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Diagnosing Rarity in Human-object Interaction Detection

Diagnosing Rarity in Human-object Interaction Detection

引用

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

作者： Kilickaya, Mert Smeulders, Arnold QUvA Deep Vis Lab Amsterdam Netherlands

ISBN: (纸本)9781728193601

Human-object interaction (HOI) detection is a core task in computer vision. The goal is to localize all human-object pairs and recognize their interactions. An interaction defined by a tuple leads to a long-tailed visual recognition challenge since many combinations are rarely represented. The performance of the proposed models is limited especially for the tail categories, but little has been done to understand the reason. To that end, in this paper, we propose to diagnose rarity in HOI detection. We propose a three-step strategy, namely Detection, Identification and recognition where we carefully analyse the limiting factors by studying state-of-the-art models. Our findings indicate that detection and identification steps are altered by the interaction signals like occlusion and relative location, as a result limiting the recognition accuracy.

关键词： computer vision

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GANDiffFace: Controllable Generation of Synthetic Datasets for Face recognition with Realistic Variations

GANDiffFace: Controllable Generation of Synthetic Datasets f...

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International conference on computer vision workshops (ICCV workshops)

作者： Pietro Melzi Christian Rathgeb Ruben Tolosana Ruben Vera-Rodriguez Dominik Lawatsch Florian Domin Maxim Schaubert Biometrics and Data Pattern Analytics Laboratory Universidad Autonoma de Madrid Spain Secunet Security Networks AG Essen Germany Hochschule Darmstadt Germany

Face recognition systems have significantly advanced in recent years, driven by the availability of large-scale datasets. However, several issues have recently came up, including privacy concerns that have led to the discontinuation of well-established public datasets. Synthetic datasets have emerged as a solution, even though current synthesis methods present other drawbacks such as limited intraclass variations, lack of realism, and unfair representation of demographic groups. This study introduces GAN-DiffFace, a novel framework for the generation of synthetic datasets for face recognition that combines the power of Generative Adversarial Networks (GANs) and Diffusion models to overcome the limitations of existing synthetic datasets. In GANDiffFace, we first propose the use of GANs to synthesize highly realistic identities and meet target demographic distributions. Subsequently, we fine-tune Diffusion models with the images generated with GANs, synthesizing multiple images of the same identity with a variety of accessories, poses, expressions, and contexts. We generate multiple synthetic datasets by changing GANDiffFace settings, and compare their mated and non-mated score distributions with the distributions provided by popular real-world datasets for face recognition, i.e. VGG2 and IJB-C. Our results show the feasibility of the proposed GANDiffFace, in particular the use of Diffusion models to enhance the (limited) intra-class variations provided by GANs towards the level of real-world datasets.

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As Seen on TV: Automatic Basketball Video Production using Gaussian-based Actionness and Game States recognition

As Seen on TV: Automatic Basketball Video Production using G...

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

作者： Quiroga, Julian Carrillo, Henry Maldonado, Edisson Ruiz, John Zapata, Luis M. Genius Sports Comp Vis Medellin Colombia

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Automatic video production of sports aims at producing an aesthetic broadcast of sporting events. We present a new video system able to automatically produce a smooth and pleasant broadcast of Basketball games using a single fixed 4K camera. The system automatically detects and localizes players, ball and referees, to recognize main action coordinates and game states yielding to a professional cameraman-like production of the basketball event. We also release a fully annotated dataset consisting of single 4K camera and twelve-camera videos of basketball games.

关键词： Cameras Games Production Streaming media Robot vision systems Computational modeling

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Intelligent Scene Caching to Improve Accuracy for Energy-Constrained Embedded vision

Intelligent Scene Caching to Improve Accuracy for Energy-Con...

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

作者： Simpson, Benjamin Lubana, Ekdeep Liu, Yuchen Dick, Robert Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781728193601

We describe an efficient method of improving the performance of vision algorithms operating on video streams by reducing the amount of data captured and transferred from image sensors to analysis servers in a data-aware manner. The key concept is to combine guided, highly heterogeneous sampling with an intelligent Scene Cache. This enables the system to adapt to spatial and temporal patterns in the scene, thus reducing redundant data capture and processing. A software prototype of our framework running on a general-purpose embedded processor enables superior object detection accuracy (by 56%) at similar energy consumption (slight improvement of 4%) compared to an H.264 hardware accelerator.

关键词： Image enhancement

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NTIRE 2023 Challenge on Stereo Image Super-Resolution: Methods and Results

NTIRE 2023 Challenge on Stereo Image Super-Resolution: Metho...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Wang, Longguang Guo, Yulan Wang, Yingqian Li, Juncheng Gu, Shuhang Timofte, Radu Cheng, Ming Ma, Haoyu Ma, Qiufang Sun, Xiaopeng Zhao, Shijie Sheng, Xuhan Ding, Yukan Sun, Ming Wen, Xing Zhang, Dafeng Li, Jia Wang, Fan Xie, Zheng He, Zongyao Qiu, Zidian Pan, Zilin Zhan, Zhihao Xian, Xingyuan Jin, Zhi Zhou, Yuanbo Deng, Wei Nie, Ruofeng Zhang, Jiajun Gao, Qinquan Tong, Tong Zhang, Kexin Zhang, Junpei Peng, Rui Ma, Yanbiao Jiao, Licheng Bai, Haoran Kong, Lingshun Pan, Jinshan Dong, Jiangxin Tang, Jinhui Cao, Pu Huang, Tianrui Yang, Lu Song, Qing Chen, Bingxin He, Chunhua Chen, Meiyun Guo, Zijie Luo, Shaojuan Cao, Chengzhi Wang, Kunyu Zhang, Fanrui Zhang, Qiang Mehta, Nancy Murala, Subrahmanyam Dudhane, Akshay Wang, Yujin Li, Lingen Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Chen, Junyang Li, Hao Shi, Yukai Yang, Zhijing Zou, Wenbin Zhang, Yunchen Jiang, Mingchao Yu, Zhongxin Tan, Ming Gao, Hongxia Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Chen, Jingxiang Yang, Bo Zhang, Xisheryl Li, Chenghua Yuan, Weijun Li, Zhan Deng, Ruting Zeng, Jintao Mahajan, Pulkit Mistry, Sahaj Chatterjee, Shreyas Jakhetiya, Vinit Subudhi, Badri Jaiswal, Sunil Zhang, Zhao Zheng, Huan Zhao, Suiyi Gao, Yangcheng Wei, Yanyan Wang, Bo Li, Gen Li, Aijin Sun, Lei Chen, Ke Tang, Congling Li, Yunzhe Chiang, Yuan-Chun Chen, Yi-Chung Huang, Zhi-Kai Yang, Hao-Hsiang Chen, I-Hsiang Kuo, Sy-Yen Wang, Yiheng Zhu, Gang Yang, Xingyi Liu, Songhua Jing, Yongcheng Hu, Xingyu Song, Jianwen Sun, Changming Sowmya, Arcot Park, Seung Ho Lei, Xiaoyan Wang, Jingchao Zhai, Chenbo Zhang, Yufei Cao, Weifeng Zhang, Wenlong Aviation University of Air Force China The Shenzhen Campus of Sun Yat-sen University Sun Yatsen University China National University of Defense Technology China Shanghai University China University of Electronic Science and Technology of China China University of Würzburg Germany ETH Zürich Switzerland ByteDance China Kuaishou Technology China Samsung Research China - Beijing China Sun Yat-sen University China Fuzhou University China Imperial Vision Technology Xidian University China Nanjing University of Science and Technology China Beijing University of Posts and Telecommunications China Guangdong University of Technology China The Hong Kong Polytechnic University Hong Kong University of Science and Technology of China China Indian Institute of Technology Ropar India MBZUAI Dubai United Arab Emirates Shanghai Artificial Intelligence Laboratory China Shanghai Jiao Tong University China Assiut University Egypt South China University of Technology China Fujian Normal University China GAC R&D Center Uppsala University Sweden Nanjing University of Information Science and Technology China Chinese Academy of Sciences Institute of Automation China Jinan University China Jiangxi Normal University China Indian Institute of Technology India K-Lens GmbH Hefei University of Technology China McMaster University Canada National Taiwan University Taiwan Research Institute Singapore National University of Singapore Singapore University of Sydney Australia Harbin Institute of Technology China University of New South Wales Australia CSIRO Data61 Australia Seoul National University Korea Republic of Zhengzhou University of Light Industry China

ISBN: (纸本)9798350302493

This paper summarizes the 2nd NTIRE challenge on stereo image super-resolution (SR) with a focus on new solutions and results. The task of the challenge is to super-resolve a low-resolution stereo image pair to a high-resolution one with a magnification factor of ×4. Compared with single image SR, the major challenge of this challenge lies in how to exploit additional information in another viewpoint and how to maintain stereo consistency in the results. This challenge has 3 tracks, including one track on distortion (e.g., PSNR) and bicubic degradation, one track on perceptual quality (e.g., LPIPS) and bicubic degradation, as well as another track on real degradations. In total, 175, 93, and 103 participants were successfully registered for each track, respectively. In the test phase, 21, 17, and 12 teams successfully submitted results with PSNR (RGB) scores better than the baseline. This challenge establishes a new benchmark for stereo image SR. © 2023 ieee.

关键词： Optical resolving power

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Multi-Scale Dynamic and Hierarchical Relationship Modeling for Facial Action Units recognition

Multi-Scale Dynamic and Hierarchical Relationship Modeling f...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Zihan Wang Siyang Song Cheng Luo Songhe Deng Weicheng Xie Linlin Shen Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Shenzhen Institute of Artificial Intelligence and Robotics for Society National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Universiry of Leicester Monash University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Human facial action units (AUs) are mutually related in a hierarchical manner, as not only they are associated with each other in both spatial and temporal domains but also AUs located in the same/close facial regions show stronger relationships than those of different facial regions. While none of existing approach thoroughly model such hi-erarchical inter-dependencies among AUs, this paper proposes to comprehensively model multi-scale AU-related dynamic and hierarchical spatiotemporal relationship among AUs for their occurrences recognition. Specifically, we first propose a novel multi-scale temporal differencing network with an adaptive weighting block to explicitly capture facial dynamics across frames at different spatial scales, which specifically considers the heterogeneity of range and mag-nitude in different AUs' activation. Then, a two-stage strategy is introduced to hierarchically model the relationship among AUs based on their spatial distribution (i.e., local and cross-region AU relationship modelling). Experimental results achieved on BP4D and DISFA show that our approach is the new state-of-the-art in the field of AU occurrence recognition. Our code is publicly available at https://***/CVI-SZU/MDHR.

关键词： Gold Adaptation models computer vision Graphical models Codes Adaptive systems Computational modeling

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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yawei Li Yulun Zhang Radu Timofte Luc Van Gool Lei Yu Youwei Li Xinpeng Li Ting Jiang Qi Wu Mingyan Han Wenjie Lin Chengzhi Jiang Jinting Luo Haoqiang Fan Shuaicheng Liu Yucong Wang Minjie Cai Mingxi Li Yuhang Zhang Xian-Jun Fan Yankai Sheng Yanyu Mao Nihao Zhang Qian Wang Mingjun Zheng Long Sun Jinshan Pan Jiangxin Dong Jinhui Tang Zhongbao Yang Yan Wang Erlin Pan Qixuan Cai Xinan Dai Magauiya Zhussip Nikolay Kalyazin Dmitry Vyal Xueyi Zou Youliang Yan Heaseo Chung Jin Zhang Gaocheng Yu Feng Zhang Hongbin Wang Bohao Liao Zhibo Du Yu-Liang Wu Gege Shi Long Peng Yang Wang Yang Cao Zhengjun Zha Zhi-Kai Huang Yi-Chung Chen Yuan-Chun Chiang Hao-Hsiang Yang Wei-Ting Chen Hua-En Chang I-Hsiang Chen Chia-Hsuan Hsieh Sy-Yen Kuo Xin Liu Jiahao Pan Hongyuan Yu Weichen Yu Lin Ge Jiahua Dong Yajun Zou Zhuoyuan Wu Binnan Han Xiaolin Zhang Heng Zhang Xuanwu Yin Kunlong Zuo Weijian Deng Hongjie Yuan Zengtong Lu Mingyu Ouyang Wenzhuo Ma Nian Liu Hanyou Zheng Yuantong Zhang Junxi Zhang Zhenzhong Chen Garas Gendy Nabil Sabor Jingchao Hou Guanghui He Yurui Zhu Xi Wang Xueyang Fu Zheng-Jun Zha Daheng Yin Mengyang Liu Baijun Chen Ao Li Lei Luo Kangjun Jin Ce Zhu Xiaoming Zhang Chengxing Xie Linze Li Haiteng Meng Tianlin Zhang Tianrui Li Xiaole Zhao Zhao Zhang Baiang Li Huan Zheng Suiyi Zhao Yangcheng Gao Jiahuan Ren Kang Hu Jingpeng Shi Zhijian Wu Dingjiang Huang Jinchen Zhu Hui Li Qianru Xv Tianle Liu Shizhuang Weng Gang Wu Junpeng Jiang Xianming Liu Junjun Jiang Mingjian Zhang Jing Hu Chengxu Wu Qinrui Fan Chengming Feng Ziwei Luo Shu Hu Siwei Lyu Xi Wu Xin Wang Computer Vision Lab ETH Zurich

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution.

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Deep Learning for Automatic Pneumonia Detection

Deep Learning for Automatic Pneumonia Detection

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

作者： Gabruseva, Tatiana Poplavskiy, Dmytro Kalinin, Alexandr Topcon Positioning Syst Brisbane Qld Australia Univ Michigan Ann Arbor MI 48109 USA Shenzhen Res Inst Big Data Shenzhen 518172 Guangdong Peoples R China

ISBN: (纸本)9781728193601

Pneumonia is the leading cause of death among young children and one of the top mortality causes worldwide. The pneumonia detection is usually performed through examine of chest X-Ray radiograph by highly-trained specialists. This process is tedious and often leads to a disagreement between radiologists. computer-aided diagnosis systems showed the potential for improving diagnostic accuracy. In this work, we develop the computational approach for pneumonia regions detection based on single-shot detectors, squeeze-and-extinction deep convolution neural networks, augmentations and multi-task learning. The proposed approach was evaluated in the context of the Radiological society of North America Pneumonia Detection Challenge, achieving one of the best results in the challenge.

关键词： computer aided diagnosis

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Sustainability in HPC: vision and Opportunities

Sustainability in HPC: Vision and Opportunities

引用

2023 International conference on High Performance Computing, Network, Storage, and Analysis, SC workshops 2023

作者： Chadha, Mohak Arima, Eishi Raoofy, Amir Gerndt, Michael Schulz, Martin Technische Universität München Germany Leibniz-Rechenzentrum Germany

ISBN: (纸本)9798400707858

Tackling climate change by reducing and eventually eliminating carbon emissions is a significant milestone on the path toward establishing an environmentally sustainable society. As we transition into the exascale era, marked by an increasing demand and scale of HPC resources, the HPC community must embrace the challenge of reducing carbon emissions from designing and operating modern HPC systems. In this position paper, we describe challenges and highlight different opportunities that can aid HPC sites in reducing the carbon footprint of modern HPC systems. © 2023 ACM.

关键词： Carbon footprint

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DRHDR: A Dual branch Residual Network for Multi-Bracket High Dynamic Range Imaging

DRHDR: A Dual branch Residual Network for Multi-Bracket High...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Juan Marí n-Vega Michael Sloth Peter Schneider-Kamp Richard Rö ttger Department of Mathematics and Computer Science (IMADA) University of Southern Denmark Esoft Systems

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

We introduce DRHDR, a Dual branch Residual Convolutional Neural Network for Multi-Bracket HDR Imaging. To address the challenges of fusing multiple brackets from dynamic scenes, we propose an efficient dual branch network that operates on two different resolutions. The full resolution branch uses a Deformable Convolutional Block to align features and retain high-frequency details. A low resolution branch with a Spatial Attention Block aims to attend wanted areas from the non-reference brackets, and suppress displaced features that could incur on ghosting artifacts. By using a dual branch approach we are able to achieve high quality results while constraining the computational resources required to estimate the HDR results.

关键词： computer vision conferences Imaging Dynamic range pattern recognition Complexity theory Convolutional neural networks

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