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Video Interaction recognition using an Attention Augmented Relational Network and Skeleton Data

Video Interaction Recognition using an Attention Augmented R...

引用

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

作者： Askari, Farzaneh Yared, Cyril Ramaprasad, Rohit Garg, Devin Hu, Anjun Clark, James J. McGill Univ Montreal PQ Canada Univ Calif San Diego San Diego CA USA Univ Oxford Oxford England

ISBN: (纸本)9798350365474

Recognizing interactions in multi-person videos, known as Video Interaction recognition (VIR), is crucial for understanding video content. Often the human skeleton pose (skeleton, for short) is a popular feature for VIR as the main feature, given its success for the task in hand. While many studies have made progress using complex architectures like Graph Neural Networks (GNN) and Transformers to capture interactions in videos, studies such as [33] that apply simple, easy to train, and adaptive architectures such as Relation reasoning Network (RN) [37], yield competitive results. Inspired by this trend, we propose the Attention Augmented Relational Network (AARN), a straightforward yet effective model that uses skeleton data to recognize interactions in videos. AARN outperforms other RN-based models and remains competitive against larger, more intricate models. We evaluate our approach on a challenging real-world Hockey Penalty Dataset (HPD), where the videos depict complex interactions between players in a non-laboratory recording setup, in addition to popular benchmark datasets demonstrating strong performance. Lastly, we show the impact of skeleton quality on the classification accuracy and the struggle of off-the-shelf pose estimators to extract precise skeleton from the challenging HPD dataset.

关键词： Musculoskeletal system

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Teaching Matters: Investigating the Role of Supervision in vision Transformers

Teaching Matters: Investigating the Role of Supervision in V...

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

作者： Walmer, Matthew Suri, Saksham Gupta, Kamal Shrivastava, Abhinav Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9798350301298

vision Transformers (ViTs) have gained significant popularity in recent years and have proliferated into many applications. However, their behavior under different learning paradigms is not well explored. We compare ViTs trained through different methods of supervision, and show that they learn a diverse range of behaviors in terms of their attention, representations, and downstream performance. We also discover ViT behaviors that are consistent across supervision, including the emergence of Offset Local Attention Heads. These are self-attention heads that attend to a token adjacent to the current token with a fixed directional offset, a phenomenon that to the best of our knowledge has not been highlighted in any prior work. Our analysis shows that ViTs are highly flexible and learn to process local and global information in different orders depending on their training method. We find that contrastive self-supervised methods learn features that are competitive with explicitly supervised features, and they can even be superior for part-level tasks. We also find that the representations of reconstruction-based models show non-trivial similarity to contrastive self-supervised models.

关键词： Explainable computer vision

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Practical Network Acceleration with Tiny Sets

Practical Network Acceleration with Tiny Sets

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

作者： Wang, Guo-Hua Wu, Jianxin Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9798350301298

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://***/DoctorKey/Practise.

关键词： Efficient and scalable vision

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FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography

FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model...

引用

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

作者： Yang, Julia Barnett, Alina Jade Donnelly, Jon Kishore, Satvik Fang, Jerry Schwartz, Fides Regina Chen, Chaofan Lo, Joseph Y. Rudin, Cynthia Duke Univ Durham NC 27708 USA Brigham & Womens Hosp 75 Francis St Boston MA 02115 USA Univ Maine Orono ME USA

ISBN: (纸本)9798350365474

Digital mammography is essential to breast cancer detection, and deep learning offers promising tools for faster and more accurate mammogram analysis. In radiology and other high-stakes environments, uninterpretable ("black box") deep learning models are unsuitable and there is a call in these fields to make interpretable models. Recent work in interpretable computer vision provides transparency to these formerly black boxes by utilizing prototypes for case-based explanations, achieving high accuracy in applications including mammography. However, these models struggle with precise feature localization, reasoning on large portions of an image when only a small part is relevant. This paper addresses this gap by proposing a novel multi-scale interpretable deep learning model for mammographic mass margin classification. Our contribution not only offers an interpretable model with reasoning aligned with radiologist practices, but also provides a general architecture for computer vision with user-configurable prototypes from coarse-to fine-grained prototypes.

关键词： breast cancer cancer computer vision deep learning interpretability interpretable machine learning machine learning mammography medical imaging neural networks

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1% VS 100%: Parameter-Efficient Low Rank Adapter for Dense Predictions

1% VS 100%: Parameter-Efficient Low Rank Adapter for Dense P...

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

作者： Yin, Dongshuo Yang, Yiran Wang, Zhechao Yu, Hongfeng Wei, Kaiwen Sun, Xian Chinese Acad Sci Aerosp Informat Res Inst Key Lab Network Informat Syst Technol Beijing Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing Peoples R China

ISBN: (纸本)9798350301298

Fine-tuning large-scale pre-trained vision models to downstream tasks is a standard technique for achieving state-of-the-art performance on computer vision benchmarks. However, fine-tuning the whole model with millions of parameters is inefficient as it requires storing a same-sized new model copy for each task. In this work, we propose LoRand, a method for fine-tuning large-scale vision models with a better trade-off between task performance and the number of trainable parameters. LoRand generates tiny adapter structures with low-rank synthesis while keeping the original backbone parameters fixed, resulting in high parameter sharing. To demonstrate LoRand's effectiveness, we implement extensive experiments on object detection, semantic segmentation, and instance segmentation tasks. By only training a small percentage (1% to 3%) of the pre-trained backbone parameters, LoRand achieves comparable performance to standard fine-tuning on COCO and ADE20K and outperforms fine-tuning in low-resource PASCAL VOC dataset.

关键词： Efficient and scalable vision

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Towards Professional Level Crowd Annotation of Expert Domain Data

Towards Professional Level Crowd Annotation of Expert Domain...

引用

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

作者： Wang, Pei Vasconcelos, Nuno Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9798350301298

Image recognition on expert domains is usually fine-grained and requires expert labeling, which is costly. This limits dataset sizes and the accuracy of learning systems. To address this challenge, we consider annotating expert data with crowdsourcing. This is denoted as PrOfeSsional lEvel cRowd (POSER) annotation. A new approach, based on semi-supervised learning (SSL) and denoted as SSL with human filtering (SSL-HF) is proposed. It is a human-in-the-loop SSL method, where crowd-source workers act as filters of pseudo-labels, replacing the unreliable confidence thresholding used by state-of-the-art SSL methods. To enable annotation by non-experts, classes are specified implicitly, via positive and negative sets of examples and augmented with deliberative explanations, which highlight regions of class ambiguity. In this way, SSL-HF leverages the strong low-shot learning and confidence estimation ability of humans to create an intuitive but effective labeling experience. Experiments show that SSL-HF significantly outperforms various alternative approaches in several benchmarks.

关键词： Efficient and scalable vision

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Confidence-Aware RGB-D Face recognition via Virtual Depth Synthesis

Confidence-Aware RGB-D Face Recognition via Virtual Depth Sy...

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

作者： Chen, Zijian Wang, Mei Deng, Weihong Shi, Hongzhi Wen, Dongchao Zhang, Yingjie Cui, Xingchen Zhao, Jian Inspur Beijing Elect Informat Ind Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350365474

2D face recognition encounters challenges in unconstrained environments due to varying illumination, occlusion, and pose. Recent studies focus on RGB-D face recognition to improve robustness by incorporating depth information. However, collecting sufficient paired RGB-D training data is expensive and time-consuming, hindering wide deployment. In this work, we first construct a diverse depth dataset generated by 3D Morphable Models for depth model pre-training. Then, we propose a domain-independent pre-training framework that utilizes readily available pre-trained RGB and depth models to separately perform face recognition without needing additional paired data for retraining. To seamlessly integrate the two distinct networks and harness the complementary benefits of RGB and depth information for improved accuracy, we propose an innovative Adaptive Confidence Weighting (ACW). This mechanism is designed to learn confidence estimates for each modality to achieve modality fusion at the score level. Our method is simple and lightweight, only requiring ACW training beyond the backbone models. Experiments on multiple public RGB-D face recognition benchmarks demonstrate state-of-the-art performance surpassing previous methods based on depth estimation and feature fusion, validating the efficacy of our approach.

关键词： domain-independent pre-training modality fusion RGB-D face recognition

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LightPainter: Interactive Portrait Relighting with Freehand Scribble

LightPainter: Interactive Portrait Relighting with Freehand ...

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

作者： Mei, Yiqun Zhang, He Zhang, Xuaner Zhang, Jianming Shu, Zhixin Wang, Yilin Wei, Zijun Yan, Shi Jung, HyunJoon Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21218 USA Adobe Inc San Jose CA USA

ISBN: (纸本)9798350301298

Recent portrait relighting methods have achieved realistic results of portrait lighting effects given a desired lighting representation such as an environment map. However, these methods are not intuitive for user interaction and lack precise lighting control. We introduce LightPainter, a scribble-based relighting system that allows users to interactively manipulate portrait lighting effect with ease. This is achieved by two conditional neural networks, a delighting module that recovers geometry and albedo optionally conditioned on skin tone, and a scribble-based module for relighting. To train the relighting module, we propose a novel scribble simulation procedure to mimic real user scribbles, which allows our pipeline to be trained without any human annotations. We demonstrate high-quality and flexible portrait lighting editing capability with both quantitative and qualitative experiments. User study comparisons with commercial lighting editing tools also demonstrate consistent user preference for our method.

关键词： vision + graphics

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MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis

MoFusion: A Framework for Denoising-Diffusion-based Motion S...

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

作者： Dabral, Rishabh Mughal, Muhammad Hamza Golyanik, Vladislav Theobalt, Christian Max Planck Inst Informat SIC Saarbrucken Germany Saarland Univ Saarbrucken Germany

ISBN: (纸本)9798350301298

Conventional methods for human motion synthesis have either been deterministic or have had to struggle with the trade-off between motion diversity vs motion quality. In response to these limitations, we introduce MoFusion, i.e., a new denoising-diffusion-based framework for high-quality conditional human motion synthesis that can synthesise long, temporally plausible, and semantically accurate motions based on a range of conditioning contexts (such as music and text). We also present ways to introduce well-known kinematic losses for motion plausibility within the motion-diffusion framework through our scheduled weighting strategy. The learned latent space can be used for several interactive motion-editing applications like in-betweening, seed-conditioning, and text-based editing, thus, providing crucial abilities for virtual-character animation and robotics. Through comprehensive quantitative evaluations and a perceptual user study, we demonstrate the effectiveness of MoFusion compared to the state of the art on established benchmarks in the literature. We urge the reader to watch our supplementary video at https://***/projects/MoFusion/.

关键词： language reasoning vision

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CUE-Net: Violence Detection Video Analytics with Spatial Cropping, Enhanced UniformerV2 and Modified Efficient Additive Attention

CUE-Net: Violence Detection Video Analytics with Spatial Cro...

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

作者： Senadeera, Damith Chamalke Yang, Xiaoyun Kollias, Dimitrios Slabaugh, Gregory Queen Mary Univ London Sch Elect Engn & Comp Sci London England Queen Marys Digital Environm Res Inst DERI London England Remark AI UK Ltd London England

ISBN: (纸本)9798350365474

In this paper we introduce CUE-Net, a novel architecture designed for automated violence detection in video surveillance. As surveillance systems become more prevalent due to technological advances and decreasing costs, the challenge of efficiently monitoring vast amounts of video data has intensified. CUE-Net addresses this challenge by combining spatial Cropping with an enhanced version of the UniformerV2 architecture, integrating convolutional and self-attention mechanisms alongside a novel Modified Efficient Additive Attention mechanism (which reduces the quadratic time complexity of self-attention) to effectively and efficiently identify violent activities. This approach aims to overcome traditional challenges such as capturing distant or partially obscured subjects within video frames. By focusing on both local and global spatio-temporal features, CUE-Net achieves state-of-the-art performance on the RWF-2000 and RLVS datasets, surpassing existing methods. The source code is available at (1).

关键词： computer vision Cropping Deep Learning Efficient Additive Attention UniFormerV2 Video Analytics Violence Detection

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