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ST-Gait++: Leveraging spatio-temporal convolutions for gait-based emotion recognition on videos

ST-Gait++: Leveraging spatio-temporal convolutions for gait-...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Maria Luísa Lima Willams de Lima Costa Estefania Talavera Martínez Veronica Teichrieb Voxar Labs Centro de Informática Universidade Federal de Pernambuco Recife Brazil University of Twente Enschede Netherlands

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Emotion recognition is relevant for human behaviour understanding, where facial expression and speech recognition have been widely explored by the computer vision community. Literature in the field of behavioural psychology indicates that gait, described as the way a person walks, is an additional indicator of emotions. In this work, we propose a deep framework for emotion recognition through the analysis of gait. More specifically, our model is composed of a sequence of spatial-temporal Graph Convolutional Networks that produce a robust skeleton-based representation for the task of emotion classification. We evaluate our proposed framework on the E-Gait dataset, composed of a total of 2177 samples. The results obtained represent an improvement of ≈ 5% in accuracy compared to the state of the art. In addition, during training we observed a faster convergence of our model compared to the state-of-the-art methodologies.

关键词： Training Emotion recognition computer vision Portable computers Graph convolutional networks conferences Psychology

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NTIRE 2022 Challenge on Night Photography Rendering

NTIRE 2022 Challenge on Night Photography Rendering

引用

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

作者： Ershov, Egor Savchik, Alex Shepelev, Denis Banic, Nikola Brown, Michael S. Timofte, Radu Koscevic, Karlo Freeman, Michael Tesalin, Vasily Bocharov, Dmitry Semenkov, Illya Subasic, Marko Loncaric, Sven Terekhin, Arseniy Liu, Shuai Feng, Chaoyu Wang, Hao Zhu, Ran Li, Yongqiang Lei, Lei Li, Zhihao Yi, Si Han, Ling-Hao Wu, Ruiqi Jin, Xin Guo, Chunle Kinli, Furkan Mentes, Sami Ozcan, Baris Kirac, Furkan Zini, Simone Rota, Claudio Buzzelli, Marco Bianco, Simone Schettini, Raimondo Li, Wei Ma, Yipeng Wang, Tao Xu, Ruikang Song, Fenglong Chen, Wei-Ting Yang, Hao-Hsiang Huang, Zhi-Kai Chang, Hua-En Kuo, Sy-Yen Liang, Zhexin Zhou, Shangchen Feng, Ruicheng Li, Chongyi Chen, Xiangyu Song, Binbin Zhang, Shile Liu, Lin Wang, Zhendong Ryu, Dohoon Bae, Hyokyoung Kwon, Taesung Desai, Chaitra Akalwadi, Nikhil Joshi, Amogh Mandi, Chinmayee Malagi, Sampada Uppin, Akash Reddy, Sai Sudheer Tabib, Ramesh Ashok Patil, Ujwala Mudenagudi, Uma Kharkevich Inst Inst Informat Transmiss Problems Moscow Russia Gideon Bros Osijek Croatia York Univ Toronto ON Canada Swiss Fed Inst Technol Zurich Zurich Switzerland Univ Wurzburg Wurzburg Germany Univ Zagreb Fac Elect Engn & Comp Zagreb Croatia Michael Freeman Photog London England Xiaomi Inc Beijing Peoples R China Nanjing Univ Nanjing Peoples R China Nankai Univ Tianjin Peoples R China Wuhan Univ Technol Wuhan Peoples R China Ozyegin Univ Istanbul Turkey Univ Milano Bicocca Milan Italy Huawei Noahs Ark Lab Montreal PQ Canada Natl Taiwan Univ Grad Inst Elect Engn New Taipei Taiwan Natl Taiwan Univ Dept Elect Engn New Taipei Taiwan Nanyang Technol Univ Singapore Singapore Univ Macau Taipa Macao Peoples R China Univ Sci & Technol China Hefei Peoples R China Korea Adv Inst Sci & Technol Daejeon South Korea KLE Technol Univ Ctr Excellence Visual Intelligence CEVI Hubballi Karnataka India

ISBN: (纸本)9781665487399

This paper reviews the NTIRE 2022 challenge on night photography rendering. The challenge solicited solutions that processed RAW camera images captured in night scenes to produce a photo-finished output image encoded in the standard RGB (sRGB) space. Given the subjective nature of this task, the proposed solutions were evaluated based on the mean opinions of viewers asked to judge the visual appearance of the results. Michael Freeman, a world-renowned photographer, further ranked the solutions with the highest mean opinion scores. A total of 13 teams competed in the final phase of the challenge. The proposed methods provided by the participating teams represent state-of-the-art performance in nighttime photography. Results from the various teams can be found here: https://***/

关键词： Photography Visualization computer vision conferences Rendering (computer graphics) Cameras pattern recognition

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Uncertainty Estimation for Tumor Prediction with Unlabeled Data

Uncertainty Estimation for Tumor Prediction with Unlabeled D...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Juyoung Yun Shahira Abousamra Chen Li Rajarsi Gupta Tahsin Kurc Dimitris Samaras Alison Van Dyke Joel Saltz Chao Chen Department of Computer Science Stony Brook University USA Department of Biomedical Informatics Stony Brook University USA National Cancer Institute Maryland USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Estimating uncertainty of a neural network is crucial in providing transparency and trustworthiness. In this paper, we focus on uncertainty estimation for digital pathology prediction models. To explore the large amount of unlabeled data in digital pathology, we propose to adopt novel learning method that can fully exploit unlabeled data. The proposed method achieves superior performance compared with different baselines including the celebrated Monte-Carlo Dropout. Closeup inspection of uncertain regions reveal insight into the model and improves the trustworthiness of the models.

关键词： Pathology Uncertainty Accuracy Monte Carlo methods Neural networks Estimation Predictive models

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Multi Model Ensemble for Compound Expression recognition

Multi Model Ensemble for Compound Expression Recognition

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jun Yu Jichao Zhu Wangyuan Zhu Zhongpeng Cai Gongpeng Zhao Zhihong Wei Guochen Xie Zerui Zhang Qingsong Liu Jiaen Liang University of Science and Technology of China Unisound AI Technology Co. Ltd.

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Compound Expression recognition (CER) plays a crucial role in interpersonal interactions. Due to the complexity of human emotional expressions, which leads to the existence of compound expressions, it is necessary to consider both local and global facial expressions comprehensively for recognition. In this paper, to address this issue, we propose a solution for compound expression recognition based on ensemble learning methods. Specifically, our task is classification. We trained three expression classification models based on convolutional networks (ResNet50), vision Transformers, and multi-scale local attention networks, respectively. Then, by using late fusion, integrated the outputs of three models to predict the final result, leveraging the strengths of different models. Our method achieves high accuracy on RAF-DB and in sixth Affective Behavior Analysis in-the-wild (ABAW) Challenge, achieves an F1 score of 0.224 on the test set of C-EXPR-DB.

关键词： computer vision Emotion recognition Face recognition Predictive models Transformers Feature extraction Compounds

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

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

引用

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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Raising the Bar of AI-generated Image Detection with CLIP

Raising the Bar of AI-generated Image Detection with CLIP

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Davide Cozzolino Giovanni Poggi Riccardo Corvi Matthias Nießner Luisa Verdoliva University Federico II of Naples Technical University of Munich

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The aim of this work is to explore the potential of pre-trained vision-language models (VLMs) for universal detection of AI-generated images. We develop a lightweight detection strategy based on CLIP features and study its performance in a wide variety of challenging scenarios. We find that, contrary to previous beliefs, it is neither necessary nor convenient to use a large domain-specific dataset for training. On the contrary, by using only a handful of example images from a single generative model, a CLIP-based detector exhibits surprising generalization ability and high robustness across different architectures, including recent commercial tools such as Dalle-3, Midjourney v5, and Firefly. We match the state-of-the-art (SoTA) on in-distribution data and significantly improve upon it in terms of generalization to out-of-distribution data (+6% AUC) and robustness to impaired/laundered data (+13%). Our project is available at https://***/ClipBased-SyntheticImageDetection/

关键词： Training computer vision conferences Detectors computer architecture Feature extraction Robustness

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Augmented Self-Mask Attention Transformer for Naturalistic Driving Action recognition

Augmented Self-Mask Attention Transformer for Naturalistic D...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Tiantian Zhang Qingtian Wang Xiaodong Dong Wenqing Yu Hao Sun Xuyang Zhou Aigong Zhen Shun Cui Dong Wu Zhongjiang He China Telecom Artificial Intelligence Technology (Beijing) Co. Ltd.

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Nowadays, naturalistic driving action recognition and computer vision techniques provide crucial solutions to identify and eliminate distracting driving behavior. Existing methods often extract features through fixed-size sliding windows and predict an action’s start and end time. However, the information about a fixed-size window may be incomplete or redundant and the connections between different windows are insufficient. To alleviate this problem, we propose a novel Augmented Self-Mask Attention (AMA) architecture that enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order. We employ an ensemble technique and use a weighted boundaries fusion to combine and refine predictions with high confidence scores action boundaries. On the test dataset of AI City Challenge 2024 Track3, we achieved significant results compared with other teams, the proposed model ranks first on the public leaderboard of the challenge. Codes are available at https://***/wolfworld6/AIcity2024-track3.

关键词： computer vision conferences Computational modeling Urban areas computer architecture Feature extraction Transformers

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CycleGANAS: Differentiable Neural Architecture Search for CycleGAN

CycleGANAS: Differentiable Neural Architecture Search for Cy...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Taegun An Changhee Joo Department of Computer Science and Engineering Korea University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We develop a Neural Architecture Search (NAS) framework for CycleGAN that carries out unpaired image-to-image translation task. Extending previous NAS techniques for Generative Adversarial Networks (GANs) to CycleGAN is not straightforward due to the task difference and greater search space. We design architectures that consist of a stack of simple ResNet-based cells and develop a search method that effectively explore the large search space. We show that our framework, called CycleGANAS, not only effectively discovers high-performance architectures that either match or surpass the performance of the original CycleGAN, but also successfully addresses the data imbalance by individual architecture search for each translation direction. To our best knowledge, it is the first NAS result for CycleGAN and shed light on NAS for more complex structures. Our code is at https://***/antaegun20/CycleGANAS. 1

关键词： Dimensionality reduction Search methods Stacking Neural networks computer architecture Generative adversarial networks Generators

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Benchmarking Zero-Shot recognition with vision-Language Models: Challenges on Granularity and Specificity

Benchmarking Zero-Shot Recognition with Vision-Language Mode...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Zhenlin Xu Yi Zhu Siqi Deng Abhay Mittal Yanbei Chen Manchen Wang Paolo Favaro Joseph Tighe Davide Modolo AWS AI Labs Boson AI Meta

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper presents novel benchmarks for evaluating vision-language models (VLMs) in zero-shot recognition, focusing on granularity and specificity. Although VLMs excel in tasks like image captioning, they face challenges in open-world settings. Our benchmarks test VLMs’ consistency in understanding concepts across semantic granularity levels and their response to varying text specificity. Findings show that VLMs favor moderately fine-grained concepts and struggle with specificity, often misjudging texts that differ from their training data. Extensive evaluations reveal limitations in current VLMs, particularly in distinguishing between correct and subtly incorrect descriptions. While fine-tuning offers some improvements, it doesn’t fully address these issues, highlighting the need for VLMs with enhanced generalization capabilities for real-world applications. This study provides insights into VLM limitations and suggests directions for developing more robust models.

关键词： computer vision Computational modeling Face recognition conferences Semantics Training data Focusing

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Refining Remote Photoplethysmography Architectures using CKA and Empirical Methods

Refining Remote Photoplethysmography Architectures using CKA...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Nathan Vance Patrick Flynn University of Notre Dame

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Model architecture refinement is a challenging task in deep learning research fields such as remote photoplethysmography (rPPG). One architectural consideration, the depth of the model, can have significant consequences on the resulting performance. In rPPG models that are overprovisioned with more layers than necessary, redundancies exist, the removal of which can result in faster training and reduced computational load at inference time. With too few layers the models may exhibit sub-optimal error rates. We apply Centered Kernel Alignment (CKA) to an array of rPPG architectures of differing depths, demonstrating that shallower models do not learn the same representations as deeper models, and that after a certain depth, redundant layers are added without significantly increased functionality. An empirical study confirms how the architectural deficiencies discovered using CKA impact performance, and we show how CKA as a diagnostic can be used to refine rPPG architectures.

关键词： Training Error analysis Computational modeling Refining Redundancy computer architecture Photoplethysmography

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