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检索条件"任意字段=2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021"

共 11423 条记录，以下是91-100 订阅

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Graph Stacked Hourglass Networks for 3D Human Pose Estimation

Graph Stacked Hourglass Networks for 3D Human Pose Estimatio...

引用

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

作者： Xu, Tianhan Takano, Wataru Osaka Univ Osaka Japan

ISBN: (纸本)9781665445092

In this paper, we propose a novel graph convolutional network architecture, Graph Stacked Hourglass Networks, for 2D-to-3D human pose estimation tasks. The proposed architecture consists of repeated encoder-decoder, in which graph-structured features are processed across three different scales of human skeletal representations. This multi-scale architecture enables the model to learn both local and global feature representations, which are critical for 3D human pose estimation. We also introduce a multi-level feature learning approach using different-depth intermediate features and show the performance improvements that result from exploiting multi-scale, multi-level feature representations. Extensive experiments are conducted to validate our approach, and the results show that our model outperforms the state-of-the-art.

关键词： Solid modeling computer vision Three-dimensional displays Pose estimation computer architecture Network architecture Feature extraction

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Ego-Exo: Transferring Visual Representations from Third-person to First-person Videos

Ego-Exo: Transferring Visual Representations from Third-pers...

引用

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

作者： Li, Yanghao Nagarajan, Tushar Xiong, Bo Grauman, Kristen Facebook AI Res Mountain View CA 94127 USA UT Austin Austin TX USA

ISBN: (纸本)9781665445092

We introduce an approach for pre-training egocentric video models using large-scale third-person video datasets. Learning from purely egocentric data is limited by low dataset scale and diversity, while using purely exocentric (third-person) data introduces a large domain mismatch. Our idea is to discover latent signals in third-person video that are predictive of key egocentric-specific properties. Incorporating these signals as knowledge distillation losses during pre-training results in models that benefit from both the scale and diversity of third-person video data, as well as representations that capture salient egocentric properties. Our experiments show that our "Ego-Exo" framework can be seamlessly integrated into standard video models;it outperforms all baselines when fine-tuned for egocentric activity recognition, achieving state-of-the-art results on Charades-Ego and EPIC-Kitchens-100.

关键词： Training Visualization computer vision Computational modeling Pipelines Activity recognition Data models

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On Disentanglement and Mutual Information in Semi-Supervised Variational Auto-Encoders

On Disentanglement and Mutual Information in Semi-Supervised...

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

作者： Gordon Rodriguez, Elliott Columbia Univ Dept Stat New York NY 10027 USA

ISBN: (纸本)9781665448994

In the context of variational auto-encoders, learning disentangled latent variable representations remains a challenging problem. In this abstract, we consider the semi-supervised setting, in which the factors of variation are labelled for a small fraction of our samples. We examine how the quality of learned representations is affected by the dimension of the unsupervised component of the latent space. We also consider a variational lower bound for the mutual information between the data and the semi-supervised component of the latent space, and analyze its role in the context of disentangled representation learning.

关键词： computer vision conferences pattern recognition Mutual information

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Fast Sinkhorn Filters: Using Matrix Scaling for Non-Rigid Shape Correspondence with Functional Maps

Fast Sinkhorn Filters: Using Matrix Scaling for Non-Rigid Sh...

引用

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

作者： Pai, Gautam Ren, Jing Melzi, Simone Wonka, Peter Ovsjanikov, Maks IP Paris Ecole Polytech LIX Palaiseau France KAUST Thuwal Saudi Arabia Sapienza Univ Rome Rome Italy

ISBN: (纸本)9781665445092

In this paper, we provide a theoretical foundation for pointwise map recovery from functional maps and highlight its relation to a range of shape correspondence methods based on spectral alignment. With this analysis in hand, we develop a novel spectral registration technique: Fast Sinkhorn Filters, which allows for the recovery of accurate and bijective pointwise correspondences with a superior time and memory complexity in comparison to existing approaches. Our method combines the simple and concise representation of correspondence using functional maps with the matrix scaling schemes from computational optimal transport. By exploiting the sparse structure of the kernel matrices involved in the transport map computation, we provide an efficient trade-off between acceptable accuracy and complexity for the problem of dense shape correspondence, while promoting bijectivity.

关键词： Visualization computer vision Shape Filtering algorithms Iterative algorithms Complexity theory pattern recognition

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Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes

Neural Scene Flow Fields for Space-Time View Synthesis of Dy...

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

作者： Li, Zhengqi Niklaus, Simon Snavely, Noah Wang, Oliver Cornell Tech New York NY 10044 USA Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

We present a method to perform novel view and time synthesis of dynamic scenes, requiring only a monocular video with known camera poses as input. To do this, we introduce Neural Scene Flow Fields, a new representation that models the dynamic scene as a time-variant continuous function of appearance, geometry, and 3D scene motion. Our representation is optimized through a neural network to fit the observed input views. We show that our representation can be used for varieties of in-the-wild scenes, including thin structures, view-dependent effects, and complex degrees of motion. We conduct a number of experiments that demonstrate our approach significantly outperforms recent monocular view synthesis methods, and show qualitative results of space-time view synthesis on a variety of real-world videos.

关键词： Geometry Solid modeling computer vision Three-dimensional displays Dynamics Neural networks Cameras

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A Generalized Loss Function for Crowd Counting and Localization

A Generalized Loss Function for Crowd Counting and Localizat...

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

作者： Wan, Jia Liu, Ziquan Chan, Antoni B. City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Previous work [40] shows that a better density map representation can improve the performance of crowd counting. In this paper, we investigate learning the density map representation through an unbalanced optimal transport problem, and propose a generalized loss function to learn density maps for crowd counting and localization. We prove that pixel-wise L2 loss and Bayesian loss [29] are special cases and suboptimal solutions to our proposed loss function. A perspective-guided transport cost function is further proposed to better handle the perspective transformation in crowd images. Since the predicted density will be pushed toward annotation positions, the density map prediction will be sparse and can naturally be used for localization. Finally, the proposed loss outperforms other losses on four large-scale datasets for counting, and achieves the best localization performance on NWPU-Crowd and UCF-QNRF.

关键词： Location awareness computer vision Annotations computer architecture Cost function Bayes methods pattern recognition

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VDSM: Unsupervised Video Disentanglement with State-Space Modeling and Deep Mixtures of Experts

VDSM: Unsupervised Video Disentanglement with State-Space Mo...

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

作者： Vowels, Matthew J. Camgoz, Necati Cihan Bowden, Richard Univ Surrey Ctr Vis Speech & Signal Proc Guildford Surrey England

ISBN: (纸本)9781665445092

Disentangled representations support a range of downstream tasks including causal reasoning, generative modeling, and fair machine learning. Unfortunately, disentanglement has been shown to be impossible without the incorporation of supervision or inductive bias. Given that supervision is often expensive or infeasible to acquire, we choose to incorporate structural inductive bias and present an unsupervised, deep State-Space-Model for Video Disentanglement (VDSM). The model disentangles latent time-varying and dynamic factors via the incorporation of hierarchical structure with a dynamic prior and a Mixture of Experts decoder. VDSM learns separate disentangled representations for the identity of the object or person in the video, and for the action being performed. We evaluate VDSM across a range of qualitative and quantitative tasks including identity and dynamics transfer;sequence generation, Frechet Inception Distance, and factor classification. VDSM achieves state-of-the-art performance and exceeds adversarial methods, even when the methods use additional supervision.

关键词： computer vision Computational modeling Machine learning Cognition pattern recognition Decoding Task analysis

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Fair Feature Distillation for Visual recognition

Fair Feature Distillation for Visual Recognition

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

作者： Jung, Sangwon Lee, Donggyu Park, Taeeon Moon, Taesup Sungkyunkwan Univ Dept Elect & Comp Engn Suwon South Korea Seoul Natl Univ Dept Elect & Comp Engn Seoul South Korea

ISBN: (纸本)9781665445092

Fairness is becoming an increasingly crucial issue for computer vision, especially in the human-related decision systems. However, achieving algorithmic fairness, which makes a model produce indiscriminative outcomes against protected groups, is still an unresolved problem. In this paper, we devise a systematic approach which reduces algorithmic biases via feature distillation for visual recognition tasks, dubbed as MMD-based Fair Distillation (MFD). While the distillation technique has been widely used in general to improve the prediction accuracy, to the best of our knowledge, there has been no explicit work that also tries to improve fairness via distillation. Furthermore, We give a theoretical justification of our MFD on the effect of knowledge distillation and fairness. Throughout the extensive experiments, we show our MFD significantly mitigates the bias against specific minorities without any loss of the accuracy on both synthetic and real-world face datasets.

关键词： Visualization computer vision Systematics Computational modeling Face recognition Predictive models Prediction algorithms

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Visual Room Rearrangement

Visual Room Rearrangement

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

作者： Weihs, Luca Deitke, Matt Kembhavi, Aniruddha Mottaghi, Roozbeh Prior Allen Inst AI Seattle WA 98103 USA Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781665445092

There has been a significant recent progress in the field of Embodied AI with researchers developing models and algorithms enabling embodied agents to navigate and interact within completely unseen environments. In this paper, we propose a new dataset and baseline models for the task of Rearrangement. We particularly focus on the task of Room Rearrangement: an agent begins by exploring a room and recording objects' initial configurations. We then remove the agent and change the poses and states (e.g., open/closed) of some objects in the room. The agent must restore the initial configurations of all objects in the room. Our dataset, named RoomR, includes 6,000 distinct rearrangement settings involving 72 different object types in 120 scenes. Our experiments show that solving this challenging interactive task that involves navigation and object interaction is beyond the capabilities of the current state-of-the-art techniques for embodied tasks and we are still very far from achieving perfect performance on these types of tasks.

关键词： Visualization computer vision Navigation pattern recognition Task analysis Artificial intelligence

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Cross Modal Focal Loss for RGBD Face Anti-Spoofing

Cross Modal Focal Loss for RGBD Face Anti-Spoofing

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

作者： George, Anjith Marcel, Sebastien Idiap Res Inst Rue Marconi 19 CH-1920 Martigny Switzerland

ISBN: (纸本)9781665445092

Automatic methods for detecting presentation attacks are essential to ensure the reliable use of facial recognition technology. Most of the methods available in the literature for presentation attack detection (PAD) fails in generalizing to unseen attacks. In recent years, multi-channel methods have been proposed to improve the robustness of PAD systems. Often, only a limited amount of data is available for additional channels, which limits the effectiveness of these methods. In this work, we present a new framework for PAD that uses RGB and depth channels together with a novel loss function. The new architecture uses complementary information from the two modalities while reducing the impact of ovetfitting. Essentially, a cross-modal focal loss function is proposed to modulate the loss contribution of each channel as a function of the confidence of individual channels. Extensive evaluations in two publicly available datasets demonstrate the effectiveness of the proposed approach.

关键词： computer vision Face recognition Computational modeling computer architecture Robustness

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