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Recognizing Actions in Videos from Unseen Viewpoints

Recognizing Actions in Videos from Unseen Viewpoints

引用

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

作者： Piergiovanni, A. J. Ryoo, Michael S. Indiana Univ Bloomington IN 47405 USA SUNY Stony Brook Stony Brook NY 11794 USA

ISBN: (纸本)9781665445092

Standard methods for video recognition use large CNNs designed to capture spatio-temporal data. However;training these models requires a large amount of labeled training data, containing a wide variety of actions, scenes, settings and camera viewpoints. In this paper, we show that current convolutional neural network models are unable to recognize actions from camera viewpoints not present in their training data (i.e., unseen view action recognition). To address this, we develop approaches based on 3D representations and introduce a new geometric convolutional layer that can learn viewpoint invariant representations. Further, we introduce a new, challenging dataset for unseen view recognition and show the approaches ability to learn viewpoint invariant representations.

关键词： Training computer vision Three-dimensional displays Training data Cameras Data models pattern recognition

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ANIM: Accurate Neural Implicit Model for Human Reconstruction from a single RGB-D image

ANIM: Accurate Neural Implicit Model for Human Reconstructio...

引用

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

作者： Pesavento, Marco Xu, Yuanlu Sarafianos, Nikolaos Maier, Robert Wang, Ziyan Yao, Chun-Han Volino, Marco Boyer, Edmond Hilton, Adrian Tung, Tony Univ Surrey CVSSP Guildford Surrey England UC Merced Merced CA USA Meta Real Labs Sausalito CA USA

ISBN: (纸本)9798350353013;9798350353006

Recent progress in human shape learning, shows that neural implicit models are effective in generating 3D human surfaces from limited number of views, and even from a single RGB image. However, existing monocular approaches still struggle to recover fine geometric details such as face, hands or cloth wrinkles. They are also easily prone to depth ambiguities that result in distorted geometries along the camera optical axis. In this paper, we explore the benefits of incorporating depth observations in the reconstruction process by introducing ANIM, a novel method that reconstructs arbitrary 3D human shapes from single-view RGB-D images with an unprecedented level of accuracy. Our model learns geometric details from both multi-resolution pixel-aligned and voxel-aligned features to leverage depth information and enable spatial relationships, mitigating depth ambiguities. We further enhance the quality of the reconstructed shape by introducing a depth-supervision strategy, which improves the accuracy of the signed distance field estimation of points that lie on the re-constructed surface. Experiments demonstrate that ANIM outperforms state-of-the-art works that use RGB, surface normals, point cloud or RGB-D data as input. In addi-tion, we introduce ANIM-Real, a new multi-modal dataset comprising high-quality scans paired with consumer-grade RGB-D camera, and our protocol to fine-tune ANIM, enabling high-quality reconstruction from real-world human capture. https://***/ANIM/

关键词： 3D Digital Avatars 3D Human reconstruction computer vision Neural Implicit Model

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Logarithmic Lenses: Exploring Log RGB Data for Image Classification

Logarithmic Lenses: Exploring Log RGB Data for Image Classif...

引用

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

作者： Maxwell, Bruce A. Singhania, Sumegha Patel, Avnish Kumar, Rahul Fryling, Heather Li, Sihan Sun, Haonan He, Ping Li, Zewen Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9798350353006

The design of deep network architectures and training methods in computer vision has been well-explored. However, in almost all cases the images have been used as provided, with little exploration of pre-processing steps beyond normalization and data augmentation. Virtually all images posted on the web or captured by devices are processed for viewing by humans. Is the pipeline used for humans also best for use by computers and deep networks? The human visual system uses logarithmic sensors;differences and sums correspond to ratios and products. Fea-tures in log space will be invariant to intensity changes and robust to color balance changes. Log RGB space also reveals structure that is corrupted by typical pre-processing. We explore using linear and log RGB data for training standard backbone architectures on an image classification task using data derived directly from RAW images to guarantee its integrity. We found that networks trained on log RGB data exhibit improved performance on an unmodified test set and invariance to intensity and color balance modifications without additional training or data augmentation. Furthermore, we found that the gains from using high quality log data could also be partially or fully realized from data in 8-bit sRGB-JPG format by inverting the sRGB transform and taking the log. These results imply existing databases may benefit from this type of pre-processing. While working with log data, we found it was critical to retain the integrity of the log relationships and that networks using log data train best with meta-parameters different than those used for sRGB or linear data. Finally, we introduce a new 10-category 10k RAW image data set (RAW10) for image classification and other purposes to enable further the exploration of log RGB as an input format for deep networks in computer vision.

关键词： computer vision data set image classification physics-based vision

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SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object Detection

SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object D...

引用

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

作者： Liu, Mingxuan Hayes, Tyler L. Ricci, Elisa Csurka, Gabriela Volpi, Riccardo Univ Trento Trento Italy NAVER LABS Europe Meylan France Fdn Bruno Kessler Trento Italy

ISBN: (纸本)9798350353006

Open-vocabulary object detection (OvOD) has transformed detection into a language-guided task, empowering users to freely define their class vocabularies of interest during inference. However, our initial investigation indicates that existing OvOD detectors exhibit significant variability when dealing with vocabularies across various semantic granularities, posing a concern for real-world deployment. To this end, we introduce Semantic Hierarchy Nexus (SHiNe), a novel classifier that uses semantic knowledge from class hierarchies. It runs offline in three steps: i) it retrieves relevant super-/sub-categories from a hierarchy for each target class;ii) it integrates these categories into hierarchy-aware sentences;iii) it fuses these sentence embeddings to generate the nexus classifier vector. Our evaluation on various detection benchmarks demonstrates that SHiNe enhances robustness across diverse vocabulary granularities, achieving up to +31.9% mAP50 with ground truth hierarchies, while retaining improvements using hierarchies generated by large language models. Moreover, when applied to open-vocabulary classification on ImageNet-1k, SHiNe improves the CLIP zero-shot baseline by +2.8% accuracy. SHiNe is training-free and can be seamlessly integrated with any off-the-shelf OvOD detector, without incurring additional computational overhead during inference. The code is open source.

关键词： Object Detection Open-vocabulary vision-Language

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OmniSeg3D: Omniversal 3D Segmentation via Hierarchical Contrastive Learning

OmniSeg3D: Omniversal 3D Segmentation via Hierarchical Contr...

引用

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

作者： Ying, Haiyang Yin, Yixuan Zhang, Jinzhi Wang, Fan Yu, Tao Huang, Ruqi Fang, Lu Tsinghua Univ Beijing Peoples R China Alibaba Grp Beijing Peoples R China

ISBN: (纸本)9798350353006

Towards holistic understanding of 3D scenes, a general 3D segmentation method is needed that can segment diverse objects without restrictions on object quantity or categories, while also reflecting the inherent hierarchical structure. To achieve this, we propose OmniSeg3D, an omniversal segmentation method aims for segmenting anything in 3D all at once. The key insight is to lift multi-view inconsistent 2D segmentations into a consistent 3D feature field through a hierarchical contrastive learning framework, which is accomplished by two steps. Firstly, we design a novel hierarchical representation based on category-agnostic 2D segmentations to model the multi-level relationship among pixels. Secondly, image features rendered from the 3D feature field are clustered at different levels, which can be further drawn closer or pushed apart according to the hierarchical relationship between different levels. In tackling the challenges posed by inconsistent 2D segmentations, this framework yields a global consistent 3D feature field, which further enables hierarchical segmentation, multi-object selection, and global discretization. Extensive experiments demonstrate the effectiveness of our method on high-quality 3D segmentation and accurate hierarchical structure understanding. A graphical user interface further facilitates flexible interaction for omniversal 3D segmentation.

关键词： 3D Segmentation 3D vision Contrastive Learning Differentiable Rendering Hierarchical Understanding Neural Radiance Field

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Towards Efficient Audio-Visual Learners via Empowering Pre-trained vision Transformers with Cross-Modal Adaptation

Towards Efficient Audio-Visual Learners via Empowering Pre-t...

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

作者： Wang, Kai Tian, Yapeng Hatzinakos, Dimitrios Univ Toronto Toronto ON Canada Univ Texas Dallas Richardson TX 75083 USA

ISBN: (纸本)9798350365474

In this paper, we explore the cross-modal adaptation of pre-trained vision Transformers (ViTs) for the audio-visual domain by incorporating a limited set of trainable parameters. To this end, we propose a Spatial-Temporal-Global Cross-Modal Adaptation (STG-CMA) to gradually equip the frozen ViTs with the capability for learning audio-visual representation, consisting of the modality-specific temporal adaptation for temporal reasoning of each modality, the cross-modal spatial adaptation for refining the spatial information with the cue from counterpart modality, and the cross-modal global adaptation for global interaction between audio and visual modalities. Our STG-CMA presents a meaningful finding that only leveraging the shared pre-trained image model with inserted lightweight adapters is enough for spatial-temporal modeling and feature interaction of audio-visual modality. Extensive experiments indicate that our STG-CMA achieves state-of-the-art performance on various audio-visual understanding tasks including AVE, AVS, and AVQA while containing significantly reduced tunable parameters. The code is available at https://***/kaiw7/STG-CMA.

关键词： Audio-visual Lenarning Cross-modal Adaptation Pre-trained vision Transformers Reduced Tunanle Parameters Spatial-temporal-global Modeling

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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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IDGI: A Framework to Eliminate Explanation Noise from Integrated Gradients

IDGI: A Framework to Eliminate Explanation Noise from Integr...

引用

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

作者： Yang, Ruo Wang, Binghui Bilgic, Mustafa IIT Dept Comp Sci Chicago IL 60616 USA

ISBN: (纸本)9798350301298

Integrated Gradients (IG) as well as its variants are well-known techniques for interpreting the decisions of deep neural networks. While IG-based approaches attain state-of-the-art performance, they often integrate noise into their explanation saliency maps, which reduce their interpretability. To minimize the noise, we examine the source of the noise analytically and propose a new approach to reduce the explanation noise based on our analytical findings. We propose the Important Direction Gradient Integration (IDGI) framework, which can be easily incorporated into any IG-based method that uses the Reimann Integration for integrated gradient computation. Extensive experiments with three IG-based methods show that IDGI improves them drastically on numerous interpretability metrics. The source code for IDGI is available at https://***/yangruo1226/IDGI.

关键词： Explainable computer vision

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Learning Semantic-Aware Dynamics for Video Prediction

Learning Semantic-Aware Dynamics for Video Prediction

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

作者： Bei, Xinzhu Yang, Yanchao Soatto, Stefano Univ Calif Los Angeles Vis Lab Los Angeles CA 90024 USA Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781665445092

We propose an architecture and training scheme to predict video frames by explicitly modeling dis-occlusions and capturing the evolution of semantically consistent regions in the video. The scene layout (semantic map) and motion (optical flow) are decomposed into layers, which are predicted and fused with their context to generate future layouts and motions. The appearance of the scene is warped from past frames using the predicted motion in co-visible regions;dis-occluded regions are synthesized with content-aware inpainting utilizing the predicted scene layout. The result is a predictive model that explicitly represents objects and learns their class-specific motion, which we evaluate on video prediction benchmarks.

关键词： Training computer vision Layout Semantics Dynamics computer architecture Predictive models

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HHMR: Holistic Hand Mesh Recovery by Enhancing the Multimodal Controllability of Graph Diffusion Models

HHMR: Holistic Hand Mesh Recovery by Enhancing the Multimoda...

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

作者： Li, Mengcheng Zhang, Hongwen Zhang, Yuxiang Shao, Ruizhi Yu, Tao Liu, Yebin Tsinghua Univ Dept Automat Beijing Peoples R China Beijing Normal Univ Sch Artificial Intelligence Beijing Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Recent years have witnessed a trend of the deep integration of the generation and reconstruction paradigms. In this paper, we extend the ability of controllable generative models for a more comprehensive hand mesh recovery task: direct hand mesh generation, inpainting, reconstruction, and fitting in a single framework, which we name as Holistic Hand Mesh Recovery (HHMR). Our key observation is that different kinds of hand mesh recovery tasks can be achieved by a single generative model with strong multimodal controllability, and in such a framework, realizing different tasks only requires giving different signals as conditions. To achieve this goal, we propose an all-in-one diffusion framework based on graph convolution and attention mechanisms for holistic hand mesh recovery. In order to achieve strong control generation capability while ensuring the decoupling of multimodal control signals, we map different modalities to a shared feature space and apply cross-scale random masking in both modality and feature levels. In this way, the correlation between different modalities can be fully exploited during the learning of hand priors. Furthermore, we propose Condition-aligned Gradient Guidance to enhance the alignment of the generated model with the control signals, which significantly improves the accuracy of the hand mesh reconstruction and fitting. Experiments show that our novel framework can realize multiple hand mesh recovery tasks simultaneously and outperform the existing methods in different tasks, which provides more possibilities for subsequent downstream applications including gesture recognition, pose generation, mesh editing, and so on.

关键词： Gesture recognition

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