Search is not available for this dataset
title
string | authors
string | abstract
string | pdf
string | supp
string | arXiv
string | bibtex
string | url
string | detail_url
string | tags
string | string |
|---|---|---|---|---|---|---|---|---|---|---|
Exploring Intra-Class Variation Factors With Learnable Cluster Prompts for Semi-Supervised Image Synthesis | Yunfei Zhang, Xiaoyang Huo, Tianyi Chen, Si Wu, Hau San Wong | Semi-supervised class-conditional image synthesis is typically performed by inferring and injecting class labels into a conditional Generative Adversarial Network (GAN). The supervision in the form of class identity may be inadequate to model classes with diverse visual appearances. In this paper, we propose a Learnable Cluster Prompt-based GAN (LCP-GAN) to capture class-wise characteristics and intra-class variation factors with a broader source of supervision. To exploit partially labeled data, we perform soft partitioning on each class, and explore the possibility of associating intra-class clusters with learnable visual concepts in the feature space of a pre-trained language-vision model, e.g., CLIP. For class-conditional image generation, we design a cluster-conditional generator by injecting a combination of intra-class cluster label embeddings, and further incorporate a real-fake classification head on top of CLIP to distinguish real instances from the synthesized ones, conditioned on the learnable cluster prompts. This significantly strengthens the generator with more semantic language supervision. LCP-GAN not only possesses superior generation capability but also matches the performance of the fully supervised version of the base models: BigGAN and StyleGAN2-ADA, on multiple standard benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Exploring_Intra-Class_Variation_Factors_With_Learnable_Cluster_Prompts_for_Semi-Supervised_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Exploring_Intra-Class_Variation_Factors_With_Learnable_Cluster_Prompts_for_Semi-Supervised_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Exploring_Intra-Class_Variation_Factors_With_Learnable_Cluster_Prompts_for_Semi-Supervised_CVPR_2023_paper.html | CVPR 2023 | null |
NeAT: Learning Neural Implicit Surfaces With Arbitrary Topologies From Multi-View Images | Xiaoxu Meng, Weikai Chen, Bo Yang | Recent progress in neural implicit functions has set new state-of-the-art in reconstructing high-fidelity 3D shapes from a collection of images. However, these approaches are limited to closed surfaces as they require the surface to be represented by a signed distance field. In this paper, we propose NeAT, a new neural rendering framework that can learn implicit surfaces with arbitrary topologies from multi-view images. In particular, NeAT represents the 3D surface as a level set of a signed distance function (SDF) with a validity branch for estimating the surface existence probability at the query positions. We also develop a novel neural volume rendering method, which uses SDF and validity to calculate the volume opacity and avoids rendering points with low validity. NeAT supports easy field-to-mesh conversion using the classic Marching Cubes algorithm. Extensive experiments on DTU, MGN, and Deep Fashion 3D datasets indicate that our approach is able to faithfully reconstruct both watertight and non-watertight surfaces. In particular, NeAT significantly outperforms the state-of-the-art methods in the task of open surface reconstruction both quantitatively and qualitatively. | https://openaccess.thecvf.com/content/CVPR2023/papers/Meng_NeAT_Learning_Neural_Implicit_Surfaces_With_Arbitrary_Topologies_From_Multi-View_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Meng_NeAT_Learning_Neural_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12012 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Meng_NeAT_Learning_Neural_Implicit_Surfaces_With_Arbitrary_Topologies_From_Multi-View_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Meng_NeAT_Learning_Neural_Implicit_Surfaces_With_Arbitrary_Topologies_From_Multi-View_CVPR_2023_paper.html | CVPR 2023 | null |
Quantum Multi-Model Fitting | Matteo Farina, Luca Magri, Willi Menapace, Elisa Ricci, Vladislav Golyanik, Federica Arrigoni | Geometric model fitting is a challenging but fundamental computer vision problem. Recently, quantum optimization has been shown to enhance robust fitting for the case of a single model, while leaving the question of multi-model fitting open. In response to this challenge, this paper shows that the latter case can significantly benefit from quantum hardware and proposes the first quantum approach to multi-model fitting (MMF). We formulate MMF as a problem that can be efficiently sampled by modern adiabatic quantum computers without the relaxation of the objective function. We also propose an iterative and decomposed version of our method, which supports real-world-sized problems. The experimental evaluation demonstrates promising results on a variety of datasets. The source code is available at https://github.com/FarinaMatteo/qmmf. | https://openaccess.thecvf.com/content/CVPR2023/papers/Farina_Quantum_Multi-Model_Fitting_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Farina_Quantum_Multi-Model_Fitting_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15444 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Farina_Quantum_Multi-Model_Fitting_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Farina_Quantum_Multi-Model_Fitting_CVPR_2023_paper.html | CVPR 2023 | null |
SPARF: Neural Radiance Fields From Sparse and Noisy Poses | Prune Truong, Marie-Julie Rakotosaona, Fabian Manhardt, Federico Tombari | Neural Radiance Field (NeRF) has recently emerged as a powerful representation to synthesize photorealistic novel views. While showing impressive performance, it relies on the availability of dense input views with highly accurate camera poses, thus limiting its application in real-world scenarios. In this work, we introduce Sparse Pose Adjusting Radiance Field (SPARF), to address the challenge of novel-view synthesis given only few wide-baseline input images (as low as 3) with noisy camera poses. Our approach exploits multi-view geometry constraints in order to jointly learn the NeRF and refine the camera poses. By relying on pixel matches extracted between the input views, our multi-view correspondence objective enforces the optimized scene and camera poses to converge to a global and geometrically accurate solution. Our depth consistency loss further encourages the reconstructed scene to be consistent from any viewpoint. Our approach sets a new state of the art in the sparse-view regime on multiple challenging datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Truong_SPARF_Neural_Radiance_Fields_From_Sparse_and_Noisy_Poses_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Truong_SPARF_Neural_Radiance_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.11738 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Truong_SPARF_Neural_Radiance_Fields_From_Sparse_and_Noisy_Poses_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Truong_SPARF_Neural_Radiance_Fields_From_Sparse_and_Noisy_Poses_CVPR_2023_paper.html | CVPR 2023 | null |
ABLE-NeRF: Attention-Based Rendering With Learnable Embeddings for Neural Radiance Field | Zhe Jun Tang, Tat-Jen Cham, Haiyu Zhao | Neural Radiance Field (NeRF) is a popular method in representing 3D scenes by optimising a continuous volumetric scene function. Its large success which lies in applying volumetric rendering (VR) is also its Achilles' heel in producing view-dependent effects. As a consequence, glossy and transparent surfaces often appear murky. A remedy to reduce these artefacts is to constrain this VR equation by excluding volumes with back-facing normal. While this approach has some success in rendering glossy surfaces, translucent objects are still poorly represented. In this paper, we present an alternative to the physics-based VR approach by introducing a self-attention-based framework on volumes along a ray. In addition, inspired by modern game engines which utilise Light Probes to store local lighting passing through the scene, we incorporate Learnable Embeddings to capture view dependent effects within the scene. Our method, which we call ABLE-NeRF, significantly reduces 'blurry' glossy surfaces in rendering and produces realistic translucent surfaces which lack in prior art. In the Blender dataset, ABLE-NeRF achieves SOTA results and surpasses Ref-NeRF in all 3 image quality metrics PSNR, SSIM, LPIPS. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tang_ABLE-NeRF_Attention-Based_Rendering_With_Learnable_Embeddings_for_Neural_Radiance_Field_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tang_ABLE-NeRF_Attention-Based_Rendering_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_ABLE-NeRF_Attention-Based_Rendering_With_Learnable_Embeddings_for_Neural_Radiance_Field_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_ABLE-NeRF_Attention-Based_Rendering_With_Learnable_Embeddings_for_Neural_Radiance_Field_CVPR_2023_paper.html | CVPR 2023 | null |
Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution | Jie-En Yao, Li-Yuan Tsao, Yi-Chen Lo, Roy Tseng, Chia-Che Chang, Chun-Yi Lee | Flow-based methods have demonstrated promising results in addressing the ill-posed nature of super-resolution (SR) by learning the distribution of high-resolution (HR) images with the normalizing flow. However, these methods can only perform a predefined fixed-scale SR, limiting their potential in real-world applications. Meanwhile, arbitrary-scale SR has gained more attention and achieved great progress. Nonetheless, previous arbitrary-scale SR methods ignore the ill-posed problem and train the model with per-pixel L1 loss, leading to blurry SR outputs. In this work, we propose "Local Implicit Normalizing Flow" (LINF) as a unified solution to the above problems. LINF models the distribution of texture details under different scaling factors with normalizing flow. Thus, LINF can generate photo-realistic HR images with rich texture details in arbitrary scale factors. We evaluate LINF with extensive experiments and show that LINF achieves the state-of-the-art perceptual quality compared with prior arbitrary-scale SR methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yao_Local_Implicit_Normalizing_Flow_for_Arbitrary-Scale_Image_Super-Resolution_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.05156 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yao_Local_Implicit_Normalizing_Flow_for_Arbitrary-Scale_Image_Super-Resolution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yao_Local_Implicit_Normalizing_Flow_for_Arbitrary-Scale_Image_Super-Resolution_CVPR_2023_paper.html | CVPR 2023 | null |
WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation | Jongheon Jeong, Yang Zou, Taewan Kim, Dongqing Zhang, Avinash Ravichandran, Onkar Dabeer | Visual anomaly classification and segmentation are vital for automating industrial quality inspection. The focus of prior research in the field has been on training custom models for each quality inspection task, which requires task-specific images and annotation. In this paper we move away from this regime, addressing zero-shot and few-normal-shot anomaly classification and segmentation. Recently CLIP, a vision-language model, has shown revolutionary generality with competitive zero/few-shot performance in comparison to full-supervision. But CLIP falls short on anomaly classification and segmentation tasks. Hence, we propose window-based CLIP (WinCLIP) with (1) a compositional ensemble on state words and prompt templates and (2) efficient extraction and aggregation of window/patch/image-level features aligned with text. We also propose its few-normal-shot extension WinCLIP+, which uses complementary information from normal images. In MVTec-AD (and VisA), without further tuning, WinCLIP achieves 91.8%/85.1% (78.1%/79.6%) AUROC in zero-shot anomaly classification and segmentation while WinCLIP+ does 93.1%/95.2% (83.8%/96.4%) in 1-normal-shot, surpassing state-of-the-art by large margins. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jeong_WinCLIP_Zero-Few-Shot_Anomaly_Classification_and_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jeong_WinCLIP_Zero-Few-Shot_Anomaly_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jeong_WinCLIP_Zero-Few-Shot_Anomaly_Classification_and_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jeong_WinCLIP_Zero-Few-Shot_Anomaly_Classification_and_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
PermutoSDF: Fast Multi-View Reconstruction With Implicit Surfaces Using Permutohedral Lattices | Radu Alexandru Rosu, Sven Behnke | Neural radiance-density field methods have become increasingly popular for the task of novel-view rendering. Their recent extension to hash-based positional encoding ensures fast training and inference with visually pleasing results. However, density-based methods struggle with recovering accurate surface geometry. Hybrid methods alleviate this issue by optimizing the density based on an underlying SDF. However, current SDF methods are overly smooth and miss fine geometric details. In this work, we combine the strengths of these two lines of work in a novel hash-based implicit surface representation. We propose improvements to the two areas by replacing the voxel hash encoding with a permutohedral lattice which optimizes faster, especially for higher dimensions. We additionally propose a regularization scheme which is crucial for recovering high-frequency geometric detail. We evaluate our method on multiple datasets and show that we can recover geometric detail at the level of pores and wrinkles while using only RGB images for supervision. Furthermore, using sphere tracing we can render novel views at 30 fps on an RTX 3090. Code is publicly available at https://radualexandru.github.io/permuto_sdf | https://openaccess.thecvf.com/content/CVPR2023/papers/Rosu_PermutoSDF_Fast_Multi-View_Reconstruction_With_Implicit_Surfaces_Using_Permutohedral_Lattices_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Rosu_PermutoSDF_Fast_Multi-View_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2211.12562 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Rosu_PermutoSDF_Fast_Multi-View_Reconstruction_With_Implicit_Surfaces_Using_Permutohedral_Lattices_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Rosu_PermutoSDF_Fast_Multi-View_Reconstruction_With_Implicit_Surfaces_Using_Permutohedral_Lattices_CVPR_2023_paper.html | CVPR 2023 | null |
TriDet: Temporal Action Detection With Relative Boundary Modeling | Dingfeng Shi, Yujie Zhong, Qiong Cao, Lin Ma, Jia Li, Dacheng Tao | In this paper, we present a one-stage framework TriDet for temporal action detection. Existing methods often suffer from imprecise boundary predictions due to the ambiguous action boundaries in videos. To alleviate this problem, we propose a novel Trident-head to model the action boundary via an estimated relative probability distribution around the boundary. In the feature pyramid of TriDet, we propose a Scalable-Granularity Perception (SGP) layer to aggregate information across different temporal granularities, which is much more efficient than the recent transformer-based feature pyramid. Benefiting from the Trident-head and the SGP-based feature pyramid, TriDet achieves state-of-the-art performance on three challenging benchmarks: THUMOS14, HACS and EPIC-KITCHEN 100, with lower computational costs, compared to previous methods. For example, TriDet hits an average mAP of 69.3% on THUMOS14, outperforming the previous best by 2.5%, but with only 74.6% of its latency. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shi_TriDet_Temporal_Action_Detection_With_Relative_Boundary_Modeling_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shi_TriDet_Temporal_Action_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.07347 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shi_TriDet_Temporal_Action_Detection_With_Relative_Boundary_Modeling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shi_TriDet_Temporal_Action_Detection_With_Relative_Boundary_Modeling_CVPR_2023_paper.html | CVPR 2023 | null |
Detection Hub: Unifying Object Detection Datasets via Query Adaptation on Language Embedding | Lingchen Meng, Xiyang Dai, Yinpeng Chen, Pengchuan Zhang, Dongdong Chen, Mengchen Liu, Jianfeng Wang, Zuxuan Wu, Lu Yuan, Yu-Gang Jiang | Combining multiple datasets enables performance boost on many computer vision tasks. But similar trend has not been witnessed in object detection when combining multiple datasets due to two inconsistencies among detection datasets: taxonomy difference and domain gap. In this paper, we address these challenges by a new design (named Detection Hub) that is dataset-aware and category-aligned. It not only mitigates the dataset inconsistency but also provides coherent guidance for the detector to learn across multiple datasets. In particular, the dataset-aware design is achieved by learning a dataset embedding that is used to adapt object queries as well as convolutional kernels in detection heads. The categories across datasets are semantically aligned into a unified space by replacing one-hot category representations with word embedding and leveraging the semantic coherence of language embedding. Detection Hub fulfills the benefits of large data on object detection. Experiments demonstrate that joint training on multiple datasets achieves significant performance gains over training on each dataset alone. Detection Hub further achieves SoTA performance on UODB benchmark with wide variety of datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Meng_Detection_Hub_Unifying_Object_Detection_Datasets_via_Query_Adaptation_on_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2206.03484 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Meng_Detection_Hub_Unifying_Object_Detection_Datasets_via_Query_Adaptation_on_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Meng_Detection_Hub_Unifying_Object_Detection_Datasets_via_Query_Adaptation_on_CVPR_2023_paper.html | CVPR 2023 | null |
Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to-Image Diffusion Models | Jiale Xu, Xintao Wang, Weihao Cheng, Yan-Pei Cao, Ying Shan, Xiaohu Qie, Shenghua Gao | Recent CLIP-guided 3D optimization methods, such as DreamFields and PureCLIPNeRF, have achieved impressive results in zero-shot text-to-3D synthesis. However, due to scratch training and random initialization without prior knowledge, these methods often fail to generate accurate and faithful 3D structures that conform to the input text. In this paper, we make the first attempt to introduce explicit 3D shape priors into the CLIP-guided 3D optimization process. Specifically, we first generate a high-quality 3D shape from the input text in the text-to-shape stage as a 3D shape prior. We then use it as the initialization of a neural radiance field and optimize it with the full prompt. To address the challenging text-to-shape generation task, we present a simple yet effective approach that directly bridges the text and image modalities with a powerful text-to-image diffusion model. To narrow the style domain gap between the images synthesized by the text-to-image diffusion model and shape renderings used to train the image-to-shape generator, we further propose to jointly optimize a learnable text prompt and fine-tune the text-to-image diffusion model for rendering-style image generation. Our method, Dream3D, is capable of generating imaginative 3D content with superior visual quality and shape accuracy compared to state-of-the-art methods. Our project page is at https://bluestyle97.github.io/dream3d/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Dream3D_Zero-Shot_Text-to-3D_Synthesis_Using_3D_Shape_Prior_and_Text-to-Image_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Dream3D_Zero-Shot_Text-to-3D_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.14704 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Dream3D_Zero-Shot_Text-to-3D_Synthesis_Using_3D_Shape_Prior_and_Text-to-Image_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Dream3D_Zero-Shot_Text-to-3D_Synthesis_Using_3D_Shape_Prior_and_Text-to-Image_CVPR_2023_paper.html | CVPR 2023 | null |
Adversarial Normalization: I Can Visualize Everything (ICE) | Hoyoung Choi, Seungwan Jin, Kyungsik Han | Vision transformers use [CLS] tokens to predict image classes. Their explainability visualization has been studied using relevant information from [CLS] tokens or focusing on attention scores during self-attention. Such visualization, however, is challenging because of the dependence of the structure of a vision transformer on skip connections and attention operators, the instability of non-linearities in the learning process, and the limited reflection of self-attention scores on relevance. We argue that the output vectors for each input patch token in a vision transformer retain the image information of each patch location, which can facilitate the prediction of an image class. In this paper, we propose ICE (Adversarial Normalization: I Can visualize Everything), a novel method that enables a model to directly predict a class for each patch in an image; thus, advancing the effective visualization of the explainability of a vision transformer. Our method distinguishes background from foreground regions by predicting background classes for patches that do not determine image classes. We used the DeiT-S model, the most representative model employed in studies, on the explainability visualization of vision transformers. On the ImageNet-Segmentation dataset, ICE outperformed all explainability visualization methods for four cases depending on the model size. We also conducted quantitative and qualitative analyses on the tasks of weakly-supervised object localization and unsupervised object discovery. On the CUB-200-2011 and PASCALVOC07/12 datasets, ICE achieved comparable performance to the state-of-the-art methods. We incorporated ICE into the encoder of DeiT-S and improved efficiency by 44.01% on the ImageNet dataset over that achieved by the original DeiT-S model. We showed performance on the accuracy and efficiency comparable to EViT, the state-of-the-art pruning model, demonstrating the effectiveness of ICE. The code is available at https://github.com/Hanyang-HCC-Lab/ICE. | https://openaccess.thecvf.com/content/CVPR2023/papers/Choi_Adversarial_Normalization_I_Can_Visualize_Everything_ICE_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Choi_Adversarial_Normalization_I_Can_Visualize_Everything_ICE_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Choi_Adversarial_Normalization_I_Can_Visualize_Everything_ICE_CVPR_2023_paper.html | CVPR 2023 | null |
Reinforcement Learning-Based Black-Box Model Inversion Attacks | Gyojin Han, Jaehyun Choi, Haeil Lee, Junmo Kim | Model inversion attacks are a type of privacy attack that reconstructs private data used to train a machine learning model, solely by accessing the model. Recently, white-box model inversion attacks leveraging Generative Adversarial Networks (GANs) to distill knowledge from public datasets have been receiving great attention because of their excellent attack performance. On the other hand, current black-box model inversion attacks that utilize GANs suffer from issues such as being unable to guarantee the completion of the attack process within a predetermined number of query accesses or achieve the same level of performance as white-box attacks. To overcome these limitations, we propose a reinforcement learning-based black-box model inversion attack. We formulate the latent space search as a Markov Decision Process (MDP) problem and solve it with reinforcement learning. Our method utilizes the confidence scores of the generated images to provide rewards to an agent. Finally, the private data can be reconstructed using the latent vectors found by the agent trained in the MDP. The experiment results on various datasets and models demonstrate that our attack successfully recovers the private information of the target model by achieving state-of-the-art attack performance. We emphasize the importance of studies on privacy-preserving machine learning by proposing a more advanced black-box model inversion attack. | https://openaccess.thecvf.com/content/CVPR2023/papers/Han_Reinforcement_Learning-Based_Black-Box_Model_Inversion_Attacks_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Han_Reinforcement_Learning-Based_Black-Box_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.04625 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Han_Reinforcement_Learning-Based_Black-Box_Model_Inversion_Attacks_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Han_Reinforcement_Learning-Based_Black-Box_Model_Inversion_Attacks_CVPR_2023_paper.html | CVPR 2023 | null |
Learning a Deep Color Difference Metric for Photographic Images | Haoyu Chen, Zhihua Wang, Yang Yang, Qilin Sun, Kede Ma | Most well-established and widely used color difference (CD) metrics are handcrafted and subject-calibrated against uniformly colored patches, which do not generalize well to photographic images characterized by natural scene complexities. Constructing CD formulae for photographic images is still an active research topic in imaging/illumination, vision science, and color science communities. In this paper, we aim to learn a deep CD metric for photographic images with four desirable properties. First, it well aligns with the observations in vision science that color and form are linked inextricably in visual cortical processing. Second, it is a proper metric in the mathematical sense. Third, it computes accurate CDs between photographic images, differing mainly in color appearances. Fourth, it is robust to mild geometric distortions (e.g., translation or due to parallax), which are often present in photographic images of the same scene captured by different digital cameras. We show that all these properties can be satisfied at once by learning a multi-scale autoregressive normalizing flow for feature transform, followed by the Euclidean distance which is linearly proportional to the human perceptual CD. Quantitative and qualitative experiments on the large-scale SPCD dataset demonstrate the promise of the learned CD metric. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Learning_a_Deep_Color_Difference_Metric_for_Photographic_Images_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.14964 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Learning_a_Deep_Color_Difference_Metric_for_Photographic_Images_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Learning_a_Deep_Color_Difference_Metric_for_Photographic_Images_CVPR_2023_paper.html | CVPR 2023 | null |
1000 FPS HDR Video With a Spike-RGB Hybrid Camera | Yakun Chang, Chu Zhou, Yuchen Hong, Liwen Hu, Chao Xu, Tiejun Huang, Boxin Shi | Capturing high frame rate and high dynamic range (HFR&HDR) color videos in high-speed scenes with conventional frame-based cameras is very challenging. The increasing frame rate is usually guaranteed by using shorter exposure time so that the captured video is severely interfered by noise. Alternating exposures could alleviate the noise issue but sacrifice frame rate due to involving long-exposure frames. The neuromorphic spiking camera records high-speed scenes of high dynamic range without colors using a completely different sensing mechanism and visual representation. We introduce a hybrid camera system composed of a spiking and an alternating-exposure RGB camera to capture HFR&HDR scenes with high fidelity. Our insight is to bring each camera's superiority into full play. The spike frames, with accurate fast motion information encoded, are first reconstructed for motion representation, from which the spike-based optical flows guide the recovery of missing temporal information for middle- and long-exposure RGB images while retaining their reliable color appearances. With the strong temporal constraint estimated from spike trains, both missing and distorted colors cross RGB frames are recovered to generate time-consistent and HFR color frames. We collect a new Spike-RGB dataset that contains 300 sequences of synthetic data and 20 groups of real-world data to demonstrate 1000 FPS HDR videos outperforming HDR video reconstruction methods and commercial high-speed cameras. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chang_1000_FPS_HDR_Video_With_a_Spike-RGB_Hybrid_Camera_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chang_1000_FPS_HDR_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chang_1000_FPS_HDR_Video_With_a_Spike-RGB_Hybrid_Camera_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chang_1000_FPS_HDR_Video_With_a_Spike-RGB_Hybrid_Camera_CVPR_2023_paper.html | CVPR 2023 | null |
DINN360: Deformable Invertible Neural Network for Latitude-Aware 360deg Image Rescaling | Yichen Guo, Mai Xu, Lai Jiang, Leonid Sigal, Yunjin Chen | With the rapid development of virtual reality, 360deg images have gained increasing popularity. Their wide field of view necessitates high resolution to ensure image quality. This, however, makes it harder to acquire, store and even process such 360deg images. To alleviate this issue, we propose the first attempt at 360deg image rescaling, which refers to downscaling a 360deg image to a visually valid low-resolution (LR) counterpart and then upscaling to a high-resolution (HR) 360deg image given the LR variant. Specifically, we first analyze two 360deg image datasets and observe several findings that characterize how 360deg images typically change along their latitudes. Inspired by these findings, we propose a novel deformable invertible neural network (INN), named DINN360, for latitude-aware 360deg image rescaling. In DINN360, a deformable INN is designed to downscale the LR image, and project the high-frequency (HF) component to the latent space by adaptively handling various deformations occurring at different latitude regions. Given the downscaled LR image, the high-quality HR image is then reconstructed in a conditional latitude-aware manner by recovering the structure-related HF component from the latent space. Extensive experiments over four public datasets show that our DINN360 method performs considerably better than other state-of-the-art methods for 2x, 4x and 8x 360deg image rescaling. | https://openaccess.thecvf.com/content/CVPR2023/papers/Guo_DINN360_Deformable_Invertible_Neural_Network_for_Latitude-Aware_360deg_Image_Rescaling_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Guo_DINN360_Deformable_Invertible_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_DINN360_Deformable_Invertible_Neural_Network_for_Latitude-Aware_360deg_Image_Rescaling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_DINN360_Deformable_Invertible_Neural_Network_for_Latitude-Aware_360deg_Image_Rescaling_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Geometric-Aware Properties in 2D Representation Using Lightweight CAD Models, or Zero Real 3D Pairs | Pattaramanee Arsomngern, Sarana Nutanong, Supasorn Suwajanakorn | Cross-modal training using 2D-3D paired datasets, such as those containing multi-view images and 3D scene scans, presents an effective way to enhance 2D scene understanding by introducing geometric and view-invariance priors into 2D features. However, the need for large-scale scene datasets can impede scalability and further improvements. This paper explores an alternative learning method by leveraging a lightweight and publicly available type of 3D data in the form of CAD models. We construct a 3D space with geometric-aware alignment where the similarity in this space reflects the geometric similarity of CAD models based on the Chamfer distance. The acquired geometric-aware properties are then induced into 2D features, which boost performance on downstream tasks more effectively than existing RGB-CAD approaches. Our technique is not limited to paired RGB-CAD datasets. By training exclusively on pseudo pairs generated from CAD-based reconstruction methods, we enhance the performance of SOTA 2D pre-trained models that use ResNet-50 or ViT-B backbones on various 2D understanding tasks. We also achieve comparable results to SOTA methods trained on scene scans on four tasks in NYUv2, SUNRGB-D, indoor ADE20k, and indoor/outdoor COCO, despite using lightweight CAD models or pseudo data. | https://openaccess.thecvf.com/content/CVPR2023/papers/Arsomngern_Learning_Geometric-Aware_Properties_in_2D_Representation_Using_Lightweight_CAD_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Arsomngern_Learning_Geometric-Aware_Properties_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Arsomngern_Learning_Geometric-Aware_Properties_in_2D_Representation_Using_Lightweight_CAD_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Arsomngern_Learning_Geometric-Aware_Properties_in_2D_Representation_Using_Lightweight_CAD_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Texts as Images in Prompt Tuning for Multi-Label Image Recognition | Zixian Guo, Bowen Dong, Zhilong Ji, Jinfeng Bai, Yiwen Guo, Wangmeng Zuo | Prompt tuning has been employed as an efficient way to adapt large vision-language pre-trained models (e.g. CLIP) to various downstream tasks in data-limited or label-limited settings. Nonetheless, visual data (e.g., images) is by default prerequisite for learning prompts in existing methods. In this work, we advocate that the effectiveness of image-text contrastive learning in aligning the two modalities (for training CLIP) further makes it feasible to treat texts as images for prompt tuning and introduce TaI prompting. In contrast to the visual data, text descriptions are easy to collect, and their class labels can be directly derived. Particularly, we apply TaI prompting to multi-label image recognition, where sentences in the wild serve as alternatives to images for prompt tuning. Moreover, with TaI, double-grained prompt tuning (TaI-DPT) is further presented to extract both coarse-grained and fine-grained embeddings for enhancing the multi-label recognition performance. Experimental results show that our proposed TaI-DPT outperforms zero-shot CLIP by a large margin on multiple benchmarks, e.g., MS-COCO, VOC2007, and NUS-WIDE, while it can be combined with existing methods of prompting from images to improve recognition performance further. The code is released at https://github.com/guozix/TaI-DPT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Guo_Texts_as_Images_in_Prompt_Tuning_for_Multi-Label_Image_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Guo_Texts_as_Images_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2211.12739 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_Texts_as_Images_in_Prompt_Tuning_for_Multi-Label_Image_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_Texts_as_Images_in_Prompt_Tuning_for_Multi-Label_Image_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Self-Correctable and Adaptable Inference for Generalizable Human Pose Estimation | Zhehan Kan, Shuoshuo Chen, Ce Zhang, Yushun Tang, Zhihai He | A central challenge in human pose estimation, as well as in many other machine learning and prediction tasks, is the generalization problem. The learned network does not have the capability to characterize the prediction error, generate feedback information from the test sample, and correct the prediction error on the fly for each individual test sample, which results in degraded performance in generalization. In this work, we introduce a self-correctable and adaptable inference (SCAI) method to address the generalization challenge of network prediction and use human pose estimation as an example to demonstrate its effectiveness and performance. We learn a correction network to correct the prediction result conditioned by a fitness feedback error. This feedback error is generated by a learned fitness feedback network which maps the prediction result to the original input domain and compares it against the original input. Interestingly, we find that this self-referential feedback error is highly correlated with the actual prediction error. This strong correlation suggests that we can use this error as feedback to guide the correction process. It can be also used as a loss function to quickly adapt and optimize the correction network during the inference process. Our extensive experimental results on human pose estimation demonstrate that the proposed SCAI method is able to significantly improve the generalization capability and performance of human pose estimation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kan_Self-Correctable_and_Adaptable_Inference_for_Generalizable_Human_Pose_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kan_Self-Correctable_and_Adaptable_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11180 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kan_Self-Correctable_and_Adaptable_Inference_for_Generalizable_Human_Pose_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kan_Self-Correctable_and_Adaptable_Inference_for_Generalizable_Human_Pose_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
Few-Shot Learning With Visual Distribution Calibration and Cross-Modal Distribution Alignment | Runqi Wang, Hao Zheng, Xiaoyue Duan, Jianzhuang Liu, Yuning Lu, Tian Wang, Songcen Xu, Baochang Zhang | Pre-trained vision-language models have inspired much research on few-shot learning. However, with only a few training images, there exist two crucial problems: (1) the visual feature distributions are easily distracted by class-irrelevant information in images, and (2) the alignment between the visual and language feature distributions is difficult. To deal with the distraction problem, we propose a Selective Attack module, which consists of trainable adapters that generate spatial attention maps of images to guide the attacks on class-irrelevant image areas. By messing up these areas, the critical features are captured and the visual distributions of image features are calibrated. To better align the visual and language feature distributions that describe the same object class, we propose a cross-modal distribution alignment module, in which we introduce a vision-language prototype for each class to align the distributions, and adopt the Earth Mover's Distance (EMD) to optimize the prototypes. For efficient computation, the upper bound of EMD is derived. In addition, we propose an augmentation strategy to increase the diversity of the images and the text prompts, which can reduce overfitting to the few-shot training images. Extensive experiments on 11 datasets demonstrate that our method consistently outperforms prior arts in few-shot learning. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Few-Shot_Learning_With_Visual_Distribution_Calibration_and_Cross-Modal_Distribution_Alignment_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Few-Shot_Learning_With_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Few-Shot_Learning_With_Visual_Distribution_Calibration_and_Cross-Modal_Distribution_Alignment_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Few-Shot_Learning_With_Visual_Distribution_Calibration_and_Cross-Modal_Distribution_Alignment_CVPR_2023_paper.html | CVPR 2023 | null |
Referring Multi-Object Tracking | Dongming Wu, Wencheng Han, Tiancai Wang, Xingping Dong, Xiangyu Zhang, Jianbing Shen | Existing referring understanding tasks tend to involve the detection of a single text-referred object. In this paper, we propose a new and general referring understanding task, termed referring multi-object tracking (RMOT). Its core idea is to employ a language expression as a semantic cue to guide the prediction of multi-object tracking. To the best of our knowledge, it is the first work to achieve an arbitrary number of referent object predictions in videos. To push forward RMOT, we construct one benchmark with scalable expressions based on KITTI, named Refer-KITTI. Specifically, it provides 18 videos with 818 expressions, and each expression in a video is annotated with an average of 10.7 objects. Further, we develop a transformer-based architecture TransRMOT to tackle the new task in an online manner, which achieves impressive detection performance and outperforms other counterparts. The Refer-KITTI dataset and the code are released at https://referringmot.github.io. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Referring_Multi-Object_Tracking_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_Referring_Multi-Object_Tracking_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.03366 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Referring_Multi-Object_Tracking_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Referring_Multi-Object_Tracking_CVPR_2023_paper.html | CVPR 2023 | null |
Finetune Like You Pretrain: Improved Finetuning of Zero-Shot Vision Models | Sachin Goyal, Ananya Kumar, Sankalp Garg, Zico Kolter, Aditi Raghunathan | Finetuning image-text models such as CLIP achieves state-of-the-art accuracies on a variety of benchmarks. However, recent works (Kumar et al., 2022; Wortsman et al., 2021) have shown that even subtle differences in the finetuning process can lead to surprisingly large differences in the final performance, both for in-distribution (ID) and out-of-distribution (OOD) data. In this work, we show that a natural and simple approach of mimicking contrastive pretraining consistently outperforms alternative finetuning approaches. Specifically, we cast downstream class labels as text prompts and continue optimizing the contrastive loss between image embeddings and class-descriptive prompt embeddings (contrastive finetuning). Our method consistently outperforms baselines across 7 distribution shift, 6 transfer learning, and 3 few-shot learning benchmarks. On WILDS-iWILDCam, our proposed approach FLYP outperforms the top of the leaderboard by 2.3% ID and 2.7% OOD, giving the highest reported accuracy. Averaged across 7 OOD datasets (2 WILDS and 5 ImageNet associated shifts), FLYP gives gains of 4.2% OOD over standard finetuning and outperforms current state-ofthe-art (LP-FT) by more than 1% both ID and OOD. Similarly, on 3 few-shot learning benchmarks, FLYP gives gains up to 4.6% over standard finetuning and 4.4% over the state-of-the-art. Thus we establish our proposed method of contrastive finetuning as a simple and intuitive state-ofthe-art for supervised finetuning of image-text models like CLIP. Code is available at https://github.com/locuslab/FLYP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Goyal_Finetune_Like_You_Pretrain_Improved_Finetuning_of_Zero-Shot_Vision_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Goyal_Finetune_Like_You_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.00638 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Goyal_Finetune_Like_You_Pretrain_Improved_Finetuning_of_Zero-Shot_Vision_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Goyal_Finetune_Like_You_Pretrain_Improved_Finetuning_of_Zero-Shot_Vision_Models_CVPR_2023_paper.html | CVPR 2023 | null |
GradMA: A Gradient-Memory-Based Accelerated Federated Learning With Alleviated Catastrophic Forgetting | Kangyang Luo, Xiang Li, Yunshi Lan, Ming Gao | Federated Learning (FL) has emerged as a de facto machine learning area and received rapid increasing research interests from the community. However, catastrophic forgetting caused by data heterogeneity and partial participation poses distinctive challenges for FL, which are detrimental to the performance. To tackle the problems, we propose a new FL approach (namely GradMA), which takes inspiration from continual learning to simultaneously correct the server-side and worker-side update directions as well as take full advantage of server's rich computing and memory resources. Furthermore, we elaborate a memory reduction strategy to enable GradMA to accommodate FL with a large scale of workers. We then analyze convergence of GradMA theoretically under the smooth non-convex setting and show that its convergence rate achieves a linear speed up w.r.t the increasing number of sampled active workers. At last, our extensive experiments on various image classification tasks show that GradMA achieves significant performance gains in accuracy and communication efficiency compared to SOTA baselines. We provide our code here: https://github.com/lkyddd/GradMA. | https://openaccess.thecvf.com/content/CVPR2023/papers/Luo_GradMA_A_Gradient-Memory-Based_Accelerated_Federated_Learning_With_Alleviated_Catastrophic_Forgetting_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Luo_GradMA_A_Gradient-Memory-Based_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.14307 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_GradMA_A_Gradient-Memory-Based_Accelerated_Federated_Learning_With_Alleviated_Catastrophic_Forgetting_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_GradMA_A_Gradient-Memory-Based_Accelerated_Federated_Learning_With_Alleviated_Catastrophic_Forgetting_CVPR_2023_paper.html | CVPR 2023 | null |
Weakly Supervised Temporal Sentence Grounding With Uncertainty-Guided Self-Training | Yifei Huang, Lijin Yang, Yoichi Sato | The task of weakly supervised temporal sentence grounding aims at finding the corresponding temporal moments of a language description in the video, given video-language correspondence only at video-level. Most existing works select mismatched video-language pairs as negative samples and train the model to generate better positive proposals that are distinct from the negative ones. However, due to the complex temporal structure of videos, proposals distinct from the negative ones may correspond to several video segments but not necessarily the correct ground truth. To alleviate this problem, we propose an uncertainty-guided self-training technique to provide extra self-supervision signals to guide the weakly-supervised learning. The self-training process is based on teacher-student mutual learning with weak-strong augmentation, which enables the teacher network to generate relatively more reliable outputs compared to the student network, so that the student network can learn from the teacher's output. Since directly applying existing self-training methods in this task easily causes error accumulation, we specifically design two techniques in our self-training method: (1) we construct a Bayesian teacher network, leveraging its uncertainty as a weight to suppress the noisy teacher supervisory signals; (2) we leverage the cycle consistency brought by temporal data augmentation to perform mutual learning between the two networks. Experiments demonstrate our method's superiority on Charades-STA and ActivityNet Captions datasets. We also show in the experiment that our self-training method can be applied to improve the performance of multiple backbone methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Weakly_Supervised_Temporal_Sentence_Grounding_With_Uncertainty-Guided_Self-Training_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Weakly_Supervised_Temporal_Sentence_Grounding_With_Uncertainty-Guided_Self-Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Weakly_Supervised_Temporal_Sentence_Grounding_With_Uncertainty-Guided_Self-Training_CVPR_2023_paper.html | CVPR 2023 | null |
Hint-Aug: Drawing Hints From Foundation Vision Transformers Towards Boosted Few-Shot Parameter-Efficient Tuning | Zhongzhi Yu, Shang Wu, Yonggan Fu, Shunyao Zhang, Yingyan (Celine) Lin | Despite the growing demand for tuning foundation vision transformers (FViTs) on downstream tasks, fully unleashing FViTs' potential under data-limited scenarios (e.g., few-shot tuning) remains a challenge due to FViTs' data-hungry nature. Common data augmentation techniques fall short in this context due to the limited features contained in the few-shot tuning data. To tackle this challenge, we first identify an opportunity for FViTs in few-shot tuning: pretrained FViTs themselves have already learned highly representative features from large-scale pretraining data, which are fully preserved during widely used parameter-efficient tuning. We thus hypothesize that leveraging those learned features to augment the tuning data can boost the effectiveness of few-shot FViT tuning. To this end, we propose a framework called Hint-based Data Augmentation (Hint-Aug), which aims to boost FViT in few-shot tuning by augmenting the over-fitted parts of tuning samples with the learned features of pretrained FViTs. Specifically, Hint-Aug integrates two key enablers: (1) an Attentive Over-fitting Detector (AOD) to detect over-confident patches of foundation ViTs for potentially alleviating their over-fitting on the few-shot tuning data and (2) a Confusion-based Feature Infusion (CFI) module to infuse easy-to-confuse features from the pretrained FViTs with the over-confident patches detected by the above AOD in order to enhance the feature diversity during tuning. Extensive experiments and ablation studies on five datasets and three parameter-efficient tuning techniques consistently validate Hint-Aug's effectiveness: 0.04% 32.91% higher accuracy over the state-of-the-art (SOTA) data augmentation method under various low-shot settings. For example, on the Pet dataset, Hint-Aug achieves a 2.22% higher accuracy with 50% less training data over SOTA data augmentation methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Hint-Aug_Drawing_Hints_From_Foundation_Vision_Transformers_Towards_Boosted_Few-Shot_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Hint-Aug_Drawing_Hints_From_Foundation_Vision_Transformers_Towards_Boosted_Few-Shot_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Hint-Aug_Drawing_Hints_From_Foundation_Vision_Transformers_Towards_Boosted_Few-Shot_CVPR_2023_paper.html | CVPR 2023 | null |
A Strong Baseline for Generalized Few-Shot Semantic Segmentation | Sina Hajimiri, Malik Boudiaf, Ismail Ben Ayed, Jose Dolz | This paper introduces a generalized few-shot segmentation framework with a straightforward training process and an easy-to-optimize inference phase. In particular, we propose a simple yet effective model based on the well-known InfoMax principle, where the Mutual Information (MI) between the learned feature representations and their corresponding predictions is maximized. In addition, the terms derived from our MI-based formulation are coupled with a knowledge distillation term to retain the knowledge on base classes. With a simple training process, our inference model can be applied on top of any segmentation network trained on base classes. The proposed inference yields substantial improvements on the popular few-shot segmentation benchmarks, PASCAL-5^i and COCO-20^i. Particularly, for novel classes, the improvement gains range from 7% to 26% (PASCAL-5^i) and from 3% to 12% (COCO-20^i) in the 1-shot and 5-shot scenarios, respectively. Furthermore, we propose a more challenging setting, where performance gaps are further exacerbated. Our code is publicly available at https://github.com/sinahmr/DIaM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hajimiri_A_Strong_Baseline_for_Generalized_Few-Shot_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hajimiri_A_Strong_Baseline_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.14126 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hajimiri_A_Strong_Baseline_for_Generalized_Few-Shot_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hajimiri_A_Strong_Baseline_for_Generalized_Few-Shot_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
AutoRecon: Automated 3D Object Discovery and Reconstruction | Yuang Wang, Xingyi He, Sida Peng, Haotong Lin, Hujun Bao, Xiaowei Zhou | A fully automated object reconstruction pipeline is crucial for digital content creation. While the area of 3D reconstruction has witnessed profound developments, the removal of background to obtain a clean object model still relies on different forms of manual labor, such as bounding box labeling, mask annotations, and mesh manipulations. In this paper, we propose a novel framework named AutoRecon for the automated discovery and reconstruction of an object from multi-view images. We demonstrate that foreground objects can be robustly located and segmented from SfM point clouds by leveraging self-supervised 2D vision transformer features. Then, we reconstruct decomposed neural scene representations with dense supervision provided by the decomposed point clouds, resulting in accurate object reconstruction and segmentation. Experiments on the DTU, BlendedMVS and CO3D-V2 datasets demonstrate the effectiveness and robustness of AutoRecon. The code and supplementary material are available on the project page: https://zju3dv.github.io/autorecon/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_AutoRecon_Automated_3D_Object_Discovery_and_Reconstruction_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2305.08810 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_AutoRecon_Automated_3D_Object_Discovery_and_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_AutoRecon_Automated_3D_Object_Discovery_and_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
POTTER: Pooling Attention Transformer for Efficient Human Mesh Recovery | Ce Zheng, Xianpeng Liu, Guo-Jun Qi, Chen Chen | Transformer architectures have achieved SOTA performance on the human mesh recovery (HMR) from monocular images. However, the performance gain has come at the cost of substantial memory and computational overhead. A lightweight and efficient model to reconstruct accurate human mesh is needed for real-world applications. In this paper, we propose a pure transformer architecture named POoling aTtention TransformER (POTTER) for the HMR task from single images. Observing that the conventional attention module is memory and computationally expensive, we propose an efficient pooling attention module, which significantly reduces the memory and computational cost without sacrificing performance. Furthermore, we design a new transformer architecture by integrating a High-Resolution (HR) stream for the HMR task. The high-resolution local and global features from the HR stream can be utilized for recovering more accurate human mesh. Our POTTER outperforms the SOTA method METRO by only requiring 7% of total parameters and 14% of the Multiply-Accumulate Operations on the Human3.6M (PA-MPJPE) and 3DPW (all three metrics) datasets. Code will be publicly available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zheng_POTTER_Pooling_Attention_Transformer_for_Efficient_Human_Mesh_Recovery_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zheng_POTTER_Pooling_Attention_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.13357 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_POTTER_Pooling_Attention_Transformer_for_Efficient_Human_Mesh_Recovery_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_POTTER_Pooling_Attention_Transformer_for_Efficient_Human_Mesh_Recovery_CVPR_2023_paper.html | CVPR 2023 | null |
Learning a Practical SDR-to-HDRTV Up-Conversion Using New Dataset and Degradation Models | Cheng Guo, Leidong Fan, Ziyu Xue, Xiuhua Jiang | In media industry, the demand of SDR-to-HDRTV up-conversion arises when users possess HDR-WCG (high dynamic range-wide color gamut) TVs while most off-the-shelf footage is still in SDR (standard dynamic range). The research community has started tackling this low-level vision task by learning-based approaches. When applied to real SDR, yet, current methods tend to produce dim and desaturated result, making nearly no improvement on viewing experience. Different from other network-oriented methods, we attribute such deficiency to training set (HDR-SDR pair). Consequently, we propose new HDRTV dataset (dubbed HDRTV4K) and new HDR-to-SDR degradation models. Then, it's used to train a luminance-segmented network (LSN) consisting of a global mapping trunk, and two Transformer branches on bright and dark luminance range. We also update assessment criteria by tailored metrics and subjective experiment. Finally, ablation studies are conducted to prove the effectiveness. Our work is available at: https://github.com/AndreGuo/HDRTVDM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Guo_Learning_a_Practical_SDR-to-HDRTV_Up-Conversion_Using_New_Dataset_and_Degradation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Guo_Learning_a_Practical_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13031 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_Learning_a_Practical_SDR-to-HDRTV_Up-Conversion_Using_New_Dataset_and_Degradation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_Learning_a_Practical_SDR-to-HDRTV_Up-Conversion_Using_New_Dataset_and_Degradation_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Detailed Radiance Manifolds for High-Fidelity and 3D-Consistent Portrait Synthesis From Monocular Image | Yu Deng, Baoyuan Wang, Heung-Yeung Shum | A key challenge for novel view synthesis of monocular portrait images is 3D consistency under continuous pose variations. Most existing methods rely on 2D generative models which often leads to obvious 3D inconsistency artifacts. We present a 3D-consistent novel view synthesis approach for monocular portrait images based on a recent proposed 3D-aware GAN, namely Generative Radiance Manifolds (GRAM), which has shown strong 3D consistency at multiview image generation of virtual subjects via the radiance manifolds representation. However, simply learning an encoder to map a real image into the latent space of GRAM can only reconstruct coarse radiance manifolds without faithful fine details, while improving the reconstruction fidelity via instance-specific optimization is time-consuming. We introduce a novel detail manifolds reconstructor to learn 3D-consistent fine details on the radiance manifolds from monocular images, and combine them with the coarse radiance manifolds for high-fidelity reconstruction. The 3D priors derived from the coarse radiance manifolds are used to regulate the learned details to ensure reasonable synthesized results at novel views. Trained on in-the-wild 2D images, our method achieves high-fidelity and 3D-consistent portrait synthesis largely outperforming the prior art. Project page: https://yudeng.github.io/GRAMInverter/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Deng_Learning_Detailed_Radiance_Manifolds_for_High-Fidelity_and_3D-Consistent_Portrait_Synthesis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Deng_Learning_Detailed_Radiance_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.13901 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Deng_Learning_Detailed_Radiance_Manifolds_for_High-Fidelity_and_3D-Consistent_Portrait_Synthesis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Deng_Learning_Detailed_Radiance_Manifolds_for_High-Fidelity_and_3D-Consistent_Portrait_Synthesis_CVPR_2023_paper.html | CVPR 2023 | null |
Patch-Craft Self-Supervised Training for Correlated Image Denoising | Gregory Vaksman, Michael Elad | Supervised neural networks are known to achieve excellent results in various image restoration tasks. However, such training requires datasets composed of pairs of corrupted images and their corresponding ground truth targets. Unfortunately, such data is not available in many applications. For the task of image denoising in which the noise statistics is unknown, several self-supervised training methods have been proposed for overcoming this difficulty. Some of these require knowledge of the noise model, while others assume that the contaminating noise is uncorrelated, both assumptions are too limiting for many practical needs. This work proposes a novel self-supervised training technique suitable for the removal of unknown correlated noise. The proposed approach neither requires knowledge of the noise model nor access to ground truth targets. The input to our algorithm consists of easily captured bursts of noisy shots. Our algorithm constructs artificial patch-craft images from these bursts by patch matching and stitching, and the obtained crafted images are used as targets for the training. Our method does not require registration of the different images within the burst. We evaluate the proposed framework through extensive experiments with synthetic and real image noise. | https://openaccess.thecvf.com/content/CVPR2023/papers/Vaksman_Patch-Craft_Self-Supervised_Training_for_Correlated_Image_Denoising_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Vaksman_Patch-Craft_Self-Supervised_Training_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.09919 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Vaksman_Patch-Craft_Self-Supervised_Training_for_Correlated_Image_Denoising_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Vaksman_Patch-Craft_Self-Supervised_Training_for_Correlated_Image_Denoising_CVPR_2023_paper.html | CVPR 2023 | null |
Learning To Fuse Monocular and Multi-View Cues for Multi-Frame Depth Estimation in Dynamic Scenes | Rui Li, Dong Gong, Wei Yin, Hao Chen, Yu Zhu, Kaixuan Wang, Xiaozhi Chen, Jinqiu Sun, Yanning Zhang | Multi-frame depth estimation generally achieves high accuracy relying on the multi-view geometric consistency. When applied in dynamic scenes, e.g., autonomous driving, this consistency is usually violated in the dynamic areas, leading to corrupted estimations. Many multi-frame methods handle dynamic areas by identifying them with explicit masks and compensating the multi-view cues with monocular cues represented as local monocular depth or features. The improvements are limited due to the uncontrolled quality of the masks and the underutilized benefits of the fusion of the two types of cues. In this paper, we propose a novel method to learn to fuse the multi-view and monocular cues encoded as volumes without needing the heuristically crafted masks. As unveiled in our analyses, the multi-view cues capture more accurate geometric information in static areas, and the monocular cues capture more useful contexts in dynamic areas. To let the geometric perception learned from multi-view cues in static areas propagate to the monocular representation in dynamic areas and let monocular cues enhance the representation of multi-view cost volume, we propose a cross-cue fusion (CCF) module, which includes the cross-cue attention (CCA) to encode the spatially non-local relative intra-relations from each source to enhance the representation of the other. Experiments on real-world datasets prove the significant effectiveness and generalization ability of the proposed method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Learning_To_Fuse_Monocular_and_Multi-View_Cues_for_Multi-Frame_Depth_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Learning_To_Fuse_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.08993 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Learning_To_Fuse_Monocular_and_Multi-View_Cues_for_Multi-Frame_Depth_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Learning_To_Fuse_Monocular_and_Multi-View_Cues_for_Multi-Frame_Depth_CVPR_2023_paper.html | CVPR 2023 | null |
DynaFed: Tackling Client Data Heterogeneity With Global Dynamics | Renjie Pi, Weizhong Zhang, Yueqi Xie, Jiahui Gao, Xiaoyu Wang, Sunghun Kim, Qifeng Chen | The Federated Learning (FL) paradigm is known to face challenges under heterogeneous client data. Local training on non-iid distributed data results in deflected local optimum, which causes the client models drift further away from each other and degrades the aggregated global model's performance. A natural solution is to gather all client data onto the server, such that the server has a global view of the entire data distribution. Unfortunately, this reduces to regular training, which compromises clients' privacy and conflicts with the purpose of FL. In this paper, we put forth an idea to collect and leverage global knowledge on the server without hindering data privacy. We unearth such knowledge from the dynamics of the global model's trajectory. Specifically, we first reserve a short trajectory of global model snapshots on the server. Then, we synthesize a small pseudo dataset such that the model trained on it mimics the dynamics of the reserved global model trajectory. Afterward, the synthesized data is used to help aggregate the deflected clients into the global model. We name our method DynaFed, which enjoys the following advantages: 1) we do not rely on any external on-server dataset, which requires no additional cost for data collection; 2) the pseudo data can be synthesized in early communication rounds, which enables DynaFed to take effect early for boosting the convergence and stabilizing training; 3) the pseudo data only needs to be synthesized once and can be directly utilized on the server to help aggregation in subsequent rounds. Experiments across extensive benchmarks are conducted to showcase the effectiveness of DynaFed. We also provide insights and understanding of the underlying mechanism of our method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Pi_DynaFed_Tackling_Client_Data_Heterogeneity_With_Global_Dynamics_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2211.10878 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pi_DynaFed_Tackling_Client_Data_Heterogeneity_With_Global_Dynamics_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pi_DynaFed_Tackling_Client_Data_Heterogeneity_With_Global_Dynamics_CVPR_2023_paper.html | CVPR 2023 | null |
Bias-Eliminating Augmentation Learning for Debiased Federated Learning | Yuan-Yi Xu, Ci-Siang Lin, Yu-Chiang Frank Wang | Learning models trained on biased datasets tend to observe correlations between categorical and undesirable features, which result in degraded performances. Most existing debiased learning models are designed for centralized machine learning, which cannot be directly applied to distributed settings like federated learning (FL), which collects data at distinct clients with privacy preserved. To tackle the challenging task of debiased federated learning, we present a novel FL framework of Bias-Eliminating Augmentation Learning (FedBEAL), which learns to deploy Bias-Eliminating Augmenters (BEA) for producing client-specific bias-conflicting samples at each client. Since the bias types or attributes are not known in advance, a unique learning strategy is presented to jointly train BEA with the proposed FL framework. Extensive image classification experiments on datasets with various bias types confirm the effectiveness and applicability of our FedBEAL, which performs favorably against state-of-the-art debiasing and FL methods for debiased FL. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Bias-Eliminating_Augmentation_Learning_for_Debiased_Federated_Learning_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Bias-Eliminating_Augmentation_Learning_for_Debiased_Federated_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Bias-Eliminating_Augmentation_Learning_for_Debiased_Federated_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
DistilPose: Tokenized Pose Regression With Heatmap Distillation | Suhang Ye, Yingyi Zhang, Jie Hu, Liujuan Cao, Shengchuan Zhang, Lei Shen, Jun Wang, Shouhong Ding, Rongrong Ji | In the field of human pose estimation, regression-based methods have been dominated in terms of speed, while heatmap-based methods are far ahead in terms of performance. How to take advantage of both schemes remains a challenging problem. In this paper, we propose a novel human pose estimation framework termed DistilPose, which bridges the gaps between heatmap-based and regression-based methods. Specifically, DistilPose maximizes the transfer of knowledge from the teacher model (heatmap-based) to the student model (regression-based) through Token-distilling Encoder (TDE) and Simulated Heatmaps. TDE aligns the feature spaces of heatmap-based and regression-based models by introducing tokenization, while Simulated Heatmaps transfer explicit guidance (distribution and confidence) from teacher heatmaps into student models. Extensive experiments show that the proposed DistilPose can significantly improve the performance of the regression-based models while maintaining efficiency. Specifically, on the MSCOCO validation dataset, DistilPose-S obtains 71.6% mAP with 5.36M parameter, 2.38 GFLOPs and 40.2 FPS, which saves 12.95x, 7.16x computational cost and is 4.9x faster than its teacher model with only 0.9 points performance drop. Furthermore, DistilPose-L obtains 74.4% mAP on MSCOCO validation dataset, achieving a new state-of-the-art among predominant regression-based models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ye_DistilPose_Tokenized_Pose_Regression_With_Heatmap_Distillation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ye_DistilPose_Tokenized_Pose_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.02455 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ye_DistilPose_Tokenized_Pose_Regression_With_Heatmap_Distillation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ye_DistilPose_Tokenized_Pose_Regression_With_Heatmap_Distillation_CVPR_2023_paper.html | CVPR 2023 | null |
Understanding the Robustness of 3D Object Detection With Bird's-Eye-View Representations in Autonomous Driving | Zijian Zhu, Yichi Zhang, Hai Chen, Yinpeng Dong, Shu Zhao, Wenbo Ding, Jiachen Zhong, Shibao Zheng | 3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations; 2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features; 3) Camera-LiDAR fusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_Understanding_the_Robustness_of_3D_Object_Detection_With_Birds-Eye-View_Representations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_Understanding_the_Robustness_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Understanding_the_Robustness_of_3D_Object_Detection_With_Birds-Eye-View_Representations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Understanding_the_Robustness_of_3D_Object_Detection_With_Birds-Eye-View_Representations_CVPR_2023_paper.html | CVPR 2023 | null |
Neural Volumetric Memory for Visual Locomotion Control | Ruihan Yang, Ge Yang, Xiaolong Wang | Legged robots have the potential to expand the reach of autonomy beyond paved roads. In this work, we consider the difficult problem of locomotion on challenging terrains using a single forward-facing depth camera. Due to the partial observability of the problem, the robot has to rely on past observations to infer the terrain currently beneath it. To solve this problem, we follow the paradigm in computer vision that explicitly models the 3D geometry of the scene and propose Neural Volumetric Memory (NVM), a geometric memory architecture that explicitly accounts for the SE(3) equivariance of the 3D world. NVM aggregates feature volumes from multiple camera views by first bringing them back to the ego-centric frame of the robot. We test the learned visual-locomotion policy on a physical robot and show that our approach, learning legged locomotion with neural volumetric memory, produces performance gains over prior works on challenging terrains. We include ablation studies and show that the representations stored in the neural volumetric memory capture sufficient geometric information to reconstruct the scene. Our project page with videos is https://rchalyang.github.io/NVM/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Neural_Volumetric_Memory_for_Visual_Locomotion_Control_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Neural_Volumetric_Memory_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01201 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Neural_Volumetric_Memory_for_Visual_Locomotion_Control_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Neural_Volumetric_Memory_for_Visual_Locomotion_Control_CVPR_2023_paper.html | CVPR 2023 | null |
CUF: Continuous Upsampling Filters | null | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2023/html/Vasconcelos_CUF_Continuous_Upsampling_Filters_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Vasconcelos_CUF_Continuous_Upsampling_Filters_CVPR_2023_paper.html | CVPR 2023 | null |
Generalist: Decoupling Natural and Robust Generalization | Hongjun Wang, Yisen Wang | Deep neural networks obtained by standard training have been constantly plagued by adversarial examples. Although adversarial training demonstrates its capability to defend against adversarial examples, unfortunately, it leads to an inevitable drop in the natural generalization. To address the issue, we decouple the natural generalization and the robust generalization from joint training and formulate different training strategies for each one. Specifically, instead of minimizing a global loss on the expectation over these two generalization errors, we propose a bi-expert framework called Generalist where we simultaneously train base learners with task-aware strategies so that they can specialize in their own fields. The parameters of base learners are collected and combined to form a global learner at intervals during the training process. The global learner is then distributed to the base learners as initialized parameters for continued training. Theoretically, we prove that the risks of Generalist will get lower once the base learners are well trained. Extensive experiments verify the applicability of Generalist to achieve high accuracy on natural examples while maintaining considerable robustness to adversarial ones. Code is available at https://github.com/PKU-ML/Generalist. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Generalist_Decoupling_Natural_and_Robust_Generalization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Generalist_Decoupling_Natural_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13813 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Generalist_Decoupling_Natural_and_Robust_Generalization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Generalist_Decoupling_Natural_and_Robust_Generalization_CVPR_2023_paper.html | CVPR 2023 | null |
Propagate and Calibrate: Real-Time Passive Non-Line-of-Sight Tracking | Yihao Wang, Zhigang Wang, Bin Zhao, Dong Wang, Mulin Chen, Xuelong Li | Non-line-of-sight (NLOS) tracking has drawn increasing attention in recent years, due to its ability to detect object motion out of sight. Most previous works on NLOS tracking rely on active illumination, e.g., laser, and suffer from high cost and elaborate experimental conditions. Besides, these techniques are still far from practical application due to oversimplified settings. In contrast, we propose a purely passive method to track a person walking in an invisible room by only observing a relay wall, which is more in line with real application scenarios, e.g., security. To excavate imperceptible changes in videos of the relay wall, we introduce difference frames as an essential carrier of temporal-local motion messages. In addition, we propose PAC-Net, which consists of alternating propagation and calibration, making it capable of leveraging both dynamic and static messages on a frame-level granularity. To evaluate the proposed method, we build and publish the first dynamic passive NLOS tracking dataset, NLOS-Track, which fills the vacuum of realistic NLOS datasets. NLOS-Track contains thousands of NLOS video clips and corresponding trajectories. Both real-shot and synthetic data are included. Our codes and dataset are available at https://againstentropy.github.io/NLOS-Track/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Propagate_and_Calibrate_Real-Time_Passive_Non-Line-of-Sight_Tracking_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Propagate_and_Calibrate_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11791 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Propagate_and_Calibrate_Real-Time_Passive_Non-Line-of-Sight_Tracking_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Propagate_and_Calibrate_Real-Time_Passive_Non-Line-of-Sight_Tracking_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Decorrelated Representations Efficiently Using Fast Fourier Transform | Yutaro Shigeto, Masashi Shimbo, Yuya Yoshikawa, Akikazu Takeuchi | Barlow Twins and VICReg are self-supervised representation learning models that use regularizers to decorrelate features. Although these models are as effective as conventional representation learning models, their training can be computationally demanding if the dimension d of the projected embeddings is high. As the regularizers are defined in terms of individual elements of a cross-correlation or covariance matrix, computing the loss for n samples takes O(n d^2) time. In this paper, we propose a relaxed decorrelating regularizer that can be computed in O(n d log d) time by Fast Fourier Transform. We also propose an inexpensive technique to mitigate undesirable local minima that develop with the relaxation. The proposed regularizer exhibits accuracy comparable to that of existing regularizers in downstream tasks, whereas their training requires less memory and is faster for large d. The source code is available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shigeto_Learning_Decorrelated_Representations_Efficiently_Using_Fast_Fourier_Transform_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shigeto_Learning_Decorrelated_Representations_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.01569 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shigeto_Learning_Decorrelated_Representations_Efficiently_Using_Fast_Fourier_Transform_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shigeto_Learning_Decorrelated_Representations_Efficiently_Using_Fast_Fourier_Transform_CVPR_2023_paper.html | CVPR 2023 | null |
Quantitative Manipulation of Custom Attributes on 3D-Aware Image Synthesis | Hoseok Do, EunKyung Yoo, Taehyeong Kim, Chul Lee, Jin Young Choi | While 3D-based GAN techniques have been successfully applied to render photo-realistic 3D images with a variety of attributes while preserving view consistency, there has been little research on how to fine-control 3D images without limiting to a specific category of objects of their properties. To fill such research gap, we propose a novel image manipulation model of 3D-based GAN representations for a fine-grained control of specific custom attributes. By extending the latest 3D-based GAN models (e.g., EG3D), our user-friendly quantitative manipulation model enables a fine yet normalized control of 3D manipulation of multi-attribute quantities while achieving view consistency. We validate the effectiveness of our proposed technique both qualitatively and quantitatively through various experiments. | https://openaccess.thecvf.com/content/CVPR2023/papers/Do_Quantitative_Manipulation_of_Custom_Attributes_on_3D-Aware_Image_Synthesis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Do_Quantitative_Manipulation_of_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Do_Quantitative_Manipulation_of_Custom_Attributes_on_3D-Aware_Image_Synthesis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Do_Quantitative_Manipulation_of_Custom_Attributes_on_3D-Aware_Image_Synthesis_CVPR_2023_paper.html | CVPR 2023 | null |
Explicit Visual Prompting for Low-Level Structure Segmentations | Weihuang Liu, Xi Shen, Chi-Man Pun, Xiaodong Cun | We consider the generic problem of detecting low-level structures in images, which includes segmenting the manipulated parts, identifying out-of-focus pixels, separating shadow regions, and detecting concealed objects. Whereas each such topic has been typically addressed with a domain-specific solution, we show that a unified approach performs well across all of them. We take inspiration from the widely-used pre-training and then prompt tuning protocols in NLP and propose a new visual prompting model, named Explicit Visual Prompting (EVP). Different from the previous visual prompting which is typically a dataset-level implicit embedding, our key insight is to enforce the tunable parameters focusing on the explicit visual content from each individual image, i.e., the features from frozen patch embeddings and the input's high-frequency components. The proposed EVP significantly outperforms other parameter-efficient tuning protocols under the same amount of tunable parameters (5.7% extra trainable parameters of each task). EVP also achieves state-of-the-art performances on diverse low-level structure segmentation tasks compared to task-specific solutions. Our code is available at: https://github.com/NiFangBaAGe/Explicit-Visual-Prompt. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Explicit_Visual_Prompting_for_Low-Level_Structure_Segmentations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Explicit_Visual_Prompting_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10883 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Explicit_Visual_Prompting_for_Low-Level_Structure_Segmentations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Explicit_Visual_Prompting_for_Low-Level_Structure_Segmentations_CVPR_2023_paper.html | CVPR 2023 | null |
HOTNAS: Hierarchical Optimal Transport for Neural Architecture Search | Jiechao Yang, Yong Liu, Hongteng Xu | Instead of searching the entire network directly, current NAS approaches increasingly search for multiple relatively small cells to reduce search costs. A major challenge is to jointly measure the similarity of cell micro-architectures and the difference in macro-architectures between different cell-based networks. Recently, optimal transport (OT) has been successfully applied to NAS as it can capture the operational and structural similarity across various networks. However, existing OT-based NAS methods either ignore the cell similarity or focus solely on searching for a single cell architecture. To address these issues, we propose a hierarchical optimal transport metric called HOTNN for measuring the similarity of different networks. In HOTNN, the cell-level similarity computes the OT distance between cells in various networks by considering the similarity of each node and the differences in the information flow costs between node pairs within each cell in terms of operational and structural information. The network-level similarity calculates OT distance between networks by considering both the cell-level similarity and the variation in the global position of each cell within their respective networks. We then explore HOTNN in a Bayesian optimization framework called HOTNAS, and demonstrate its efficacy in diverse tasks. Extensive experiments demonstrate that HOTNAS can discover network architectures with better performance in multiple modular cell-based search spaces. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_HOTNAS_Hierarchical_Optimal_Transport_for_Neural_Architecture_Search_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_HOTNAS_Hierarchical_Optimal_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_HOTNAS_Hierarchical_Optimal_Transport_for_Neural_Architecture_Search_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_HOTNAS_Hierarchical_Optimal_Transport_for_Neural_Architecture_Search_CVPR_2023_paper.html | CVPR 2023 | null |
Two-Shot Video Object Segmentation | Kun Yan, Xiao Li, Fangyun Wei, Jinglu Wang, Chenbin Zhang, Ping Wang, Yan Lu | Previous works on video object segmentation (VOS) are trained on densely annotated videos. Nevertheless, acquiring annotations in pixel level is expensive and time-consuming. In this work, we demonstrate the feasibility of training a satisfactory VOS model on sparsely annotated videos--we merely require two labeled frames per training video while the performance is sustained. We term this novel training paradigm as two-shot video object segmentation, or two-shot VOS for short. The underlying idea is to generate pseudo labels for unlabeled frames during training and to optimize the model on the combination of labeled and pseudo-labeled data. Our approach is extremely simple and can be applied to a majority of existing frameworks. We first pre-train a VOS model on sparsely annotated videos in a semi-supervised manner, with the first frame always being a labeled one. Then, we adopt the pre-trained VOS model to generate pseudo labels for all unlabeled frames, which are subsequently stored in a pseudo-label bank. Finally, we retrain a VOS model on both labeled and pseudo-labeled data without any restrictions on the first frame. For the first time, we present a general way to train VOS models on two-shot VOS datasets. By using 7.3% and 2.9% labeled data of YouTube-VOS and DAVIS benchmarks, our approach achieves comparable results in contrast to the counterparts trained on fully labeled set. Code and models are available at https://github.com/yk-pku/Two-shot-Video-Object-Segmentation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yan_Two-Shot_Video_Object_Segmentation_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.12078 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_Two-Shot_Video_Object_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_Two-Shot_Video_Object_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Neural Fields Meet Explicit Geometric Representations for Inverse Rendering of Urban Scenes | Zian Wang, Tianchang Shen, Jun Gao, Shengyu Huang, Jacob Munkberg, Jon Hasselgren, Zan Gojcic, Wenzheng Chen, Sanja Fidler | Reconstruction and intrinsic decomposition of scenes from captured imagery would enable many applications such as relighting and virtual object insertion. Recent NeRF based methods achieve impressive fidelity of 3D reconstruction, but bake the lighting and shadows into the radiance field, while mesh-based methods that facilitate intrinsic decomposition through differentiable rendering have not yet scaled to the complexity and scale of outdoor scenes. We present a novel inverse rendering framework for large urban scenes capable of jointly reconstructing the scene geometry, spatially-varying materials, and HDR lighting from a set of posed RGB images with optional depth. Specifically, we use a neural field to account for the primary rays, and use an explicit mesh (reconstructed from the underlying neural field) for modeling secondary rays that produce higher-order lighting effects such as cast shadows. By faithfully disentangling complex geometry and materials from lighting effects, our method enables photorealistic relighting with specular and shadow effects on several outdoor datasets. Moreover, it supports physics-based scene manipulations such as virtual object insertion with ray-traced shadow casting. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Neural_Fields_Meet_Explicit_Geometric_Representations_for_Inverse_Rendering_of_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Neural_Fields_Meet_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.03266 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Neural_Fields_Meet_Explicit_Geometric_Representations_for_Inverse_Rendering_of_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Neural_Fields_Meet_Explicit_Geometric_Representations_for_Inverse_Rendering_of_CVPR_2023_paper.html | CVPR 2023 | null |
Practical Network Acceleration With Tiny Sets | Guo-Hua Wang, Jianxin Wu | Due to data privacy issues, accelerating networks with tiny training sets has become a critical need in practice. Previous methods mainly adopt filter-level pruning to accelerate networks with scarce training samples. In this paper, we reveal that dropping blocks is a fundamentally superior approach in this scenario. It enjoys a higher acceleration ratio and results in a better latency-accuracy performance under the few-shot setting. To choose which blocks to drop, we propose a new concept namely recoverability to measure the difficulty of recovering the compressed network. Our recoverability is efficient and effective for choosing which blocks to drop. Finally, we propose an algorithm named PRACTISE to accelerate networks using only tiny sets of training images. PRACTISE outperforms previous methods by a significant margin. For 22% latency reduction, PRACTISE surpasses previous methods by on average 7% on ImageNet-1k. It also enjoys high generalization ability, working well under data-free or out-of-domain data settings, too. Our code is at https://github.com/DoctorKey/Practise. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Practical_Network_Acceleration_With_Tiny_Sets_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Practical_Network_Acceleration_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Practical_Network_Acceleration_With_Tiny_Sets_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Practical_Network_Acceleration_With_Tiny_Sets_CVPR_2023_paper.html | CVPR 2023 | null |
NeRF-RPN: A General Framework for Object Detection in NeRFs | Benran Hu, Junkai Huang, Yichen Liu, Yu-Wing Tai, Chi-Keung Tang | This paper presents the first significant object detection framework, NeRF-RPN, which directly operates on NeRF. Given a pre-trained NeRF model, NeRF-RPN aims to detect all bounding boxes of objects in a scene. By exploiting a novel voxel representation that incorporates multi-scale 3D neural volumetric features, we demonstrate it is possible to regress the 3D bounding boxes of objects in NeRF directly without rendering the NeRF at any viewpoint. NeRF-RPN is a general framework and can be applied to detect objects without class labels. We experimented NeRF-RPN with various backbone architectures, RPN head designs, and loss functions. All of them can be trained in an end-to-end manner to estimate high quality 3D bounding boxes. To facilitate future research in object detection for NeRF, we built a new benchmark dataset which consists of both synthetic and real-world data with careful labeling and clean up. Code and dataset are available at https://github.com/lyclyc52/NeRF_RPN. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hu_NeRF-RPN_A_General_Framework_for_Object_Detection_in_NeRFs_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hu_NeRF-RPN_A_General_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_NeRF-RPN_A_General_Framework_for_Object_Detection_in_NeRFs_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_NeRF-RPN_A_General_Framework_for_Object_Detection_in_NeRFs_CVPR_2023_paper.html | CVPR 2023 | null |
Cross-Image-Attention for Conditional Embeddings in Deep Metric Learning | Dmytro Kotovenko, Pingchuan Ma, Timo Milbich, Björn Ommer | Learning compact image embeddings that yield semantic similarities between images and that generalize to unseen test classes, is at the core of deep metric learning (DML). Finding a mapping from a rich, localized image feature map onto a compact embedding vector is challenging: Although similarity emerges between tuples of images, DML approaches marginalize out information in an individual image before considering another image to which similarity is to be computed. Instead, we propose during training to condition the embedding of an image on the image we want to compare it to. Rather than embedding by a simple pooling as in standard DML, we use cross-attention so that one image can identify relevant features in the other image. Consequently, the attention mechanism establishes a hierarchy of conditional embeddings that gradually incorporates information about the tuple to steer the representation of an individual image. The cross-attention layers bridge the gap between the original unconditional embedding and the final similarity and allow backpropagtion to update encodings more directly than through a lossy pooling layer. At test time we use the resulting improved unconditional embeddings, thus requiring no additional parameters or computational overhead. Experiments on established DML benchmarks show that our cross-attention conditional embedding during training improves the underlying standard DML pipeline significantly so that it outperforms the state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kotovenko_Cross-Image-Attention_for_Conditional_Embeddings_in_Deep_Metric_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kotovenko_Cross-Image-Attention_for_Conditional_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kotovenko_Cross-Image-Attention_for_Conditional_Embeddings_in_Deep_Metric_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kotovenko_Cross-Image-Attention_for_Conditional_Embeddings_in_Deep_Metric_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Masked Wavelet Representation for Compact Neural Radiance Fields | Daniel Rho, Byeonghyeon Lee, Seungtae Nam, Joo Chan Lee, Jong Hwan Ko, Eunbyung Park | Neural radiance fields (NeRF) have demonstrated the potential of coordinate-based neural representation (neural fields or implicit neural representation) in neural rendering. However, using a multi-layer perceptron (MLP) to represent a 3D scene or object requires enormous computational resources and time. There have been recent studies on how to reduce these computational inefficiencies by using additional data structures, such as grids or trees. Despite the promising performance, the explicit data structure necessitates a substantial amount of memory. In this work, we present a method to reduce the size without compromising the advantages of having additional data structures. In detail, we propose using the wavelet transform on grid-based neural fields. Grid-based neural fields are for fast convergence, and the wavelet transform, whose efficiency has been demonstrated in high-performance standard codecs, is to improve the parameter efficiency of grids. Furthermore, in order to achieve a higher sparsity of grid coefficients while maintaining reconstruction quality, we present a novel trainable masking approach. Experimental results demonstrate that non-spatial grid coefficients, such as wavelet coefficients, are capable of attaining a higher level of sparsity than spatial grid coefficients, resulting in a more compact representation. With our proposed mask and compression pipeline, we achieved state-of-the-art performance within a memory budget of 2 MB. Our code is available at https://github.com/daniel03c1/masked_wavelet_nerf. | https://openaccess.thecvf.com/content/CVPR2023/papers/Rho_Masked_Wavelet_Representation_for_Compact_Neural_Radiance_Fields_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Rho_Masked_Wavelet_Representation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.09069 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Rho_Masked_Wavelet_Representation_for_Compact_Neural_Radiance_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Rho_Masked_Wavelet_Representation_for_Compact_Neural_Radiance_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detection | Anthony Chen, Kevin Zhang, Renrui Zhang, Zihan Wang, Yuheng Lu, Yandong Guo, Shanghang Zhang | Masked Autoencoders learn strong visual representations and achieve state-of-the-art results in several independent modalities, yet very few works have addressed their capabilities in multi-modality settings. In this work, we focus on point cloud and RGB image data, two modalities that are often presented together in the real world and explore their meaningful interactions. To improve upon the cross-modal synergy in existing works, we propose PiMAE, a self-supervised pre-training framework that promotes 3D and 2D interaction through three aspects. Specifically, we first notice the importance of masking strategies between the two sources and utilize a projection module to complementarily align the mask and visible tokens of the two modalities. Then, we utilize a well-crafted two-branch MAE pipeline with a novel shared decoder to promote cross-modality interaction in the mask tokens. Finally, we design a unique cross-modal reconstruction module to enhance representation learning for both modalities. Through extensive experiments performed on large-scale RGB-D scene understanding benchmarks (SUN RGB-D and ScannetV2), we discover it is nontrivial to interactively learn point-image features, where we greatly improve multiple 3D detectors, 2D detectors and few-shot classifiers by 2.9%, 6.7%, and 2.4%, respectively. Code is available at https://github.com/BLVLab/PiMAE. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_PiMAE_Point_Cloud_and_Image_Interactive_Masked_Autoencoders_for_3D_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_PiMAE_Point_Cloud_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.08129 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_PiMAE_Point_Cloud_and_Image_Interactive_Masked_Autoencoders_for_3D_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_PiMAE_Point_Cloud_and_Image_Interactive_Masked_Autoencoders_for_3D_CVPR_2023_paper.html | CVPR 2023 | null |
ObjectStitch: Object Compositing With Diffusion Model | Yizhi Song, Zhifei Zhang, Zhe Lin, Scott Cohen, Brian Price, Jianming Zhang, Soo Ye Kim, Daniel Aliaga | Object compositing based on 2D images is a challenging problem since it typically involves multiple processing stages such as color harmonization, geometry correction and shadow generation to generate realistic results. Furthermore, annotating training data pairs for compositing requires substantial manual effort from professionals, and is hardly scalable. Thus, with the recent advances in generative models, in this work, we propose a self-supervised framework for object compositing by leveraging the power of conditional diffusion models. Our framework can hollistically address the object compositing task in a unified model, transforming the viewpoint, geometry, color and shadow of the generated object while requiring no manual labeling. To preserve the input object's characteristics, we introduce a content adaptor that helps to maintain categorical semantics and object appearance. A data augmentation method is further adopted to improve the fidelity of the generator. Our method outperforms relevant baselines in both realism and faithfulness of the synthesized result images in a user study on various real-world images. | https://openaccess.thecvf.com/content/CVPR2023/papers/Song_ObjectStitch_Object_Compositing_With_Diffusion_Model_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Song_ObjectStitch_Object_Compositing_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Song_ObjectStitch_Object_Compositing_With_Diffusion_Model_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Song_ObjectStitch_Object_Compositing_With_Diffusion_Model_CVPR_2023_paper.html | CVPR 2023 | null |
High-Fidelity 3D GAN Inversion by Pseudo-Multi-View Optimization | Jiaxin Xie, Hao Ouyang, Jingtan Piao, Chenyang Lei, Qifeng Chen | We present a high-fidelity 3D generative adversarial network (GAN) inversion framework that can synthesize photo-realistic novel views while preserving specific details of the input image. High-fidelity 3D GAN inversion is inherently challenging due to the geometry-texture trade-off, where overfitting to a single view input image often damages the estimated geometry during the latent optimization. To solve this challenge, we propose a novel pipeline that builds on the pseudo-multi-view estimation with visibility analysis. We keep the original textures for the visible parts and utilize generative priors for the occluded parts. Extensive experiments show that our approach achieves advantageous reconstruction and novel view synthesis quality over prior work, even for images with out-of-distribution textures. The proposed pipeline also enables image attribute editing with the inverted latent code and 3D-aware texture modification. Our approach enables high-fidelity 3D rendering from a single image, which is promising for various applications of AI-generated 3D content. The source code is at https://github.com/jiaxinxie97/HFGI3D/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_High-Fidelity_3D_GAN_Inversion_by_Pseudo-Multi-View_Optimization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xie_High-Fidelity_3D_GAN_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.15662 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_High-Fidelity_3D_GAN_Inversion_by_Pseudo-Multi-View_Optimization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_High-Fidelity_3D_GAN_Inversion_by_Pseudo-Multi-View_Optimization_CVPR_2023_paper.html | CVPR 2023 | null |
Anchor3DLane: Learning To Regress 3D Anchors for Monocular 3D Lane Detection | Shaofei Huang, Zhenwei Shen, Zehao Huang, Zi-han Ding, Jiao Dai, Jizhong Han, Naiyan Wang, Si Liu | Monocular 3D lane detection is a challenging task due to its lack of depth information. A popular solution is to first transform the front-viewed (FV) images or features into the bird-eye-view (BEV) space with inverse perspective mapping (IPM) and detect lanes from BEV features. However, the reliance of IPM on flat ground assumption and loss of context information make it inaccurate to restore 3D information from BEV representations. An attempt has been made to get rid of BEV and predict 3D lanes from FV representations directly, while it still underperforms other BEV-based methods given its lack of structured representation for 3D lanes. In this paper, we define 3D lane anchors in the 3D space and propose a BEV-free method named Anchor3DLane to predict 3D lanes directly from FV representations. 3D lane anchors are projected to the FV features to extract their features which contain both good structural and context information to make accurate predictions. In addition, we also develop a global optimization method that makes use of the equal-width property between lanes to reduce the lateral error of predictions. Extensive experiments on three popular 3D lane detection benchmarks show that our Anchor3DLane outperforms previous BEV-based methods and achieves state-of-the-art performances. The code is available at: https://github.com/tusen-ai/Anchor3DLane. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Anchor3DLane_Learning_To_Regress_3D_Anchors_for_Monocular_3D_Lane_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2301.02371 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Anchor3DLane_Learning_To_Regress_3D_Anchors_for_Monocular_3D_Lane_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Anchor3DLane_Learning_To_Regress_3D_Anchors_for_Monocular_3D_Lane_CVPR_2023_paper.html | CVPR 2023 | null |
Class-Balancing Diffusion Models | Yiming Qin, Huangjie Zheng, Jiangchao Yao, Mingyuan Zhou, Ya Zhang | Diffusion-based models have shown the merits of generating high-quality visual data while preserving better diversity in recent studies. However, such observation is only justified with curated data distribution, where the data samples are nicely pre-processed to be uniformly distributed in terms of their labels. In practice, a long-tailed data distribution appears more common and how diffusion models perform on such class-imbalanced data remains unknown. In this work, we first investigate this problem and observe significant degradation in both diversity and fidelity when the diffusion model is trained on datasets with class-imbalanced distributions. Especially in tail classes, the generations largely lose diversity and we observe severe mode-collapse issues. To tackle this problem, we set from the hypothesis that the data distribution is not class-balanced, and propose Class-Balancing Diffusion Models (CBDM) that are trained with a distribution adjustment regularizer as a solution. Experiments show that images generated by CBDM exhibit higher diversity and quality in both quantitative and qualitative ways. Our method benchmarked the generation results on CIFAR100/CIFAR100LT dataset and shows outstanding performance on the downstream recognition task. | https://openaccess.thecvf.com/content/CVPR2023/papers/Qin_Class-Balancing_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qin_Class-Balancing_Diffusion_Models_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.00562 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qin_Class-Balancing_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qin_Class-Balancing_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
AstroNet: When Astrocyte Meets Artificial Neural Network | Mengqiao Han, Liyuan Pan, Xiabi Liu | Network structure learning aims to optimize network architectures and make them more efficient without compromising performance. In this paper, we first study the astrocytes, a new mechanism to regulate connections in the classic M-P neuron. Then, with the astrocytes, we propose an AstroNet that can adaptively optimize neuron connections and therefore achieves structure learning to achieve higher accuracy and efficiency. AstroNet is based on our built Astrocyte-Neuron model, with a temporal regulation mechanism and a global connection mechanism, which is inspired by the bidirectional communication property of astrocytes. With the model, the proposed AstroNet uses a neural network (NN) for performing tasks, and an astrocyte network (AN) to continuously optimize the connections of NN, i.e., assigning weight to the neuron units in the NN adaptively. Experiments on the classification task demonstrate that our AstroNet can efficiently optimize the network structure while achieving state-of-the-art (SOTA) accuracy. | https://openaccess.thecvf.com/content/CVPR2023/papers/Han_AstroNet_When_Astrocyte_Meets_Artificial_Neural_Network_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Han_AstroNet_When_Astrocyte_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Han_AstroNet_When_Astrocyte_Meets_Artificial_Neural_Network_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Han_AstroNet_When_Astrocyte_Meets_Artificial_Neural_Network_CVPR_2023_paper.html | CVPR 2023 | null |
Feature Alignment and Uniformity for Test Time Adaptation | Shuai Wang, Daoan Zhang, Zipei Yan, Jianguo Zhang, Rui Li | Test time adaptation (TTA) aims to adapt deep neural networks when receiving out of distribution test domain samples. In this setting, the model can only access online unlabeled test samples and pre-trained models on the training domains. We first address TTA as a feature revision problem due to the domain gap between source domains and target domains. After that, we follow the two measurements alignment and uniformity to discuss the test time feature revision. For test time feature uniformity, we propose a test time self-distillation strategy to guarantee the consistency of uniformity between representations of the current batch and all the previous batches. For test time feature alignment, we propose a memorized spatial local clustering strategy to align the representations among the neighborhood samples for the upcoming batch. To deal with the common noisy label problem, we propound the entropy and consistency filters to select and drop the possible noisy labels. To prove the scalability and efficacy of our method, we conduct experiments on four domain generalization benchmarks and four medical image segmentation tasks with various backbones. Experiment results show that our method not only improves baseline stably but also outperforms existing state-of-the-art test time adaptation methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Feature_Alignment_and_Uniformity_for_Test_Time_Adaptation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Feature_Alignment_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10902 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Feature_Alignment_and_Uniformity_for_Test_Time_Adaptation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Feature_Alignment_and_Uniformity_for_Test_Time_Adaptation_CVPR_2023_paper.html | CVPR 2023 | null |
Balanced Product of Calibrated Experts for Long-Tailed Recognition | Emanuel Sanchez Aimar, Arvi Jonnarth, Michael Felsberg, Marco Kuhlmann | Many real-world recognition problems are characterized by long-tailed label distributions. These distributions make representation learning highly challenging due to limited generalization over the tail classes. If the test distribution differs from the training distribution, e.g. uniform versus long-tailed, the problem of the distribution shift needs to be addressed. A recent line of work proposes learning multiple diverse experts to tackle this issue. Ensemble diversity is encouraged by various techniques, e.g. by specializing different experts in the head and the tail classes. In this work, we take an analytical approach and extend the notion of logit adjustment to ensembles to form a Balanced Product of Experts (BalPoE). BalPoE combines a family of experts with different test-time target distributions, generalizing several previous approaches. We show how to properly define these distributions and combine the experts in order to achieve unbiased predictions, by proving that the ensemble is Fisher-consistent for minimizing the balanced error. Our theoretical analysis shows that our balanced ensemble requires calibrated experts, which we achieve in practice using mixup. We conduct extensive experiments and our method obtains new state-of-the-art results on three long-tailed datasets: CIFAR-100-LT, ImageNet-LT, and iNaturalist-2018. Our code is available at https://github.com/emasa/BalPoE-CalibratedLT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Aimar_Balanced_Product_of_Calibrated_Experts_for_Long-Tailed_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Aimar_Balanced_Product_of_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2206.05260 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Aimar_Balanced_Product_of_Calibrated_Experts_for_Long-Tailed_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Aimar_Balanced_Product_of_Calibrated_Experts_for_Long-Tailed_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Single Image Backdoor Inversion via Robust Smoothed Classifiers | Mingjie Sun, Zico Kolter | Backdoor inversion, the process of finding a backdoor trigger inserted into a machine learning model, has become the pillar of many backdoor detection and defense methods. Previous works on backdoor inversion often recover the backdoor through an optimization process to flip a support set of clean images into the target class. However, it is rarely studied and understood how large this support set should be to recover a successful backdoor. In this work, we show that one can reliably recover the backdoor trigger with as few as a single image. Specifically, we propose the SmoothInv method, which first constructs a robust smoothed version of the backdoored classifier and then performs guided image synthesis towards the target class to reveal the backdoor pattern. SmoothInv requires neither an explicit modeling of the backdoor via a mask variable, nor any complex regularization schemes, which has become the standard practice in backdoor inversion methods. We perform both quantitaive and qualitative study on backdoored classifiers from previous published backdoor attacks. We demonstrate that compared to existing methods, SmoothInv is able to recover successful backdoors from single images, while maintaining high fidelity to the original backdoor. We also show how we identify the target backdoored class from the backdoored classifier. Last, we propose and analyze two countermeasures to our approach and show that SmoothInv remains robust in the face of an adaptive attacker. Our code is available at https://github.com/locuslab/smoothinv. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_Single_Image_Backdoor_Inversion_via_Robust_Smoothed_Classifiers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sun_Single_Image_Backdoor_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.00215 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Single_Image_Backdoor_Inversion_via_Robust_Smoothed_Classifiers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Single_Image_Backdoor_Inversion_via_Robust_Smoothed_Classifiers_CVPR_2023_paper.html | CVPR 2023 | null |
PanoSwin: A Pano-Style Swin Transformer for Panorama Understanding | Zhixin Ling, Zhen Xing, Xiangdong Zhou, Manliang Cao, Guichun Zhou | In panorama understanding, the widely used equirectangular projection (ERP) entails boundary discontinuity and spatial distortion. It severely deteriorates the conventional CNNs and vision Transformers on panoramas. In this paper, we propose a simple yet effective architecture named PanoSwin to learn panorama representations with ERP. To deal with the challenges brought by equirectangular projection, we explore a pano-style shift windowing scheme and novel pitch attention to address the boundary discontinuity and the spatial distortion, respectively. Besides, based on spherical distance and Cartesian coordinates, we adapt absolute positional encodings and relative positional biases for panoramas to enhance panoramic geometry information. Realizing that planar image understanding might share some common knowledge with panorama understanding, we devise a novel two-stage learning framework to facilitate knowledge transfer from the planar images to panoramas. We conduct experiments against the state-of-the-art on various panoramic tasks, i.e., panoramic object detection, panoramic classification, and panoramic layout estimation. The experimental results demonstrate the effectiveness of PanoSwin in panorama understanding. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ling_PanoSwin_A_Pano-Style_Swin_Transformer_for_Panorama_Understanding_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ling_PanoSwin_A_Pano-Style_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ling_PanoSwin_A_Pano-Style_Swin_Transformer_for_Panorama_Understanding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ling_PanoSwin_A_Pano-Style_Swin_Transformer_for_Panorama_Understanding_CVPR_2023_paper.html | CVPR 2023 | null |
Parameter Efficient Local Implicit Image Function Network for Face Segmentation | Mausoom Sarkar, Nikitha SR, Mayur Hemani, Rishabh Jain, Balaji Krishnamurthy | Face parsing is defined as the per-pixel labeling of images containing human faces. The labels are defined to identify key facial regions like eyes, lips, nose, hair, etc. In this work, we make use of the structural consistency of the human face to propose a lightweight face-parsing method using a Local Implicit Function network, FP-LIIF. We propose a simple architecture having a convolutional encoder and a pixel MLP decoder that uses 1/26th number of parameters compared to the state-of-the-art models and yet matches or outperforms state-of-the-art models on multiple datasets, like CelebAMask-HQ and LaPa. We do not use any pretraining, and compared to other works, our network can also generate segmentation at different resolutions without any changes in the input resolution. This work enables the use of facial segmentation on low-compute or low-bandwidth devices because of its higher FPS and smaller model size. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sarkar_Parameter_Efficient_Local_Implicit_Image_Function_Network_for_Face_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sarkar_Parameter_Efficient_Local_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15122 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sarkar_Parameter_Efficient_Local_Implicit_Image_Function_Network_for_Face_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sarkar_Parameter_Efficient_Local_Implicit_Image_Function_Network_for_Face_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
A Hierarchical Representation Network for Accurate and Detailed Face Reconstruction From In-the-Wild Images | Biwen Lei, Jianqiang Ren, Mengyang Feng, Miaomiao Cui, Xuansong Xie | Limited by the nature of the low-dimensional representational capacity of 3DMM, most of the 3DMM-based face reconstruction (FR) methods fail to recover high-frequency facial details, such as wrinkles, dimples, etc. Some attempt to solve the problem by introducing detail maps or non-linear operations, however, the results are still not vivid. To this end, we in this paper present a novel hierarchical representation network (HRN) to achieve accurate and detailed face reconstruction from a single image. Specifically, we implement the geometry disentanglement and introduce the hierarchical representation to fulfill detailed face modeling. Meanwhile, 3D priors of facial details are incorporated to enhance the accuracy and authenticity of the reconstruction results. We also propose a de-retouching module to achieve better decoupling of the geometry and appearance. It is noteworthy that our framework can be extended to a multi-view fashion by considering detail consistency of different views. Extensive experiments on two single-view and two multi-view FR benchmarks demonstrate that our method outperforms the existing methods in both reconstruction accuracy and visual effects. Finally, we introduce a high-quality 3D face dataset FaceHD-100 to boost the research of high-fidelity face reconstruction. The project homepage is at https://younglbw.github.io/HRN-homepage/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lei_A_Hierarchical_Representation_Network_for_Accurate_and_Detailed_Face_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lei_A_Hierarchical_Representation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.14434 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lei_A_Hierarchical_Representation_Network_for_Accurate_and_Detailed_Face_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lei_A_Hierarchical_Representation_Network_for_Accurate_and_Detailed_Face_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
PersonNeRF: Personalized Reconstruction From Photo Collections | Chung-Yi Weng, Pratul P. Srinivasan, Brian Curless, Ira Kemelmacher-Shlizerman | We present PersonNeRF, a method that takes a collection of photos of a subject (e.g., Roger Federer) captured across multiple years with arbitrary body poses and appearances, and enables rendering the subject with arbitrary novel combinations of viewpoint, body pose, and appearance. PersonNeRF builds a customized neural volumetric 3D model of the subject that is able to render an entire space spanned by camera viewpoint, body pose, and appearance. A central challenge in this task is dealing with sparse observations; a given body pose is likely only observed by a single viewpoint with a single appearance, and a given appearance is only observed under a handful of different body poses. We address this issue by recovering a canonical T-pose neural volumetric representation of the subject that allows for changing appearance across different observations, but uses a shared pose-dependent motion field across all observations. We demonstrate that this approach, along with regularization of the recovered volumetric geometry to encourage smoothness, is able to recover a model that renders compelling images from novel combinations of viewpoint, pose, and appearance from these challenging unstructured photo collections, outperforming prior work for free-viewpoint human rendering. | https://openaccess.thecvf.com/content/CVPR2023/papers/Weng_PersonNeRF_Personalized_Reconstruction_From_Photo_Collections_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Weng_PersonNeRF_Personalized_Reconstruction_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.08504 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Weng_PersonNeRF_Personalized_Reconstruction_From_Photo_Collections_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Weng_PersonNeRF_Personalized_Reconstruction_From_Photo_Collections_CVPR_2023_paper.html | CVPR 2023 | null |
Enhanced Multimodal Representation Learning With Cross-Modal KD | Mengxi Chen, Linyu Xing, Yu Wang, Ya Zhang | This paper explores the tasks of leveraging auxiliary modalities which are only available at training to enhance multimodal representation learning through cross-modal Knowledge Distillation (KD). The widely adopted mutual information maximization-based objective leads to a short-cut solution of the weak teacher, i.e., achieving the maximum mutual information by simply making the teacher model as weak as the student model. To prevent such a weak solution, we introduce an additional objective term, i.e., the mutual information between the teacher and the auxiliary modality model. Besides, to narrow down the information gap between the student and teacher, we further propose to minimize the conditional entropy of the teacher given the student. Novel training schemes based on contrastive learning and adversarial learning are designed to optimize the mutual information and the conditional entropy, respectively. Experimental results on three popular multimodal benchmark datasets have shown that the proposed method outperforms a range of state-of-the-art approaches for video recognition, video retrieval and emotion classification. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Enhanced_Multimodal_Representation_Learning_With_Cross-Modal_KD_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Enhanced_Multimodal_Representation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Enhanced_Multimodal_Representation_Learning_With_Cross-Modal_KD_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Enhanced_Multimodal_Representation_Learning_With_Cross-Modal_KD_CVPR_2023_paper.html | CVPR 2023 | null |
Learning a Depth Covariance Function | Eric Dexheimer, Andrew J. Davison | We propose learning a depth covariance function with applications to geometric vision tasks. Given RGB images as input, the covariance function can be flexibly used to define priors over depth functions, predictive distributions given observations, and methods for active point selection. We leverage these techniques for a selection of downstream tasks: depth completion, bundle adjustment, and monocular dense visual odometry. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dexheimer_Learning_a_Depth_Covariance_Function_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dexheimer_Learning_a_Depth_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.12157 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dexheimer_Learning_a_Depth_Covariance_Function_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dexheimer_Learning_a_Depth_Covariance_Function_CVPR_2023_paper.html | CVPR 2023 | null |
Evading DeepFake Detectors via Adversarial Statistical Consistency | Yang Hou, Qing Guo, Yihao Huang, Xiaofei Xie, Lei Ma, Jianjun Zhao | In recent years, as various realistic face forgery techniques known as DeepFake improves by leaps and bounds, more and more DeepFake detection techniques have been proposed. These methods typically rely on detecting statistical differences between natural (i.e., real) and DeepFake-generated images in both spatial and frequency domains. In this work, we propose to explicitly minimize the statistical differences to evade state-of-the-art DeepFake detectors. To this end, we propose a statistical consistency attack (StatAttack) against DeepFake detectors, which contains two main parts. First, we select several statistical-sensitive natural degradations (i.e., exposure, blur, and noise) and add them to the fake images in an adversarial way. Second, we find that the statistical differences between natural and DeepFake images are positively associated with the distribution shifting between the two kinds of images, and we propose to use a distribution-aware loss to guide the optimization of different degradations. As a result, the feature distributions of generated adversarial examples is close to the natural images. Furthermore, we extend the StatAttack to a more powerful version, MStatAttack, where we extend the single-layer degradation to multi-layer degradations sequentially and use the loss to tune the combination weights jointly. Comprehensive experimental results on four spatial-based detectors and two frequency-based detectors with four datasets demonstrate the effectiveness of our proposed attack method in both white-box and black-box settings. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hou_Evading_DeepFake_Detectors_via_Adversarial_Statistical_Consistency_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hou_Evading_DeepFake_Detectors_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.11670 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hou_Evading_DeepFake_Detectors_via_Adversarial_Statistical_Consistency_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hou_Evading_DeepFake_Detectors_via_Adversarial_Statistical_Consistency_CVPR_2023_paper.html | CVPR 2023 | null |
Referring Image Matting | Jizhizi Li, Jing Zhang, Dacheng Tao | Different from conventional image matting, which either requires user-defined scribbles/trimap to extract a specific foreground object or directly extracts all the foreground objects in the image indiscriminately, we introduce a new task named Referring Image Matting (RIM) in this paper, which aims to extract the meticulous alpha matte of the specific object that best matches the given natural language description, thus enabling a more natural and simpler instruction for image matting. First, we establish a large-scale challenging dataset RefMatte by designing a comprehensive image composition and expression generation engine to automatically produce high-quality images along with diverse text attributes based on public datasets. RefMatte consists of 230 object categories, 47,500 images, 118,749 expression-region entities, and 474,996 expressions. Additionally, we construct a real-world test set with 100 high-resolution natural images and manually annotate complex phrases to evaluate the out-of-domain generalization abilities of RIM methods. Furthermore, we present a novel baseline method CLIPMat for RIM, including a context-embedded prompt, a text-driven semantic pop-up, and a multi-level details extractor. Extensive experiments on RefMatte in both keyword and expression settings validate the superiority of CLIPMat over representative methods. We hope this work could provide novel insights into image matting and encourage more follow-up studies. The dataset, code and models are available at https://github.com/JizhiziLi/RIM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Referring_Image_Matting_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Referring_Image_Matting_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2206.05149 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Referring_Image_Matting_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Referring_Image_Matting_CVPR_2023_paper.html | CVPR 2023 | null |
V2V4Real: A Real-World Large-Scale Dataset for Vehicle-to-Vehicle Cooperative Perception | Runsheng Xu, Xin Xia, Jinlong Li, Hanzhao Li, Shuo Zhang, Zhengzhong Tu, Zonglin Meng, Hao Xiang, Xiaoyu Dong, Rui Song, Hongkai Yu, Bolei Zhou, Jiaqi Ma | Modern perception systems of autonomous vehicles are known to be sensitive to occlusions and lack the capability of long perceiving range. It has been one of the key bottlenecks that prevents Level 5 autonomy. Recent research has demonstrated that the Vehicle-to-Vehicle (V2V) cooperative perception system has great potential to revolutionize the autonomous driving industry. However, the lack of a real-world dataset hinders the progress of this field. To facilitate the development of cooperative perception, we present V2V4Real, the first large-scale real-world multi-modal dataset for V2V perception. The data is collected by two vehicles equipped with multi-modal sensors driving together through diverse scenarios. Our V2V4Real dataset covers a driving area of 410 km, comprising 20K LiDAR frames, 40K RGB frames, 240K annotated 3D bounding boxes for 5 classes, and HDMaps that cover all the driving routes. V2V4Real introduces three perception tasks, including cooperative 3D object detection, cooperative 3D object tracking, and Sim2Real domain adaptation for cooperative perception. We provide comprehensive benchmarks of recent cooperative perception algorithms on three tasks. The V2V4Real dataset can be found at research.seas.ucla.edu/mobility-lab/v2v4real/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_V2V4Real_A_Real-World_Large-Scale_Dataset_for_Vehicle-to-Vehicle_Cooperative_Perception_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_V2V4Real_A_Real-World_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.07601 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_V2V4Real_A_Real-World_Large-Scale_Dataset_for_Vehicle-to-Vehicle_Cooperative_Perception_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_V2V4Real_A_Real-World_Large-Scale_Dataset_for_Vehicle-to-Vehicle_Cooperative_Perception_CVPR_2023_paper.html | CVPR 2023 | null |
RMLVQA: A Margin Loss Approach for Visual Question Answering With Language Biases | Abhipsa Basu, Sravanti Addepalli, R. Venkatesh Babu | Visual Question Answering models have been shown to suffer from language biases, where the model learns a correlation between the question and the answer, ignoring the image. While early works attempted to use question-only models or data augmentations to reduce this bias, we propose an adaptive margin loss approach having two components. The first component considers the frequency of answers within a question type in the training data, which addresses the concern of the class-imbalance causing the language biases. However, it does not take into account the answering difficulty of the samples, which impacts their learning. We address this through the second component, where instance-specific margins are learnt, allowing the model to distinguish between samples of varying complexity. We introduce a bias-injecting component to our model, and compute the instance-specific margins from the confidence of this component. We combine these with the estimated margins to consider both answer-frequency and task-complexity in the training loss. We show that, while the margin loss is effective for out-of-distribution (ood) data, the bias-injecting component is essential for generalising to in-distribution (id) data. Our proposed approach, Robust Margin Loss for Visual Question Answering (RMLVQA) improves upon the existing state-of-the-art results when compared to augmentation-free methods on benchmark VQA datasets suffering from language biases, while maintaining competitive performance on id data, making our method the most robust one among all comparable methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Basu_RMLVQA_A_Margin_Loss_Approach_for_Visual_Question_Answering_With_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Basu_RMLVQA_A_Margin_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Basu_RMLVQA_A_Margin_Loss_Approach_for_Visual_Question_Answering_With_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Basu_RMLVQA_A_Margin_Loss_Approach_for_Visual_Question_Answering_With_CVPR_2023_paper.html | CVPR 2023 | null |
NeuralLift-360: Lifting an In-the-Wild 2D Photo to a 3D Object With 360deg Views | Dejia Xu, Yifan Jiang, Peihao Wang, Zhiwen Fan, Yi Wang, Zhangyang Wang | Virtual reality and augmented reality (XR) bring increasing demand for 3D content generation. However, creating high-quality 3D content requires tedious work from a human expert. In this work, we study the challenging task of lifting a single image to a 3D object and, for the first time, demonstrate the ability to generate a plausible 3D object with 360deg views that corresponds well with the given reference image. By conditioning on the reference image, our model can fulfill the everlasting curiosity for synthesizing novel views of objects from images. Our technique sheds light on a promising direction of easing the workflows for 3D artists and XR designers. We propose a novel framework, dubbed NeuralLift-360, that utilizes a depth-aware neural radiance representation (NeRF) and learns to craft the scene guided by denoising diffusion models. By introducing a ranking loss, our NeuralLift-360 can be guided with rough depth estimation in the wild. We also adopt a CLIP-guided sampling strategy for the diffusion prior to provide coherent guidance. Extensive experiments demonstrate that our NeuralLift-360 significantly outperforms existing state-of-the-art baselines. Project page: https://vita-group.github.io/NeuralLift-360/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_NeuralLift-360_Lifting_an_In-the-Wild_2D_Photo_to_a_3D_Object_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_NeuralLift-360_Lifting_an_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_NeuralLift-360_Lifting_an_In-the-Wild_2D_Photo_to_a_3D_Object_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_NeuralLift-360_Lifting_an_In-the-Wild_2D_Photo_to_a_3D_Object_CVPR_2023_paper.html | CVPR 2023 | null |
ViP3D: End-to-End Visual Trajectory Prediction via 3D Agent Queries | Junru Gu, Chenxu Hu, Tianyuan Zhang, Xuanyao Chen, Yilun Wang, Yue Wang, Hang Zhao | Perception and prediction are two separate modules in the existing autonomous driving systems. They interact with each other via hand-picked features such as agent bounding boxes and trajectories. Due to this separation, prediction, as a downstream module, only receives limited information from the perception module. To make matters worse, errors from the perception modules can propagate and accumulate, adversely affecting the prediction results. In this work, we propose ViP3D, a query-based visual trajectory prediction pipeline that exploits rich information from raw videos to directly predict future trajectories of agents in a scene. ViP3D employs sparse agent queries to detect, track, and predict throughout the pipeline, making it the first fully differentiable vision-based trajectory prediction approach. Instead of using historical feature maps and trajectories, useful information from previous timestamps is encoded in agent queries, which makes ViP3D a concise streaming prediction method. Furthermore, extensive experimental results on the nuScenes dataset show the strong vision-based prediction performance of ViP3D over traditional pipelines and previous end-to-end models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gu_ViP3D_End-to-End_Visual_Trajectory_Prediction_via_3D_Agent_Queries_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gu_ViP3D_End-to-End_Visual_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2208.01582 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gu_ViP3D_End-to-End_Visual_Trajectory_Prediction_via_3D_Agent_Queries_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gu_ViP3D_End-to-End_Visual_Trajectory_Prediction_via_3D_Agent_Queries_CVPR_2023_paper.html | CVPR 2023 | null |
Modality-Invariant Visual Odometry for Embodied Vision | Marius Memmel, Roman Bachmann, Amir Zamir | Effectively localizing an agent in a realistic, noisy setting is crucial for many embodied vision tasks. Visual Odometry (VO) is a practical substitute for unreliable GPS and compass sensors, especially in indoor environments. While SLAM-based methods show a solid performance without large data requirements, they are less flexible and robust w.r.t. to noise and changes in the sensor suite compared to learning-based approaches. Recent deep VO models, however, limit themselves to a fixed set of input modalities, e.g., RGB and depth, while training on millions of samples. When sensors fail, sensor suites change, or modalities are intentionally looped out due to available resources, e.g., power consumption, the models fail catastrophically. Furthermore, training these models from scratch is even more expensive without simulator access or suitable existing models that can be fine-tuned. While such scenarios get mostly ignored in simulation, they commonly hinder a model's reusability in real-world applications. We propose a Transformer-based modality-invariant VO approach that can deal with diverse or changing sensor suites of navigation agents. Our model outperforms previous methods while training on only a fraction of the data. We hope this method opens the door to a broader range of real-world applications that can benefit from flexible and learned VO models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Memmel_Modality-Invariant_Visual_Odometry_for_Embodied_Vision_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Memmel_Modality-Invariant_Visual_Odometry_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.00348 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Memmel_Modality-Invariant_Visual_Odometry_for_Embodied_Vision_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Memmel_Modality-Invariant_Visual_Odometry_for_Embodied_Vision_CVPR_2023_paper.html | CVPR 2023 | null |
What You Can Reconstruct From a Shadow | Ruoshi Liu, Sachit Menon, Chengzhi Mao, Dennis Park, Simon Stent, Carl Vondrick | 3D reconstruction is a fundamental problem in computer vision, and the task is especially challenging when the object to reconstruct is partially or fully occluded. We introduce a method that uses the shadows cast by an unobserved object in order to infer the possible 3D volumes under occlusion. We create a differentiable image formation model that allows us to jointly infer the 3D shape of an object, its pose, and the position of a light source. Since the approach is end-to-end differentiable, we are able to integrate learned priors of object geometry in order to generate realistic 3D shapes of different object categories. Experiments and visualizations show that the method is able to generate multiple possible solutions that are consistent with the observation of the shadow. Our approach works even when the position of the light source and object pose are both unknown. Our approach is also robust to real-world images where ground-truth shadow mask is unknown. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_What_You_Can_Reconstruct_From_a_Shadow_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_What_You_Can_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_What_You_Can_Reconstruct_From_a_Shadow_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_What_You_Can_Reconstruct_From_a_Shadow_CVPR_2023_paper.html | CVPR 2023 | null |
Adaptive Sparse Convolutional Networks With Global Context Enhancement for Faster Object Detection on Drone Images | Bowei Du, Yecheng Huang, Jiaxin Chen, Di Huang | Object detection on drone images with low-latency is an important but challenging task on the resource-constrained unmanned aerial vehicle (UAV) platform. This paper investigates optimizing the detection head based on the sparse convolution, which proves effective in balancing the accuracy and efficiency. Nevertheless, it suffers from inadequate integration of contextual information of tiny objects as well as clumsy control of the mask ratio in the presence of foreground with varying scales. To address the issues above, we propose a novel global context-enhanced adaptive sparse convolutional network (CEASC). It first develops a context-enhanced group normalization (CE-GN) layer, by replacing the statistics based on sparsely sampled features with the global contextual ones, and then designs an adaptive multi-layer masking strategy to generate optimal mask ratios at distinct scales for compact foreground coverage, promoting both the accuracy and efficiency. Extensive experimental results on two major benchmarks, i.e. VisDrone and UAVDT, demonstrate that CEASC remarkably reduces the GFLOPs and accelerates the inference procedure when plugging into the typical state-of-the-art detection frameworks (e.g. RetinaNet and GFL V1) with competitive performance. Code is available at https://github.com/Cuogeihong/CEASC. | https://openaccess.thecvf.com/content/CVPR2023/papers/Du_Adaptive_Sparse_Convolutional_Networks_With_Global_Context_Enhancement_for_Faster_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Du_Adaptive_Sparse_Convolutional_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14488 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Adaptive_Sparse_Convolutional_Networks_With_Global_Context_Enhancement_for_Faster_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Adaptive_Sparse_Convolutional_Networks_With_Global_Context_Enhancement_for_Faster_CVPR_2023_paper.html | CVPR 2023 | null |
LidarGait: Benchmarking 3D Gait Recognition With Point Clouds | Chuanfu Shen, Chao Fan, Wei Wu, Rui Wang, George Q. Huang, Shiqi Yu | Video-based gait recognition has achieved impressive results in constrained scenarios. However, visual cameras neglect human 3D structure information, which limits the feasibility of gait recognition in the 3D wild world. Instead of extracting gait features from images, this work explores precise 3D gait features from point clouds and proposes a simple yet efficient 3D gait recognition framework, termed LidarGait. Our proposed approach projects sparse point clouds into depth maps to learn the representations with 3D geometry information, which outperforms existing point-wise and camera-based methods by a significant margin. Due to the lack of point cloud datasets, we build the first large-scale LiDAR-based gait recognition dataset, SUSTech1K, collected by a LiDAR sensor and an RGB camera. The dataset contains 25,239 sequences from 1,050 subjects and covers many variations, including visibility, views, occlusions, clothing, carrying, and scenes. Extensive experiments show that (1) 3D structure information serves as a significant feature for gait recognition. (2) LidarGait outperforms existing point-based and silhouette-based methods by a significant margin, while it also offers stable cross-view results. (3) The LiDAR sensor is superior to the RGB camera for gait recognition in the outdoor environment. The source code and dataset have been made available at https://lidargait.github.io. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shen_LidarGait_Benchmarking_3D_Gait_Recognition_With_Point_Clouds_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shen_LidarGait_Benchmarking_3D_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.10598 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_LidarGait_Benchmarking_3D_Gait_Recognition_With_Point_Clouds_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_LidarGait_Benchmarking_3D_Gait_Recognition_With_Point_Clouds_CVPR_2023_paper.html | CVPR 2023 | null |
Command-Driven Articulated Object Understanding and Manipulation | Ruihang Chu, Zhengzhe Liu, Xiaoqing Ye, Xiao Tan, Xiaojuan Qi, Chi-Wing Fu, Jiaya Jia | We present Cart, a new approach towards articulated-object manipulations by human commands. Beyond the existing work that focuses on inferring articulation structures, we further support manipulating articulated shapes to align them subject to simple command templates. The key of Cart is to utilize the prediction of object structures to connect visual observations with user commands for effective manipulations. It is achieved by encoding command messages for motion prediction and a test-time adaptation to adjust the amount of movement from only command supervision. For a rich variety of object categories, Cart can accurately manipulate object shapes and outperform the state-of-the-art approaches in understanding the inherent articulation structures. Also, it can well generalize to unseen object categories and real-world objects. We hope Cart could open new directions for instructing machines to operate articulated objects. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chu_Command-Driven_Articulated_Object_Understanding_and_Manipulation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chu_Command-Driven_Articulated_Object_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chu_Command-Driven_Articulated_Object_Understanding_and_Manipulation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chu_Command-Driven_Articulated_Object_Understanding_and_Manipulation_CVPR_2023_paper.html | CVPR 2023 | null |
D2Former: Jointly Learning Hierarchical Detectors and Contextual Descriptors via Agent-Based Transformers | Jianfeng He, Yuan Gao, Tianzhu Zhang, Zhe Zhang, Feng Wu | Establishing pixel-level matches between image pairs is vital for a variety of computer vision applications. However, achieving robust image matching remains challenging because CNN extracted descriptors usually lack discriminative ability in texture-less regions and keypoint detectors are only good at identifying keypoints with a specific level of structure. To deal with these issues, a novel image matching method is proposed by Jointly Learning Hierarchical Detectors and Contextual Descriptors via Agent-based Transformers (D2Former), including a contextual feature descriptor learning (CFDL) module and a hierarchical keypoint detector learning (HKDL) module. The proposed D2Former enjoys several merits. First, the proposed CFDL module can model long-range contexts efficiently and effectively with the aid of designed descriptor agents. Second, the HKDL module can generate keypoint detectors in a hierarchical way, which is helpful for detecting keypoints with diverse levels of structures. Extensive experimental results on four challenging benchmarks show that our proposed method significantly outperforms state-of-the-art image matching methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/He_D2Former_Jointly_Learning_Hierarchical_Detectors_and_Contextual_Descriptors_via_Agent-Based_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/He_D2Former_Jointly_Learning_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/He_D2Former_Jointly_Learning_Hierarchical_Detectors_and_Contextual_Descriptors_via_Agent-Based_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/He_D2Former_Jointly_Learning_Hierarchical_Detectors_and_Contextual_Descriptors_via_Agent-Based_CVPR_2023_paper.html | CVPR 2023 | null |
ConStruct-VL: Data-Free Continual Structured VL Concepts Learning | James Seale Smith, Paola Cascante-Bonilla, Assaf Arbelle, Donghyun Kim, Rameswar Panda, David Cox, Diyi Yang, Zsolt Kira, Rogerio Feris, Leonid Karlinsky | Recently, large-scale pre-trained Vision-and-Language (VL) foundation models have demonstrated remarkable capabilities in many zero-shot downstream tasks, achieving competitive results for recognizing objects defined by as little as short text prompts. However, it has also been shown that VL models are still brittle in Structured VL Concept (SVLC) reasoning, such as the ability to recognize object attributes, states, and inter-object relations. This leads to reasoning mistakes, which need to be corrected as they occur by teaching VL models the missing SVLC skills; often this must be done using private data where the issue was found, which naturally leads to a data-free continual (no task-id) VL learning setting. In this work, we introduce the first Continual Data-Free Structured VL Concepts Learning (ConStruct-VL) benchmark and show it is challenging for many existing data-free CL strategies. We, therefore, propose a data-free method comprised of a new approach of Adversarial Pseudo-Replay (APR) which generates adversarial reminders of past tasks from past task models. To use this method efficiently, we also propose a continual parameter-efficient Layered-LoRA (LaLo) neural architecture allowing no-memory-cost access to all past models at train time. We show this approach outperforms all data-free methods by as much as 7% while even matching some levels of experience-replay (prohibitive for applications where data-privacy must be preserved). Our code is publicly available at https://github.com/jamessealesmith/ConStruct-VL | https://openaccess.thecvf.com/content/CVPR2023/papers/Smith_ConStruct-VL_Data-Free_Continual_Structured_VL_Concepts_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Smith_ConStruct-VL_Data-Free_Continual_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Smith_ConStruct-VL_Data-Free_Continual_Structured_VL_Concepts_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Smith_ConStruct-VL_Data-Free_Continual_Structured_VL_Concepts_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Lite DETR: An Interleaved Multi-Scale Encoder for Efficient DETR | Feng Li, Ailing Zeng, Shilong Liu, Hao Zhang, Hongyang Li, Lei Zhang, Lionel M. Ni | Recent DEtection TRansformer-based (DETR) models have obtained remarkable performance. Its success cannot be achieved without the re-introduction of multi-scale feature fusion in the encoder. However, the excessively increased tokens in multi-scale features, especially for about 75% of low-level features, are quite computationally inefficient, which hinders real applications of DETR models. In this paper, we present Lite DETR, a simple yet efficient end-to-end object detection framework that can effectively reduce the GFLOPs of the detection head by 60% while keeping 99% of the original performance. Specifically, we design an efficient encoder block to update high-level features (corresponding to small-resolution feature maps) and low-level features (corresponding to large-resolution feature maps) in an interleaved way. In addition, to better fuse cross-scale features, we develop a key-aware deformable attention to predict more reliable attention weights. Comprehensive experiments validate the effectiveness and efficiency of the proposed Lite DETR, and the efficient encoder strategy can generalize well across existing DETR-based models. The code will be released after the blind review. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Lite_DETR_An_Interleaved_Multi-Scale_Encoder_for_Efficient_DETR_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Lite_DETR_An_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.07335 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Lite_DETR_An_Interleaved_Multi-Scale_Encoder_for_Efficient_DETR_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Lite_DETR_An_Interleaved_Multi-Scale_Encoder_for_Efficient_DETR_CVPR_2023_paper.html | CVPR 2023 | null |
HelixSurf: A Robust and Efficient Neural Implicit Surface Learning of Indoor Scenes With Iterative Intertwined Regularization | Zhihao Liang, Zhangjin Huang, Changxing Ding, Kui Jia | Recovery of an underlying scene geometry from multi-view images stands as a long-time challenge in computer vision research. The recent promise leverages neural implicit surface learning and differentiable volume rendering, and achieves both the recovery of scene geometry and synthesis of novel views, where deep priors of neural models are used as an inductive smoothness bias. While promising for object-level surfaces, these methods suffer when coping with complex scene surfaces. In the meanwhile, traditional multi-view stereo can recover the geometry of scenes with rich textures, by globally optimizing the local, pixel-wise correspondences across multiple views. We are thus motivated to make use of the complementary benefits from the two strategies, and propose a method termed Helix-shaped neural implicit Surface learning or HelixSurf; HelixSurf uses the intermediate prediction from one strategy as the guidance to regularize the learning of the other one, and conducts such intertwined regularization iteratively during the learning process. We also propose an efficient scheme for differentiable volume rendering in HelixSurf. Experiments on surface reconstruction of indoor scenes show that our method compares favorably with existing methods and is orders of magnitude faster, even when some of existing methods are assisted with auxiliary training data. The source code is available at https://github.com/Gorilla-Lab-SCUT/HelixSurf. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liang_HelixSurf_A_Robust_and_Efficient_Neural_Implicit_Surface_Learning_of_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liang_HelixSurf_A_Robust_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.14340 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liang_HelixSurf_A_Robust_and_Efficient_Neural_Implicit_Surface_Learning_of_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liang_HelixSurf_A_Robust_and_Efficient_Neural_Implicit_Surface_Learning_of_CVPR_2023_paper.html | CVPR 2023 | null |
Joint Appearance and Motion Learning for Efficient Rolling Shutter Correction | Bin Fan, Yuxin Mao, Yuchao Dai, Zhexiong Wan, Qi Liu | Rolling shutter correction (RSC) is becoming increasingly popular for RS cameras that are widely used in commercial and industrial applications. Despite the promising performance, existing RSC methods typically employ a two-stage network structure that ignores intrinsic information interactions and hinders fast inference. In this paper, we propose a single-stage encoder-decoder-based network, named JAMNet, for efficient RSC. It first extracts pyramid features from consecutive RS inputs, and then simultaneously refines the two complementary information (i.e., global shutter appearance and undistortion motion field) to achieve mutual promotion in a joint learning decoder. To inject sufficient motion cues for guiding joint learning, we introduce a transformer-based motion embedding module and propose to pass hidden states across pyramid levels. Moreover, we present a new data augmentation strategy "vertical flip + inverse order" to release the potential of the RSC datasets. Experiments on various benchmarks show that our approach surpasses the state-of-the-art methods by a large margin, especially with a 4.7 dB PSNR leap on real-world RSC. Code is available at https://github.com/GitCVfb/JAMNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fan_Joint_Appearance_and_Motion_Learning_for_Efficient_Rolling_Shutter_Correction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fan_Joint_Appearance_and_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fan_Joint_Appearance_and_Motion_Learning_for_Efficient_Rolling_Shutter_Correction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fan_Joint_Appearance_and_Motion_Learning_for_Efficient_Rolling_Shutter_Correction_CVPR_2023_paper.html | CVPR 2023 | null |
Towards a Smaller Student: Capacity Dynamic Distillation for Efficient Image Retrieval | Yi Xie, Huaidong Zhang, Xuemiao Xu, Jianqing Zhu, Shengfeng He | Previous Knowledge Distillation based efficient image retrieval methods employ a lightweight network as the student model for fast inference. However, the lightweight student model lacks adequate representation capacity for effective knowledge imitation during the most critical early training period, causing final performance degeneration. To tackle this issue, we propose a Capacity Dynamic Distillation framework, which constructs a student model with editable representation capacity. Specifically, the employed student model is initially a heavy model to fruitfully learn distilled knowledge in the early training epochs, and the student model is gradually compressed during the training. To dynamically adjust the model capacity, our dynamic framework inserts a learnable convolutional layer within each residual block in the student model as the channel importance indicator. The indicator is optimized simultaneously by the image retrieval loss and the compression loss, and a retrieval-guided gradient resetting mechanism is proposed to release the gradient conflict. Extensive experiments show that our method has superior inference speed and accuracy, e.g., on the VeRi-776 dataset, given the ResNet101 as a teacher, our method saves 67.13% model parameters and 65.67% FLOPs without sacrificing accuracy. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_Towards_a_Smaller_Student_Capacity_Dynamic_Distillation_for_Efficient_Image_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xie_Towards_a_Smaller_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.09230 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_Towards_a_Smaller_Student_Capacity_Dynamic_Distillation_for_Efficient_Image_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_Towards_a_Smaller_Student_Capacity_Dynamic_Distillation_for_Efficient_Image_CVPR_2023_paper.html | CVPR 2023 | null |
Federated Incremental Semantic Segmentation | Jiahua Dong, Duzhen Zhang, Yang Cong, Wei Cong, Henghui Ding, Dengxin Dai | Federated learning-based semantic segmentation (FSS) has drawn widespread attention via decentralized training on local clients. However, most FSS models assume categories are fxed in advance, thus heavily undergoing forgetting on old categories in practical applications where local clients receive new categories incrementally while have no memory storage to access old classes. Moreover, new clients collecting novel classes may join in the global training of FSS, which further exacerbates catastrophic forgetting. To surmount the above challenges, we propose a Forgetting-Balanced Learning (FBL) model to address heterogeneous forgetting on old classes from both intra-client and inter-client aspects. Specifically, under the guidance of pseudo labels generated via adaptive class-balanced pseudo labeling, we develop a forgetting-balanced semantic compensation loss and a forgetting-balanced relation consistency loss to rectify intra-client heterogeneous forgetting of old categories with background shift. It performs balanced gradient propagation and relation consistency distillation within local clients. Moreover, to tackle heterogeneous forgetting from inter-client aspect, we propose a task transition monitor. It can identify new classes under privacy protection and store the latest old global model for relation distillation. Qualitative experiments reveal large improvement of our model against comparison methods. The code is available at https://github.com/JiahuaDong/FISS. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dong_Federated_Incremental_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dong_Federated_Incremental_Semantic_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.04620 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dong_Federated_Incremental_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dong_Federated_Incremental_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
3D-Aware Facial Landmark Detection via Multi-View Consistent Training on Synthetic Data | Libing Zeng, Lele Chen, Wentao Bao, Zhong Li, Yi Xu, Junsong Yuan, Nima Khademi Kalantari | Accurate facial landmark detection on wild images plays an essential role in human-computer interaction, entertainment, and medical applications. Existing approaches have limitations in enforcing 3D consistency while detecting 3D/2D facial landmarks due to the lack of multi-view in-the-wild training data. Fortunately, with the recent advances in generative visual models and neural rendering, we have witnessed rapid progress towards high quality 3D image synthesis. In this work, we leverage such approaches to construct a synthetic dataset and propose a novel multi-view consistent learning strategy to improve 3D facial landmark detection accuracy on in-the-wild images. The proposed 3D-aware module can be plugged into any learning-based landmark detection algorithm to enhance its accuracy. We demonstrate the superiority of the proposed plug-in module with extensive comparison against state-of-the-art methods on several real and synthetic datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zeng_3D-Aware_Facial_Landmark_Detection_via_Multi-View_Consistent_Training_on_Synthetic_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zeng_3D-Aware_Facial_Landmark_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zeng_3D-Aware_Facial_Landmark_Detection_via_Multi-View_Consistent_Training_on_Synthetic_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zeng_3D-Aware_Facial_Landmark_Detection_via_Multi-View_Consistent_Training_on_Synthetic_CVPR_2023_paper.html | CVPR 2023 | null |
Attention-Based Point Cloud Edge Sampling | Chengzhi Wu, Junwei Zheng, Julius Pfrommer, Jürgen Beyerer | Point cloud sampling is a less explored research topic for this data representation. The most commonly used sampling methods are still classical random sampling and farthest point sampling. With the development of neural networks, various methods have been proposed to sample point clouds in a task-based learning manner. However, these methods are mostly generative-based, rather than selecting points directly using mathematical statistics. Inspired by the Canny edge detection algorithm for images and with the help of the attention mechanism, this paper proposes a non-generative Attention-based Point cloud Edge Sampling method (APES), which captures salient points in the point cloud outline. Both qualitative and quantitative experimental results show the superior performance of our sampling method on common benchmark tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Attention-Based_Point_Cloud_Edge_Sampling_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2302.14673 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Attention-Based_Point_Cloud_Edge_Sampling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Attention-Based_Point_Cloud_Edge_Sampling_CVPR_2023_paper.html | CVPR 2023 | null |
Avatars Grow Legs: Generating Smooth Human Motion From Sparse Tracking Inputs With Diffusion Model | Yuming Du, Robin Kips, Albert Pumarola, Sebastian Starke, Ali Thabet, Artsiom Sanakoyeu | With the recent surge in popularity of AR/VR applications, realistic and accurate control of 3D full-body avatars has become a highly demanded feature. A particular challenge is that only a sparse tracking signal is available from standalone HMDs (Head Mounted Devices), often limited to tracking the user's head and wrists. While this signal is resourceful for reconstructing the upper body motion, the lower body is not tracked and must be synthesized from the limited information provided by the upper body joints. In this paper, we present AGRoL, a novel conditional diffusion model specifically designed to track full bodies given sparse upper-body tracking signals. Our model is based on a simple multi-layer perceptron (MLP) architecture and a novel conditioning scheme for motion data. It can predict accurate and smooth full-body motion, particularly the challenging lower body movement. Unlike common diffusion architectures, our compact architecture can run in real-time, making it suitable for online body-tracking applications. We train and evaluate our model on AMASS motion capture dataset, and demonstrate that our approach outperforms state-of-the-art methods in generated motion accuracy and smoothness. We further justify our design choices through extensive experiments and ablation studies. | https://openaccess.thecvf.com/content/CVPR2023/papers/Du_Avatars_Grow_Legs_Generating_Smooth_Human_Motion_From_Sparse_Tracking_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Du_Avatars_Grow_Legs_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2304.08577 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Avatars_Grow_Legs_Generating_Smooth_Human_Motion_From_Sparse_Tracking_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Avatars_Grow_Legs_Generating_Smooth_Human_Motion_From_Sparse_Tracking_CVPR_2023_paper.html | CVPR 2023 | null |
MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures | Zhiqin Chen, Thomas Funkhouser, Peter Hedman, Andrea Tagliasacchi | Neural Radiance Fields (NeRFs) have demonstrated amazing ability to synthesize images of 3D scenes from novel views. However, they rely upon specialized volumetric rendering algorithms based on ray marching that are mismatched to the capabilities of widely deployed graphics hardware. This paper introduces a new NeRF representation based on textured polygons that can synthesize novel images efficiently with standard rendering pipelines. The NeRF is represented as a set of polygons with textures representing binary opacities and feature vectors. Traditional rendering of the polygons with a z-buffer yields an image with features at every pixel, which are interpreted by a small, view-dependent MLP running in a fragment shader to produce a final pixel color. This approach enables NeRFs to be rendered with the traditional polygon rasterization pipeline, which provides massive pixel-level parallelism, achieving interactive frame rates on a wide range of compute platforms, including mobile phones. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_MobileNeRF_Exploiting_the_Polygon_Rasterization_Pipeline_for_Efficient_Neural_Field_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_MobileNeRF_Exploiting_the_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2208.00277 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_MobileNeRF_Exploiting_the_Polygon_Rasterization_Pipeline_for_Efficient_Neural_Field_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_MobileNeRF_Exploiting_the_Polygon_Rasterization_Pipeline_for_Efficient_Neural_Field_CVPR_2023_paper.html | CVPR 2023 | null |
Pseudo-Label Guided Contrastive Learning for Semi-Supervised Medical Image Segmentation | Hritam Basak, Zhaozheng Yin | Although recent works in semi-supervised learning (SemiSL) have accomplished significant success in natural image segmentation, the task of learning discriminative representations from limited annotations has been an open problem in medical images. Contrastive Learning (CL) frameworks use the notion of similarity measure which is useful for classification problems, however, they fail to transfer these quality representations for accurate pixel-level segmentation. To this end, we propose a novel semi-supervised patch-based CL framework for medical image segmentation without using any explicit pretext task. We harness the power of both CL and SemiSL, where the pseudo-labels generated from SemiSL aid CL by providing additional guidance, whereas discriminative class information learned in CL leads to accurate multi-class segmentation. Additionally, we formulate a novel loss that synergistically encourages inter-class separability and intra-class compactness among the learned representations. A new inter-patch semantic disparity mapping using average patch entropy is employed for a guided sampling of positives and negatives in the proposed CL framework. Experimental analysis on three publicly available datasets of multiple modalities reveals the superiority of our proposed method as compared to the state-of-the-art methods. Code is available at: https://github.com/hritam-98/PatchCL-MedSeg. | https://openaccess.thecvf.com/content/CVPR2023/papers/Basak_Pseudo-Label_Guided_Contrastive_Learning_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Basak_Pseudo-Label_Guided_Contrastive_Learning_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Basak_Pseudo-Label_Guided_Contrastive_Learning_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Neural Proto-Face Field for Disentangled 3D Face Modeling in the Wild | Zhenyu Zhang, Renwang Chen, Weijian Cao, Ying Tai, Chengjie Wang | Generative models show good potential for recovering 3D faces beyond limited shape assumptions. While plausible details and resolutions are achieved, these models easily fail under extreme conditions of pose, shadow or appearance, due to the entangled fitting or lack of multi-view priors. To address this problem, this paper presents a novel Neural Proto-face Field (NPF) for unsupervised robust 3D face modeling. Instead of using constrained images as Neural Radiance Field (NeRF), NPF disentangles the common/specific facial cues, i.e., ID, expression and scene-specific details from in-the-wild photo collections. Specifically, NPF learns a face prototype to aggregate 3D-consistent identity via uncertainty modeling, extracting multi-image priors from a photo collection. NPF then learns to deform the prototype with the appropriate facial expressions, constrained by a loss of expression consistency and personal idiosyncrasies. Finally, NPF is optimized to fit a target image in the collection, recovering specific details of appearance and geometry. In this way, the generative model benefits from multi-image priors and meaningful facial structures. Extensive experiments on benchmarks show that NPF recovers superior or competitive facial shapes and textures, compared to state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Learning_Neural_Proto-Face_Field_for_Disentangled_3D_Face_Modeling_in_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Learning_Neural_Proto-Face_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Learning_Neural_Proto-Face_Field_for_Disentangled_3D_Face_Modeling_in_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Learning_Neural_Proto-Face_Field_for_Disentangled_3D_Face_Modeling_in_CVPR_2023_paper.html | CVPR 2023 | null |
Self-Supervised Geometry-Aware Encoder for Style-Based 3D GAN Inversion | Yushi Lan, Xuyi Meng, Shuai Yang, Chen Change Loy, Bo Dai | StyleGAN has achieved great progress in 2D face reconstruction and semantic editing via image inversion and latent editing. While studies over extending 2D StyleGAN to 3D faces have emerged, a corresponding generic 3D GAN inversion framework is still missing, limiting the applications of 3D face reconstruction and semantic editing. In this paper, we study the challenging problem of 3D GAN inversion where a latent code is predicted given a single face image to faithfully recover its 3D shapes and detailed textures. The problem is ill-posed: innumerable compositions of shape and texture could be rendered to the current image. Furthermore, with the limited capacity of a global latent code, 2D inversion methods cannot preserve faithful shape and texture at the same time when applied to 3D models. To solve this problem, we devise an effective self-training scheme to constrain the learning of inversion. The learning is done efficiently without any real-world 2D-3D training pairs but proxy samples generated from a 3D GAN. In addition, apart from a global latent code that captures the coarse shape and texture information, we augment the generation network with a local branch, where pixel-aligned features are added to faithfully reconstruct face details. We further consider a new pipeline to perform 3D view-consistent editing. Extensive experiments show that our method outperforms state-of-the-art inversion methods in both shape and texture reconstruction quality. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lan_Self-Supervised_Geometry-Aware_Encoder_for_Style-Based_3D_GAN_Inversion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lan_Self-Supervised_Geometry-Aware_Encoder_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.07409 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lan_Self-Supervised_Geometry-Aware_Encoder_for_Style-Based_3D_GAN_Inversion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lan_Self-Supervised_Geometry-Aware_Encoder_for_Style-Based_3D_GAN_Inversion_CVPR_2023_paper.html | CVPR 2023 | null |
PC2: Projection-Conditioned Point Cloud Diffusion for Single-Image 3D Reconstruction | Luke Melas-Kyriazi, Christian Rupprecht, Andrea Vedaldi | Reconstructing the 3D shape of an object from a single RGB image is a long-standing problem in computer vision. In this paper, we propose a novel method for single-image 3D reconstruction which generates a sparse point cloud via a conditional denoising diffusion process. Our method takes as input a single RGB image along with its camera pose and gradually denoises a set of 3D points, whose positions are initially sampled randomly from a three-dimensional Gaussian distribution, into the shape of an object. The key to our method is a geometrically-consistent conditioning process which we call projection conditioning: at each step in the diffusion process, we project local image features onto the partially-denoised point cloud from the given camera pose. This projection conditioning process enables us to generate high-resolution sparse geometries that are well-aligned with the input image and can additionally be used to predict point colors after shape reconstruction. Moreover, due to the probabilistic nature of the diffusion process, our method is naturally capable of generating multiple different shapes consistent with a single input image. In contrast to prior work, our approach not only performs well on synthetic benchmarks but also gives large qualitative improvements on complex real-world data. | https://openaccess.thecvf.com/content/CVPR2023/papers/Melas-Kyriazi_PC2_Projection-Conditioned_Point_Cloud_Diffusion_for_Single-Image_3D_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Melas-Kyriazi_PC2_Projection-Conditioned_Point_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Melas-Kyriazi_PC2_Projection-Conditioned_Point_Cloud_Diffusion_for_Single-Image_3D_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Melas-Kyriazi_PC2_Projection-Conditioned_Point_Cloud_Diffusion_for_Single-Image_3D_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
Gradient-Based Uncertainty Attribution for Explainable Bayesian Deep Learning | Hanjing Wang, Dhiraj Joshi, Shiqiang Wang, Qiang Ji | Predictions made by deep learning models are prone to data perturbations, adversarial attacks, and out-of-distribution inputs. To build a trusted AI system, it is therefore critical to accurately quantify the prediction uncertainties. While current efforts focus on improving uncertainty quantification accuracy and efficiency, there is a need to identify uncertainty sources and take actions to mitigate their effects on predictions. Therefore, we propose to develop explainable and actionable Bayesian deep learning methods to not only perform accurate uncertainty quantification but also explain the uncertainties, identify their sources, and propose strategies to mitigate the uncertainty impacts. Specifically, we introduce a gradient-based uncertainty attribution method to identify the most problematic regions of the input that contribute to the prediction uncertainty. Compared to existing methods, the proposed UA-Backprop has competitive accuracy, relaxed assumptions, and high efficiency. Moreover, we propose an uncertainty mitigation strategy that leverages the attribution results as attention to further improve the model performance. Both qualitative and quantitative evaluations are conducted to demonstrate the effectiveness of our proposed methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Gradient-Based_Uncertainty_Attribution_for_Explainable_Bayesian_Deep_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Gradient-Based_Uncertainty_Attribution_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.04824 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Gradient-Based_Uncertainty_Attribution_for_Explainable_Bayesian_Deep_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Gradient-Based_Uncertainty_Attribution_for_Explainable_Bayesian_Deep_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Manipulating Transfer Learning for Property Inference | Yulong Tian, Fnu Suya, Anshuman Suri, Fengyuan Xu, David Evans | Transfer learning is a popular method for tuning pretrained (upstream) models for different downstream tasks using limited data and computational resources. We study how an adversary with control over an upstream model used in transfer learning can conduct property inference attacks on a victim's tuned downstream model. For example, to infer the presence of images of a specific individual in the downstream training set. We demonstrate attacks in which an adversary can manipulate the upstream model to conduct highly effective and specific property inference attacks (AUC score > 0.9), without incurring significant performance loss on the main task. The main idea of the manipulation is to make the upstream model generate activations (intermediate features) with different distributions for samples with and without a target property, thus enabling the adversary to distinguish easily between downstream models trained with and without training examples that have the target property. Our code is available at https://github.com/yulongt23/Transfer-Inference. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tian_Manipulating_Transfer_Learning_for_Property_Inference_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tian_Manipulating_Transfer_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11643 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_Manipulating_Transfer_Learning_for_Property_Inference_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_Manipulating_Transfer_Learning_for_Property_Inference_CVPR_2023_paper.html | CVPR 2023 | null |
POEM: Reconstructing Hand in a Point Embedded Multi-View Stereo | Lixin Yang, Jian Xu, Licheng Zhong, Xinyu Zhan, Zhicheng Wang, Kejian Wu, Cewu Lu | Enable neural networks to capture 3D geometrical-aware features is essential in multi-view based vision tasks. Previous methods usually encode the 3D information of multi-view stereo into the 2D features. In contrast, we present a novel method, named POEM, that directly operates on the 3D POints Embedded in the Multi-view stereo for reconstructing hand mesh in it. Point is a natural form of 3D information and an ideal medium for fusing features across views, as it has different projections on different views. Our method is thus in light of a simple yet effective idea, that a complex 3D hand mesh can be represented by a set of 3D points that 1) are embedded in the multi-view stereo, 2) carry features from the multi-view images, and 3) encircle the hand. To leverage the power of points, we design two operations: point-based feature fusion and cross-set point attention mechanism. Evaluation on three challenging multi-view datasets shows that POEM outperforms the state-of-the-art in hand mesh reconstruction. Code and models are available for research at github.com/lixiny/POEM | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_POEM_Reconstructing_Hand_in_a_Point_Embedded_Multi-View_Stereo_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_POEM_Reconstructing_Hand_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.04038 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_POEM_Reconstructing_Hand_in_a_Point_Embedded_Multi-View_Stereo_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_POEM_Reconstructing_Hand_in_a_Point_Embedded_Multi-View_Stereo_CVPR_2023_paper.html | CVPR 2023 | null |
BUFFER: Balancing Accuracy, Efficiency, and Generalizability in Point Cloud Registration | Sheng Ao, Qingyong Hu, Hanyun Wang, Kai Xu, Yulan Guo | An ideal point cloud registration framework should have superior accuracy, acceptable efficiency, and strong generalizability. However, this is highly challenging since existing registration techniques are either not accurate enough, far from efficient, or generalized poorly. It remains an open question that how to achieve a satisfying balance between this three key elements. In this paper, we propose BUFFER, a point cloud registration method for balancing accuracy, efficiency, and generalizability. The key to our approach is to take advantage of both point-wise and patch-wise techniques, while overcoming the inherent drawbacks simultaneously. Different from a simple combination of existing methods, each component of our network has been carefully crafted to tackle specific issues. Specifically, a Point-wise Learner is first introduced to enhance computational efficiency by predicting keypoints and improving the representation capacity of features by estimating point orientations, a Patch-wise Embedder which leverages a lightweight local feature learner is then deployed to extract efficient and general patch features. Additionally, an Inliers Generator which combines simple neural layers and general features is presented to search inlier correspondences. Extensive experiments on real-world scenarios demonstrate that our method achieves the best of both worlds in accuracy, efficiency, and generalization. In particular, our method not only reaches the highest success rate on unseen domains, but also is almost 30 times faster than the strong baselines specializing in generalization. Code is available at https://github.com/aosheng1996/BUFFER. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ao_BUFFER_Balancing_Accuracy_Efficiency_and_Generalizability_in_Point_Cloud_Registration_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ao_BUFFER_Balancing_Accuracy_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ao_BUFFER_Balancing_Accuracy_Efficiency_and_Generalizability_in_Point_Cloud_Registration_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ao_BUFFER_Balancing_Accuracy_Efficiency_and_Generalizability_in_Point_Cloud_Registration_CVPR_2023_paper.html | CVPR 2023 | null |
CrOC: Cross-View Online Clustering for Dense Visual Representation Learning | Thomas Stegmüller, Tim Lebailly, Behzad Bozorgtabar, Tinne Tuytelaars, Jean-Philippe Thiran | Learning dense visual representations without labels is an arduous task and more so from scene-centric data. We propose to tackle this challenging problem by proposing a Cross-view consistency objective with an Online Clustering mechanism (CrOC) to discover and segment the semantics of the views. In the absence of hand-crafted priors, the resulting method is more generalizable and does not require a cumbersome pre-processing step. More importantly, the clustering algorithm conjointly operates on the features of both views, thereby elegantly bypassing the issue of content not represented in both views and the ambiguous matching of objects from one crop to the other. We demonstrate excellent performance on linear and unsupervised segmentation transfer tasks on various datasets and similarly for video object segmentation. Our code and pre-trained models are publicly available at https://github.com/stegmuel/CrOC. | https://openaccess.thecvf.com/content/CVPR2023/papers/Stegmuller_CrOC_Cross-View_Online_Clustering_for_Dense_Visual_Representation_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Stegmuller_CrOC_Cross-View_Online_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Stegmuller_CrOC_Cross-View_Online_Clustering_for_Dense_Visual_Representation_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Stegmuller_CrOC_Cross-View_Online_Clustering_for_Dense_Visual_Representation_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Class Adaptive Network Calibration | Bingyuan Liu, Jérôme Rony, Adrian Galdran, Jose Dolz, Ismail Ben Ayed | Recent studies have revealed that, beyond conventional accuracy, calibration should also be considered for training modern deep neural networks. To address miscalibration during learning, some methods have explored different penalty functions as part of the learning objective, alongside a standard classification loss, with a hyper-parameter controlling the relative contribution of each term. Nevertheless, these methods share two major drawbacks: 1) the scalar balancing weight is the same for all classes, hindering the ability to address different intrinsic difficulties or imbalance among classes; and 2) the balancing weight is usually fixed without an adaptive strategy, which may prevent from reaching the best compromise between accuracy and calibration, and requires hyper-parameter search for each application. We propose Class Adaptive Label Smoothing (CALS) for calibrating deep networks, which allows to learn class-wise multipliers during training, yielding a powerful alternative to common label smoothing penalties. Our method builds on a general Augmented Lagrangian approach, a well-established technique in constrained optimization, but we introduce several modifications to tailor it for large-scale, class-adaptive training. Comprehensive evaluation and multiple comparisons on a variety of benchmarks, including standard and long-tailed image classification, semantic segmentation, and text classification, demonstrate the superiority of the proposed method. The code is available at https://github.com/by-liu/CALS. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Class_Adaptive_Network_Calibration_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Class_Adaptive_Network_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.15088 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Class_Adaptive_Network_Calibration_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Class_Adaptive_Network_Calibration_CVPR_2023_paper.html | CVPR 2023 | null |
DrapeNet: Garment Generation and Self-Supervised Draping | Luca De Luigi, Ren Li, Benoît Guillard, Mathieu Salzmann, Pascal Fua | Recent approaches to drape garments quickly over arbitrary human bodies leverage self-supervision to eliminate the need for large training sets. However, they are designed to train one network per clothing item, which severely limits their generalization abilities. In our work, we rely on self-supervision to train a single network to drape multiple garments. This is achieved by predicting a 3D deformation field conditioned on the latent codes of a generative network, which models garments as unsigned distance fields. Our pipeline can generate and drape previously unseen garments of any topology, whose shape can be edited by manipulating their latent codes. Being fully differentiable, our formulation makes it possible to recover accurate 3D models of garments from partial observations -- images or 3D scans -- via gradient descent. Our code is publicly available at https://github.com/liren2515/DrapeNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/De_Luigi_DrapeNet_Garment_Generation_and_Self-Supervised_Draping_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/De_Luigi_DrapeNet_Garment_Generation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.11277 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/De_Luigi_DrapeNet_Garment_Generation_and_Self-Supervised_Draping_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/De_Luigi_DrapeNet_Garment_Generation_and_Self-Supervised_Draping_CVPR_2023_paper.html | CVPR 2023 | null |
Evading Forensic Classifiers With Attribute-Conditioned Adversarial Faces | Fahad Shamshad, Koushik Srivatsan, Karthik Nandakumar | The ability of generative models to produce highly realistic synthetic face images has raised security and ethical concerns. As a first line of defense against such fake faces, deep learning based forensic classifiers have been developed. While these forensic models can detect whether a face image is synthetic or real with high accuracy, they are also vulnerable to adversarial attacks. Although such attacks can be highly successful in evading detection by forensic classifiers, they introduce visible noise patterns that are detectable through careful human scrutiny. Additionally, these attacks assume access to the target model(s) which may not always be true. Attempts have been made to directly perturb the latent space of GANs to produce adversarial fake faces that can circumvent forensic classifiers. In this work, we go one step further and show that it is possible to successfully generate adversarial fake faces with a specified set of attributes (e.g., hair color, eye size, race, gender, etc.). To achieve this goal, we leverage the state-of-the-art generative model StyleGAN with disentangled representations, which enables a range of modifications without leaving the manifold of natural images. We propose a framework to search for adversarial latent codes within the feature space of StyleGAN, where the search can be guided either by a text prompt or a reference image. We also propose a meta-learning based optimization strategy to achieve transferable performance on unknown target models. Extensive experiments demonstrate that the proposed approach can produce semantically manipulated adversarial fake faces, which are true to the specified attribute set and can successfully fool forensic face classifiers, while remaining undetectable by humans. Code: https://github.com/koushiksrivats/face_attribute_attack. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shamshad_Evading_Forensic_Classifiers_With_Attribute-Conditioned_Adversarial_Faces_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shamshad_Evading_Forensic_Classifiers_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shamshad_Evading_Forensic_Classifiers_With_Attribute-Conditioned_Adversarial_Faces_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shamshad_Evading_Forensic_Classifiers_With_Attribute-Conditioned_Adversarial_Faces_CVPR_2023_paper.html | CVPR 2023 | null |
FeatureBooster: Boosting Feature Descriptors With a Lightweight Neural Network | Xinjiang Wang, Zeyu Liu, Yu Hu, Wei Xi, Wenxian Yu, Danping Zou | We introduce a lightweight network to improve descriptors of keypoints within the same image. The network takes the original descriptors and the geometric properties of keypoints as the input, and uses an MLP-based self-boosting stage and a Transformer-based cross-boosting stage to enhance the descriptors. The boosted descriptors can be either real-valued or binary ones. We use the proposed network to boost both hand-crafted (ORB, SIFT) and the state-of-the-art learning-based descriptors (SuperPoint, ALIKE) and evaluate them on image matching, visual localization, and structure-from-motion tasks. The results show that our method significantly improves the performance of each task, particularly in challenging cases such as large illumination changes or repetitive patterns. Our method requires only 3.2ms on desktop GPU and 27ms on embedded GPU to process 2000 features, which is fast enough to be applied to a practical system. The code and trained weights are publicly available at github.com/SJTU-ViSYS/FeatureBooster. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_FeatureBooster_Boosting_Feature_Descriptors_With_a_Lightweight_Neural_Network_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_FeatureBooster_Boosting_Feature_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.15069 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_FeatureBooster_Boosting_Feature_Descriptors_With_a_Lightweight_Neural_Network_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_FeatureBooster_Boosting_Feature_Descriptors_With_a_Lightweight_Neural_Network_CVPR_2023_paper.html | CVPR 2023 | null |
Subsets and Splits