against different model hijacking attacks and outperform existing methods in identifying and removing Trojaned gradients with significantly higher precision and much lower false-positive rates. The source code is available at https://github.com/git-disl/STDLens. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chow_STDLens_Model_Hijacking-Resilient_Federated_Learning_for_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chow_STDLens_Model_Hijacking-Resilient_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11511 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chow_STDLens_Model_Hijacking-Resilient_Federated_Learning_for_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chow_STDLens_Model_Hijacking-Resilient_Federated_Learning_for_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Contrastive Grouping With Transformer for Referring Image Segmentation | Jiajin Tang, Ge Zheng, Cheng Shi, Sibei Yang | Referring image segmentation aims to segment the target referent in an image conditioning on a natural language expression. Existing one-stage methods employ per-pixel classification frameworks, which attempt straightforwardly to align vision and language at the pixel level, thus failing to capture critical object-level information. In this paper, we propose a mask classification framework, Contrastive Grouping with Transformer network (CGFormer), which explicitly captures object-level information via token-based querying and grouping strategy. Specifically, CGFormer first introduces learnable query tokens to represent objects and then alternately queries linguistic features and groups visual features into the query tokens for object-aware cross-modal reasoning. In addition, CGFormer achieves cross-level interaction by jointly updating the query tokens and decoding masks in every two consecutive layers. Finally, CGFormer cooperates contrastive learning to the grouping strategy to identify the token and its mask corresponding to the referent. Experimental results demonstrate that CGFormer outperforms state-of-the-art methods in both segmentation and generalization settings consistently and significantly. Code is available at https://github.com/Toneyaya/CGFormer. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tang_Contrastive_Grouping_With_Transformer_for_Referring_Image_Segmentation_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Contrastive_Grouping_With_Transformer_for_Referring_Image_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Contrastive_Grouping_With_Transformer_for_Referring_Image_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
MagicPony: Learning Articulated 3D Animals in the Wild | Shangzhe Wu, Ruining Li, Tomas Jakab, Christian Rupprecht, Andrea Vedaldi | We consider the problem of predicting the 3D shape, articulation, viewpoint, texture, and lighting of an articulated animal like a horse given a single test image as input. We present a new method, dubbed MagicPony, that learns this predictor purely from in-the-wild single-view images of the object category, with minimal assumptions about the topology of deformation. At its core is an implicit-explicit representation of articulated shape and appearance, combining the strengths of neural fields and meshes. In order to help the model understand an object's shape and pose, we distil the knowledge captured by an off-the-shelf self-supervised vision transformer and fuse it into the 3D model. To overcome local optima in viewpoint estimation, we further introduce a new viewpoint sampling scheme that comes at no additional training cost. MagicPony outperforms prior work on this challenging task and demonstrates excellent generalisation in reconstructing art, despite the fact that it is only trained on real images. The code can be found on the project page at https://3dmagicpony.github.io/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_MagicPony_Learning_Articulated_3D_Animals_in_the_Wild_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_MagicPony_Learning_Articulated_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.12497 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_MagicPony_Learning_Articulated_3D_Animals_in_the_Wild_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_MagicPony_Learning_Articulated_3D_Animals_in_the_Wild_CVPR_2023_paper.html | CVPR 2023 | null |
PaCa-ViT: Learning Patch-to-Cluster Attention in Vision Transformers | Ryan Grainger, Thomas Paniagua, Xi Song, Naresh Cuntoor, Mun Wai Lee, Tianfu Wu | Vision Transformers (ViTs) are built on the assumption of treating image patches as "visual tokens" and learn patch-to-patch attention. The patch embedding based tokenizer has a semantic gap with respect to its counterpart, the textual tokenizer. The patch-to-patch attention suffers from the quadratic complexity issue, and also makes it non-trivial to explain learned ViTs. To address these issues in ViT, this paper proposes to learn Patch-to-Cluster attention (PaCa) in ViT. Queries in our PaCa-ViT starts with patches, while keys and values are directly based on clustering (with a predefined small number of clusters). The clusters are learned end-to-end, leading to better tokenizers and inducing joint clustering-for-attention and attention-for-clustering for better and interpretable models. The quadratic complexity is relaxed to linear complexity. The proposed PaCa module is used in designing efficient and interpretable ViT backbones and semantic segmentation head networks. In experiments, the proposed methods are tested on ImageNet-1k image classification, MS-COCO object detection and instance segmentation and MIT-ADE20k semantic segmentation. Compared with the prior art, it obtains better performance in all the three benchmarks than the SWin and the PVTs by significant margins in ImageNet-1k and MIT-ADE20k. It is also significantly more efficient than PVT models in MS-COCO and MIT-ADE20k due to the linear complexity. The learned clusters are semantically meaningful. Code and model checkpoints are available at https://github.com/iVMCL/PaCaViT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Grainger_PaCa-ViT_Learning_Patch-to-Cluster_Attention_in_Vision_Transformers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Grainger_PaCa-ViT_Learning_Patch-to-Cluster_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Grainger_PaCa-ViT_Learning_Patch-to-Cluster_Attention_in_Vision_Transformers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Grainger_PaCa-ViT_Learning_Patch-to-Cluster_Attention_in_Vision_Transformers_CVPR_2023_paper.html | CVPR 2023 | null |
Pix2map: Cross-Modal Retrieval for Inferring Street Maps From Images | Xindi Wu, KwunFung Lau, Francesco Ferroni, Aljoša Ošep, Deva Ramanan | Self-driving vehicles rely on urban street maps for autonomous navigation. In this paper, we introduce Pix2Map, a method for inferring urban street map topology directly from ego-view images, as needed to continually update and expand existing maps. This is a challenging task, as we need to infer a complex urban road topology directly from raw image data. The main insight of this paper is that this problem can be posed as cross-modal retrieval by learning a joint, cross-modal embedding space for images and existing maps, represented as discrete graphs that encode the topological layout of the visual surroundings. We conduct our experimental evaluation using the Argoverse dataset and show that it is indeed possible to accurately retrieve street maps corresponding to both seen and unseen roads solely from image data. Moreover, we show that our retrieved maps can be used to update or expand existing maps and even show proof-of-concept results for visual localization and image retrieval from spatial graphs. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Pix2map_Cross-Modal_Retrieval_for_Inferring_Street_Maps_From_Images_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_Pix2map_Cross-Modal_Retrieval_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.04224 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Pix2map_Cross-Modal_Retrieval_for_Inferring_Street_Maps_From_Images_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Pix2map_Cross-Modal_Retrieval_for_Inferring_Street_Maps_From_Images_CVPR_2023_paper.html | CVPR 2023 | null |
LightPainter: Interactive Portrait Relighting With Freehand Scribble | Yiqun Mei, He Zhang, Xuaner Zhang, Jianming Zhang, Zhixin Shu, Yilin Wang, Zijun Wei, Shi Yan, HyunJoon Jung, Vishal M. Patel | Recent portrait relighting methods have achieved realistic results of portrait lighting effects given a desired lighting representation such as an environment map. However, these methods are not intuitive for user interaction and lack precise lighting control. We introduce LightPainter, a scribble-based relighting system that allows users to interactively manipulate portrait lighting effect with ease. This is achieved by two conditional neural networks, a delighting module that recovers geometry and albedo optionally conditioned on skin tone, and a scribble-based module for relighting. To train the relighting module, we propose a novel scribble simulation procedure to mimic real user scribbles, which allows our pipeline to be trained without any human annotations. We demonstrate high-quality and flexible portrait lighting editing capability with both quantitative and qualitative experiments. User study comparisons with commercial lighting editing tools also demonstrate consistent user preference for our method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Mei_LightPainter_Interactive_Portrait_Relighting_With_Freehand_Scribble_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mei_LightPainter_Interactive_Portrait_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12950 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mei_LightPainter_Interactive_Portrait_Relighting_With_Freehand_Scribble_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mei_LightPainter_Interactive_Portrait_Relighting_With_Freehand_Scribble_CVPR_2023_paper.html | CVPR 2023 | null |
Affordances From Human Videos as a Versatile Representation for Robotics | Shikhar Bahl, Russell Mendonca, Lili Chen, Unnat Jain, Deepak Pathak | Building a robot that can understand and learn to interact by watching humans has inspired several vision problems. However, despite some successful results on static datasets, it remains unclear how current models can be used on a robot directly. In this paper, we aim to bridge this gap by leveraging videos of human interactions in an environment centric manner. Utilizing internet videos of human behavior, we train a visual affordance model that estimates where and how in the scene a human is likely to interact. The structure of these behavioral affordances directly enables the robot to perform many complex tasks. We show how to seamlessly integrate our affordance model with four robot learning paradigms including offline imitation learning, exploration, goal-conditioned learning, and action parameterization for reinforcement learning. We show the efficacy of our approach, which we call Vision-Robotics Bridge (VRB) across 4 real world environments, over 10 different tasks, and 2 robotic platforms operating in the wild. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bahl_Affordances_From_Human_Videos_as_a_Versatile_Representation_for_Robotics_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bahl_Affordances_From_Human_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2304.08488 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bahl_Affordances_From_Human_Videos_as_a_Versatile_Representation_for_Robotics_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bahl_Affordances_From_Human_Videos_as_a_Versatile_Representation_for_Robotics_CVPR_2023_paper.html | CVPR 2023 | null |
Unsupervised Inference of Signed Distance Functions From Single Sparse Point Clouds Without Learning Priors | Chao Chen, Yu-Shen Liu, Zhizhong Han | It is vital to infer signed distance functions (SDFs) from 3D point clouds. The latest methods rely on generalizing the priors learned from large scale supervision. However, the learned priors do not generalize well to various geometric variations that are unseen during training, especially for extremely sparse point clouds. To resolve this issue, we present a neural network to directly infer SDFs from single sparse point clouds without using signed distance supervision, learned priors or even normals. Our insight here is to learn surface parameterization and SDFs inference in an end-to-end manner. To make up the sparsity, we leverage parameterized surfaces as a coarse surface sampler to provide many coarse surface estimations in training iterations, according to which we mine supervision and our thin plate splines (TPS) based network infers SDFs as smooth functions in a statistical way. Our method significantly improves the generalization ability and accuracy in unseen point clouds. Our experimental results show our advantages over the state-of-the-art methods in surface reconstruction for sparse point clouds under synthetic datasets and real scans.The code is available at https://github.com/chenchao15/NeuralTPS. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Unsupervised_Inference_of_Signed_Distance_Functions_From_Single_Sparse_Point_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Unsupervised_Inference_of_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.14505 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Unsupervised_Inference_of_Signed_Distance_Functions_From_Single_Sparse_Point_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Unsupervised_Inference_of_Signed_Distance_Functions_From_Single_Sparse_Point_CVPR_2023_paper.html | CVPR 2023 | null |
AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation | Zhen Li, Zuo-Liang Zhu, Ling-Hao Han, Qibin Hou, Chun-Le Guo, Ming-Ming Cheng | We present All-Pairs Multi-Field Transforms (AMT), a new network architecture for video frame interpolation. It is based on two essential designs. First, we build bidirectional correlation volumes for all pairs of pixels and use the predicted bilateral flows to retrieve correlations for updating both flows and the interpolated content feature. Second, we derive multiple groups of fine-grained flow fields from one pair of updated coarse flows for performing backward warping on the input frames separately. Combining these two designs enables us to generate promising task-oriented flows and reduce the difficulties in modeling large motions and handling occluded areas during frame interpolation. These qualities promote our model to achieve state-of-the-art performance on various benchmarks with high efficiency. Moreover, our convolution-based model competes favorably compared to Transformer-based models in terms of accuracy and efficiency. Our code is available at https://github.com/MCG-NKU/AMT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_AMT_All-Pairs_Multi-Field_Transforms_for_Efficient_Frame_Interpolation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_AMT_All-Pairs_Multi-Field_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.09790 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_AMT_All-Pairs_Multi-Field_Transforms_for_Efficient_Frame_Interpolation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_AMT_All-Pairs_Multi-Field_Transforms_for_Efficient_Frame_Interpolation_CVPR_2023_paper.html | CVPR 2023 | null |
Vision Transformers Are Parameter-Efficient Audio-Visual Learners | Yan-Bo Lin, Yi-Lin Sung, Jie Lei, Mohit Bansal, Gedas Bertasius | Vision transformers (ViTs) have achieved impressive results on various computer vision tasks in the last several years. In this work, we study the capability of frozen ViTs, pretrained only on visual data, to generalize to audio-visual data without finetuning any of its original parameters. To do so, we propose a latent audio-visual hybrid (LAVISH) adapter that adapts pretrained ViTs to audio-visual tasks by injecting a small number of trainable parameters into every layer of a frozen ViT. To efficiently fuse visual and audio cues, our LAVISH adapter uses a small set of latent tokens, which form an attention bottleneck, thus, eliminating the quadratic cost of standard cross-attention. Compared to the existing modality-specific audio-visual methods, our approach achieves competitive or even better performance on various audio-visual tasks while using fewer tunable parameters and without relying on costly audio pretraining or external audio encoders. Our code is available at https://genjib.github.io/project_page/LAVISH/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Vision_Transformers_Are_Parameter-Efficient_Audio-Visual_Learners_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lin_Vision_Transformers_Are_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.07983 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Vision_Transformers_Are_Parameter-Efficient_Audio-Visual_Learners_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Vision_Transformers_Are_Parameter-Efficient_Audio-Visual_Learners_CVPR_2023_paper.html | CVPR 2023 | null |
Deep Discriminative Spatial and Temporal Network for Efficient Video Deblurring | Jinshan Pan, Boming Xu, Jiangxin Dong, Jianjun Ge, Jinhui Tang | How to effectively explore spatial and temporal information is important for video deblurring. In contrast to existing methods that directly align adjacent frames without discrimination, we develop a deep discriminative spatial and temporal network to facilitate the spatial and temporal feature exploration for better video deblurring. We first develop a channel-wise gated dynamic network to adaptively explore the spatial information. As adjacent frames usually contain different contents, directly stacking features of adjacent frames without discrimination may affect the latent clear frame restoration. Therefore, we develop a simple yet effective discriminative temporal feature fusion module to obtain useful temporal features for latent frame restoration. Moreover, to utilize the information from long-range frames, we develop a wavelet-based feature propagation method that takes the discriminative temporal feature fusion module as the basic unit to effectively propagate main structures from long-range frames for better video deblurring. We show that the proposed method does not require additional alignment methods and performs favorably against state-of-the-art ones on benchmark datasets in terms of accuracy and model complexity. | https://openaccess.thecvf.com/content/CVPR2023/papers/Pan_Deep_Discriminative_Spatial_and_Temporal_Network_for_Efficient_Video_Deblurring_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Pan_Deep_Discriminative_Spatial_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pan_Deep_Discriminative_Spatial_and_Temporal_Network_for_Efficient_Video_Deblurring_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pan_Deep_Discriminative_Spatial_and_Temporal_Network_for_Efficient_Video_Deblurring_CVPR_2023_paper.html | CVPR 2023 | null |
Training Debiased Subnetworks With Contrastive Weight Pruning | Geon Yeong Park, Sangmin Lee, Sang Wan Lee, Jong Chul Ye | Neural networks are often biased to spuriously correlated features that provide misleading statistical evidence that does not generalize. This raises an interesting question: "Does an optimal unbiased functional subnetwork exist in a severely biased network? If so, how to extract such subnetwork?" While empirical evidence has been accumulated about the existence of such unbiased subnetworks, these observations are mainly based on the guidance of ground-truth unbiased samples. Thus, it is unexplored how to discover the optimal subnetworks with biased training datasets in practice. To address this, here we first present our theoretical insight that alerts potential limitations of existing algorithms in exploring unbiased subnetworks in the presence of strong spurious correlations. We then further elucidate the importance of bias-conflicting samples on structure learning. Motivated by these observations, we propose a Debiased Contrastive Weight Pruning (DCWP) algorithm, which probes unbiased subnetworks without expensive group annotations. Experimental results demonstrate that our approach significantly outperforms state-of-the-art debiasing methods despite its considerable reduction in the number of parameters. | https://openaccess.thecvf.com/content/CVPR2023/papers/Park_Training_Debiased_Subnetworks_With_Contrastive_Weight_Pruning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Park_Training_Debiased_Subnetworks_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Park_Training_Debiased_Subnetworks_With_Contrastive_Weight_Pruning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Park_Training_Debiased_Subnetworks_With_Contrastive_Weight_Pruning_CVPR_2023_paper.html | CVPR 2023 | null |
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