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检索条件"任意字段=2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"

共 21008 条记录，以下是1421-1430 订阅

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ABAW: Valence-Arousal Estimation, Expression recognition, Action Unit Detection & Multi-Task Learning Challenges

ABAW: Valence-Arousal Estimation, Expression Recognition, Ac...

引用

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

作者： Kollias, Dimitrios Queen Mary Univ London London England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper describes the third Affective Behavior Analysis in-the-wild (ABAW) Competition, held in conjunction with ieee International conference on computer vision and pattern recognition (cvpr), 2022. The 3rd ABAW Competition is a continuation of the Competitions held at ICCV 2021, ieee FG 2020 and ieee cvpr 2017 conferences, and aims at automatically analyzing affect. This year the Competition encompasses four Challenges: i) uni-task Valence-Arousal Estimation, ii) uni-task Expression Classification, iii) uni-task Action Unit Detection, and iv) MultiTask-Learning. All the Challenges are based on a common benchmark database, Aff-Wild2, which is a large scale in-the-wild database and the first one to be annotated in terms of valence-arousal, expressions and action units. In this paper, we present the four Challenges, with the utilized Competition corpora, we outline the evaluation metrics and present both the baseline systems and the top performing teams' per Challenge. Finally we illustrate the obtained results of the baseline systems and of all participating teams.

关键词： Measurement computer vision Databases conferences Estimation Benchmark testing Multitasking

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CellTypeGraph: A New Geometric computer vision Benchmark

CellTypeGraph: A New Geometric Computer Vision Benchmark

引用

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

作者： Cerrone, Lorenzo Vijayan, Athul Mody, Tejasvinee Schneitz, Kay Hamprecht, Fred A. Heidelberg Univ HCI Heidelberg Germany Tech Univ Munich Sch Life Munich Germany Max Planck Inst Plant Breeding Res Cologne Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Classifying all cells in an organ is a relevant and difficult problem from plant developmental biology. We here abstract the problem into a new benchmark for node classification in a geo-referenced graph. Solving it requires learning the spatial layout of the organ including symmetries. To allow the convenient testing of new geometrical learning methods, the benchmark of Arabidopsis thaliana ovules is made available as a PyTorch data loader, along with a large number of precomputed features. Finally, we benchmark eight recent graph neural network architectures, finding that DeeperGCN currently works best on this problem.

关键词： computer vision Three-dimensional displays Plants (biology) Supervised learning Biological systems computer architecture Benchmark testing

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Deep orientation-aware functional maps: Tackling symmetry issues in Shape Matching

Deep orientation-aware functional maps: Tackling symmetry is...

引用

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

作者： Donati, Nicolas Corman, Etienne Ovsjanikov, Maks Ecole Polytech LIX Palaiseau France CNRS INRIA Paris France

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

State-of-the-art fully intrinsic network for non-rigid shape matching are unable to disambiguate between shape inner symmetries. Meanwhile, recent advances in the functional map framework allow to enforce orientation preservation using a functional representation for tangent vector field transfer, through so-called complex functional maps. Using this representation, we propose a new deep learning approach to learn orientation-aware features in a fully unsupervised setting. Our architecture is built on DiffusionNet, which makes our method robust to discretization changes, while adding a vector-field-based loss, which promotes orientation preservation without using (often unstable) extrinsic descriptors.

关键词： Deep learning computer vision Codes Shape computer architecture pattern matching

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Understanding 3D Object Articulation in Internet Videos

Understanding 3D Object Articulation in Internet Videos

引用

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

作者： Qian, Shengyi Jin, Linyi Rockwell, Chris Chen, Siyi Fouhey, David F. Univ Michigan Ann Arbor MI 48109 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose to investigate detecting and characterizing the 3D planar articulation of objects from ordinary RGB videos. While seemingly easy for humans, this problem poses many challenges for computers. Our approach is based on a top-down detection system that finds planes that can be articulated. This approach is followed by optimizing for a 3D plane that explains a sequence of detected articulations. We show that this system can be trained on a combination of videos and 3D scan datasets. When tested on a dataset of challenging Internet videos and the Charades dataset, our approach obtains strong performance.

关键词： computers computer vision Three-dimensional displays Shape Surveillance Internet pattern recognition

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Self-Supervised Equivariant Learning for Oriented Keypoint Detection

Self-Supervised Equivariant Learning for Oriented Keypoint D...

引用

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

作者： Lee, Jongmin Kim, Byungjin Cho, Minsu Pohang Univ Sci & Technol POSTECH Pohang South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Detecting robust keypoints from an image is an integral part of many computer vision problems, and the characteristic orientation and scale of keypoints play an important role for keypoint description and matching. Existing learning-based methods for keypoint detection rely on standard translation-equivariant CNNs but often fail to detect reliable keypoints against geometric variations. To learn to detect robust oriented keypoints, we introduce a self-supervised learning framework using rotation-equivariant CNNs. We propose a dense orientation alignment loss by an image pair generated by synthetic transformations for training a histogram-based orientation map. Our method outperforms the previous methods on an image matching benchmark and a camera pose estimation benchmark.

关键词： Training computer vision Histograms Image matching Pose estimation Self-supervised learning Benchmark testing

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Input-level Inductive Biases for 3D Reconstruction

Input-level Inductive Biases for 3D Reconstruction

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

作者： Wang Yifan Doersch, Carl Arandjelovic, Relja Carreira, Joao Zisserman, Andrew Swiss Fed Inst Technol Zurich Switzerland DeepMind London England Univ Oxford Dept Engn Sci VGG Oxford England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Much of the recent progress in 3D vision has been driven by the development of specialized architectures that incorporate geometrical inductive biases. In this paper we tackle 3D reconstruction using a domain agnostic architecture and study how to inject the same type of inductive biases directly as extra inputs to the model. This approach makes it possible to apply existing general models, such as Perceivers, on this rich domain, without the need for architectural changes, while simultaneously maintaining data efficiency of bespoke models. In particular we study how to encode cameras, projective ray incidence and epipolar geometry as model inputs, and demonstrate competitive multi-view depth estimation performance on multiple benchmarks.

关键词： Geometry Solid modeling computer vision Three-dimensional displays Computational modeling Estimation computer architecture

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Use All The Labels: A Hierarchical Multi-Label Contrastive Learning Framework

Use All The Labels: A Hierarchical Multi-Label Contrastive L...

引用

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

作者： Zhang, Shu Xu, Ran Xiong, Caiming Ramaiah, Chetan Salesforce Res Washington DC 20006 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Current contrastive learning frameworks focus on leveraging a single supervisory signal to learn representations, which limits the efficacy on unseen data and downstream tasks. In this paper, we present a hierarchical multi-label representation learning framework that can leverage all available labels and preserve the hierarchical relationship between classes. We introduce novel hierarchy preserving losses, which jointly apply a hierarchical penalty to the contrastive loss, and enforce the hierarchy constraint. The loss function is data driven and automatically adapts to arbitrary multi-label structures. Experiments on several datasets show that our relationship preserving embedding performs well on a variety of tasks and outperform the baseline supervised and self-supervised approaches. Code is available at https://***/salesforce/hierarchicalContrastiveLearning.

关键词： Representation learning computer vision Codes Speech recognition Task analysis Standards

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ImplicitAtlas: Learning Deformable Shape Templates in Medical Imaging

ImplicitAtlas: Learning Deformable Shape Templates in Medica...

引用

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

作者： Yang, Jiancheng Wickramasinghe, Udaranga Ni, Bingbing Fua, Pascal Shanghai Jiao Tong Univ Shanghai Peoples R China Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Deep implicit shape models have become popular in the computer vision community at large but less so for biomedical applications. This is in part because large training databases do not exist and in part because biomedical annotations are often noisy. In this paper;we show that by introducing templates within the deep learning pipeline we can overcome these problems. The proposed framework, named ImplicitAtlas, represents a shape as a deformation field from a learned template field, where multiple templates could be integrated to improve the shape representation capacity at negligible computational cost. Extensive experiments on three medical shape datasets prove the superiority over current implicit representation methods.

关键词： Training computer vision Three-dimensional displays Shape Pipelines Sensors pattern recognition

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Spatio-temporal Relation Modeling for Few-shot Action recognition

Spatio-temporal Relation Modeling for Few-shot Action Recogn...

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

作者： Thatipelli, Anirudh Narayan, Sanath Khan, Salman Anwer, Rao Muhammad Khan, Fahad Shahbaz Ghanem, Bernard Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Aalto Univ Espoo Finland Australian Natl Univ Canberra ACT Australia Linkoping Univ CVL Linkoping Sweden King Abdullah Univ Sci & Technol Thuwal Saudi Arabia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose a novel few-shot action recognition framework, STRM, which enhances class-specific feature discriminability while simultaneously learning higher-order temporal representations. The focus of our approach is a novel spatio-temporal enrichment module that aggregates spatial and temporal contexts with dedicated local patch-level and global frame-level feature enrichment sub-modules. Local patch-level enrichment captures the appearance-based characteristics of actions. On the other hand, global frame-level enrichment explicitly encodes the broad temporal context, thereby capturing the relevant object features over time. The resulting spatio-temporally enriched representations are then utilized to learn the relational matching between query and support action sub-sequences. We further introduce a query-class similarity classifier on the patch-level enriched features to enhance class-specific feature discriminability by reinforcing the feature learning at different stages in the proposed framework. Experiments are performed on four few-shot action recognition benchmarks: Kinetics, SSv2, HMDB51 and UCF101. Our extensive ablation study reveals the benefits of the proposed contributions. Furthermore, our approach sets a new state-of-the-art on all four benchmarks. On the challenging SSv2 benchmark, our approach achieves an absolute gain of 3.5% in classification accuracy, as compared to the best existing method in the literature.

关键词： Representation learning computer vision Codes Computational modeling Aggregates Benchmark testing pattern recognition

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Camera Pose Estimation using Implicit Distortion Models

Camera Pose Estimation using Implicit Distortion Models

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

作者： Pan, Linfei Pollefeys, Marc Larsson, Viktor Swiss Fed Inst Technol Zurich Switzerland Microsoft Redmond WA USA Lund Univ Lund Sweden

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Low-dimensional parametric models are the de-facto standard in computer vision for intrinsic camera calibration. These models explicitly describe the mapping between incoming viewing rays and image pixels. In this paper, we explore an alternative approach which implicitly models the lens distortion. The main idea is to replace the parametric model with a regularization term that ensures the latent distortion map varies smoothly throughout the image. The proposed model is effectively parameter-free and allows us to optimize the 6 degree-of-freedom camera pose without explicitly knowing the intrinsic calibration. We show that the method is applicable to a wide selection of cameras with varying distortion and in multiple applications, such as visual localization and structure-from-motion.

关键词： Solid modeling computer vision Visualization Computational modeling Pose estimation Cameras Distortion

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