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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023"

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Posterior Promoted GAN with Distribution Discriminator for Unsupervised Image Synthesis

Posterior Promoted GAN with Distribution Discriminator for U...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Xianchao Cheng, Ziyang Zhang, Xiaotong Liu, Han Dalian Univ Technol Sch Software Key Lab Ubiquitous Network & Serv Software Liaoni Dalian Peoples R China

ISBN: (纸本)9781665445092

Sufficient real information in generator is a critical point for the generation ability of GAN. However, GAN and its variants suffer from lack of this point, resulting in brittle training processes. In this paper, we propose a novel variant of GAN, Posterior Promoted GAN (P(2)GAN), which promotes generator with the real information in the posterior distribution produced by discriminator. In our framework, different from other variants of GAN, the discriminator maps images to a multivariate Gaussian distribution and extracts real information. The generator employs the real information by AdaIN and a latent code regularizer. Besides, reparameterization trick and pretraining is applied to guarantee a stable training process in practice. The convergence of P(2)GAN is theoretically proved. Experimental results on typical high-dimensional multi-modal datasets demonstrate that P(2)GAN has achieved comparable results with the state-of-the-art variants of GAN on unsupervised image synthesis.

关键词： Training Manifolds Interpolation computer vision Image synthesis Computational modeling Gaussian distribution

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Neural Lumigraph Rendering

Neural Lumigraph Rendering

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Kellnhofer, Petr Jebe, Lars C. Jones, Andrew Spicer, Ryan Pulli, Kari Wetzstein, Gordon Raxium Fremont CA 94539 USA Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781665445092

Novel view synthesis is a challenging and ill-posed inverse rendering problem. Neural rendering techniques have recently achieved photorealistic image quality for this task. State-of-the-art (SOTA) neural volume rendering approaches, however, are slow to train and require minutes of inference (i.e., rendering) time for high image resolutions. We adopt high-capacity neural scene representations with periodic activations for jointly optimizing an implicit surface and a radiance field of a scene supervised exclusively with posed 2D images. Our neural rendering pipeline accelerates SOTA neural volume rendering by about two orders of magnitude and our implicit surface representation is unique in allowing us to export a mesh with view-dependent texture information. Thus, like other implicit surface representations, ours is compatible with traditional graphics pipelines, enabling real-time rendering rates, while achieving unprecedented image quality compared to other surface methods. We assess the quality of our approach using existing datasets as well as high-quality 3D face data captured with a custom multi-camera rig.

关键词： Graphics Image quality Three-dimensional displays Image resolution Face recognition Pipelines Rendering (computer graphics)

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NIKI: Neural Inverse Kinematics with Invertible Neural Networks for 3D Human Pose and Shape Estimation

NIKI: Neural Inverse Kinematics with Invertible Neural Netwo...

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conference on computer vision and pattern recognition (cvpr)

作者： Jiefeng Li Siyuan Bian Qi Liu Jiasheng Tang Fan Wang Cewu Lu Department of Computer Science and Engineering Shanghai Jiao Tong University Alibaba Group MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University

With the progress of 3D human pose and shape estimation, state-of-the-art methods can either be robust to occlusions or obtain pixel-aligned accuracy in non-occlusion cases. However, they cannot obtain robustness and mesh-image alignment at the same time. In this work, we present NIKI (Neural Inverse Kinematics with Invertible Neural Network), which models bidirectional errors to improve the robustness to occlusions and obtain pixel-aligned accuracy. NIKI can learn from both the forward and inverse processes with invertible networks. In the inverse process, the model separates the error from the plausible 3D pose manifold for a robust 3D human pose estimation. In the forward process, we enforce the zero-error boundary conditions to improve the sensitivity to reliable joint positions for better mesh-image alignment. Furthermore, NIKI emulates the analytical inverse kinematics algorithms with the twist-and-swing decomposition for better interpretability. Experiments on standard and occlusion-specific benchmarks demonstrate the effectiveness of NIKI, where we exhibit robust and well-aligned results simultaneously. Code is available at https://***/Jeff-sjtu/NIKI.

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NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Dataset and Study

NTIRE 2021 Challenge on Quality Enhancement of Compressed Vi...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Yang, Ren Timofte, Radu Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9781665448994

This paper introduces a novel dataset for video enhancement and studies the state-of-the-art methods of the NTIRE 2021 challenge on quality enhancement of compressed video. The challenge is the first NTIRE challenge in this direction, with three competitions, hundreds of participants and tens of proposed solutions. Our newly collected Large-scale Diverse Video (LDV) dataset is employed in the challenge. In our study, we analyze the solutions of the challenges and several representative methods from previous literature on the proposed LDV dataset. We find that the NTIRE 2021 challenge advances the state-of-theart of quality enhancement on compressed video.

关键词： computer vision Databases conferences Benchmark testing Solids pattern recognition

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Variational Prototype Learning for Deep Face recognition

Variational Prototype Learning for Deep Face Recognition

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Deng, Jiankang Guo, Jia Yang, Jing Lattas, Alexandros Zafeiriou, Stefanos Huawei Shenzhen Peoples R China Imperial Coll London England InsightFace London England Univ Nottingham Nottingham England

ISBN: (纸本)9781665445092

Deep face recognition has achieved remarkable improvements due to the introduction of margin-based soft-max loss, in which the prototype stored in the last linear layer represents the center of each class. In these methods, training samples are enforced to be close to positive prototypes and far apart from negative prototypes by a clear margin. However, we argue that prototype learning only employs sample-to-prototype comparisons without considering sample-to-sample comparisons during training and the low loss value gives us an illusion of perfect feature embedding, impeding the further exploration of SGD. To this end, we propose Variational Prototype Learning (VPL), which represents every class as a distribution instead of a point in the latent space. By identifying the slow feature drift phenomenon, we directly inject memorized features into prototypes to approximate variational prototype sampling. The proposed VPL can simulate sample-to-sample comparisons within the classification framework, encouraging the SGD solver to be more exploratory, while boosting performance. Moreover, VPL is conceptually simple, easy to implement, computationally efficient and memory saving. We present extensive experimental results on popular benchmarks, which demonstrate the superiority of the proposed VPL method over the state-of-the-art competitors.

关键词： Training Learning systems computer vision Face recognition Memory management Prototypes Benchmark testing

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Spatially-Varying Outdoor Lighting Estimation from Intrinsics

Spatially-Varying Outdoor Lighting Estimation from Intrinsic...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zhu, Yongjie Zhang, Yinda Li, Si Shi, Boxin Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China Google Mountain View CA 94043 USA Peking Univ Dept Comp Sci & Technol NELVT Beijing Peoples R China Peking Univ Inst Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9781665445092

We present SOLID-Net, a neural network for spatially-varying outdoor lighting estimation from a single outdoor image for any 2D pixel location. Previous work has used a unified sky environment map to represent outdoor lighting. Instead, we generate spatially-varying local lighting environment maps by combining global sky environment map with warped image information according to geometric information estimated from intrinsics. As no outdoor dataset with image and local lighting ground truth is readily available, we introduce the SOLID-Img dataset with physically-based rendered images and their corresponding intrinsic and lighting information. We train a deep neural network to regress intrinsic cues with physically-based constraints and use them to conduct global and local lightings estimation. Experiments on both synthetic and real datasets show that SOLID-Net significantly outperforms previous methods.

关键词： Deep learning computer vision Neural networks Lighting Estimation pattern recognition

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Co-Grounding Networks with Semantic Attention for Referring Expression Comprehension in Videos

Co-Grounding Networks with Semantic Attention for Referring ...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Song, Sijie Lin, Xudong Liu, Jiaying Guo, Zongming Chang, Shih-Fu Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China Columbia Univ DVMM Lab New York NY USA

ISBN: (纸本)9781665445092

In this paper, we address the problem of referring expression comprehension in videos, which is challenging due to complex expression and scene dynamics. Unlike previous methods which solve the problem in multiple stages (i.e., tracking, proposal-based matching), we tackle the problem from a novel perspective, co-grounding, with an elegant one-stage framework. We enhance the single-frame grounding accuracy by semantic attention learning and improve the cross-frame grounding consistency with co-grounding feature learning. Semantic attention learning explicitly parses referring cues in different attributes to reduce the ambiguity in the complex expression. Co-grounding feature learning boosts visual feature representations by integrating temporal correlation to reduce the ambiguity caused by scene dynamics. Experiment results demonstrate the superiority of our framework on the video grounding datasets VID and LiOTB in generating accurate and stable results across frames. Our model is also applicable to referring expression comprehension in images, illustrated by the improved performance on the RefCOCO dataset.

关键词： Visualization computer vision Correlation Grounding Computational modeling Semantics Benchmark testing

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The Blessings of Unlabeled Background in Untrimmed Videos

The Blessings of Unlabeled Background in Untrimmed Videos

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Liu, Yuan Chen, Jingyuan Chen, Zhenfang Deng, Bing Huang, Jianqiang Zhang, Hanwang Alibaba Grp Hangzhou Peoples R China Univ Hong Kong Hong Kong Peoples R China Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9781665445092

Weakly-supervised Temporal Action Localization (WTAL) aims to detect the action segments with only video-level action labels in training. The key challenge is how to distinguish the action of interest segments from the background, which is unlabelled even on the video-level. While previous works treat the background as "curses", we consider it as "blessings". Specifically, we first use causal analysis to point out that the common localization errors are due to the unobserved confounder that resides ubiquitously in visual recognition. Then, we propose a Temporal Smoothing PCA-based (TS-PCA) deconfounder, which exploits the unlabelled background to model an observed substitute for the unobserved confounder, to remove the confounding effect. Note that the proposed deconfounder is model-agnostic and non-intrusive, and hence can be applied in any WTAL method without model re-designs. Through extensive experiments on four state-of-the-art WTAL methods, we show that the deconfounder can improve all of them on the public datasets: THUMOS-14 and ActivityNet-1.3(1).

关键词： Location awareness Training Visualization computer vision Smoothing methods Computational modeling pattern recognition

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Research on Identifying Differential Behavior in Complex Scenarios 39

Research on Identifying Differential Behavior in Complex Sce...

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39th Youth Academic Annual conference of Chinese-Association-of-Automation (YAC)

作者： Zhao, Shitao Fang, Kai Feng, Zihao China Acad Railway Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9798350390780;9798350379228

Group behavior recognition is a significant research area in computer vision and pattern recognition. However, recognizing an individual who behaves differently from the majority in complex scenes, such as malls and transport stations, is even more crucial. This paper proposes a novel approach for group behavior recognition based on deep learning and graph theory. The approach comprises two main stages: feature extraction and graph-based classification. In the feature extraction stage, visual features are extracted from each individual in the group. In the graph-based classification stage, a graph representation of the group is constructed, and graph convolutional networks (GCNs) are utilized to classify the group behavior and differentiate the anomalous behavior.

关键词： Group behavior recognition complex scenarios

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Visual Atoms: Pre-Training vision Transformers with Sinusoidal Waves

Visual Atoms: Pre-Training Vision Transformers with Sinusoid...

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conference on computer vision and pattern recognition (cvpr)

作者： Sora Takashima Ryo Hayamizu Nakamasa Inoue Hirokatsu Kataoka Rio Yokota National Institute of Advanced Industrial Science and Technology (AIST) Tokyo Institute of Technology

Formula-driven supervised learning (FDSL) has been shown to be an effective method for pre-training vision transformers, where ExFractalDB-21k was shown to exceed the pre-training effect of ImageNet-21k. These studies also indicate that contours mattered more than textures when pre-training vision transformers. However, the lack of a systematic investigation as to why these contour-oriented synthetic datasets can achieve the same accuracy as real datasets leaves much room for skepticism. In the present work, we develop a novel methodology based on circular harmonics for systematically investigating the design space of contour-oriented synthetic datasets. This allows us to efficiently search the optimal range of FDSL parameters and maximize the variety of synthetic images in the dataset, which we found to be a critical factor. When the resulting new dataset VisualAtom-21k is used for pre-training ViT-Base, the top-1 accuracy reached 83.7% when fine-tuning on ImageNet-1k. This is only 0.5% difference from the top-1 accuracy (84.2%) achieved by the JFT-300M pre-training, even though the scale of images is 1/14. Unlike JFT-300M which is a static dataset, the quality of synthetic datasets will continue to improve, and the current work is a testament to this possibility. FDSL is also free of the common issues associated with real images, e.g. privacy/copyright issues, labeling costs/errors, and ethical biases.

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