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2023 IEEE/CVF Conference on computer vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yawei Zhang, Yulun Timofte, Radu Van Gool, Luc Yu, Lei Li, Youwei Li, Xinpeng Jiang, Ting Wu, Qi Han, Mingyan Lin, Wenjie Jiang, Chengzhi Luo, Jinting Fan, Haoqiang Liu, Shuaicheng Wang, Yucong Cai, Minjie Li, Mingxi Zhang, Yuhang Fan, Xian-Jun Sheng, Yankai Mao, Yanyu Zhang, Nihao Wang, Qian Zheng, Mingjun Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Zhongbao Wang, Yan Pan, Erlin Cai, Qixuan Dai, Xinan Zhussip, Magauiya Kalyazin, Nikolay Vyal, Dmitry Zou, Xueyi Yan, Youliang Chung, Heaseo Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Liao, Bohao Du, Zhibo Wu, Yu-Liang Shi, Gege Peng, Long Wang, Yang Cao, Yang Zha, Zhengjun Huang, Zhi-Kai Chen, Yi-Chung Chiang, Yuan-Chun Yang, Hao-Hsiang Chen, Wei-Ting Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Liu, Xin Pan, Jiahao Yu, Hongyuan Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Yin, Xuanwu Zuo, Kunlong Deng, Weijian Yuan, Hongjie Lu, Zengtong Ouyang, Mingyu Ma, Wenzhuo Liu, Nian Zheng, Hanyou Zhang, Yuantong Zhang, Junxi Chen, Zhenzhong Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhu, Yurui Wang, Xi Fu, Xueyang Zha, Zheng-Jun Yin, Daheng Liu, Mengyang Chen, Baijun Li, Ao Luo, Lei Jin, Kangjun Zhu, Ce Zhang, Xiaoming Xie, Chengxing Li, Linze Meng, Haiteng Zhang, Tianlin Li, Tianrui Zhao, Xiaole Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Ren, Jiahuan Hu, Kang Shi, Jingpeng Wu, Zhijian Huang, Dingjiang Zhu, Jinchen Li, Hui Xv, Qianru Liu, Tianle Weng, Shizhuang Wu, Gang Jiang, Junpeng Liu, Xianming Jiang, Junjun Zhang, Mingjian Hu, Jing Wu, Chengxu Fan, Qinrui Feng, Chengming Luo, Ziwei Hu, Shu Lyu, Siwei Wu, Xi Wang, Xin Computer Vision Lab ETH Zurich Switzerland Computer Vision Lab University of Würzburg Germany Megvii Technology China MicroBT China College of Computer Science and Electronic Engineering Hunan University China Attrsense Xian University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Cyber Event Warning and Control Technologies China Nanjing University of Science and Technology China Nankai University China University of Electronic Science and Technology of China China Tianjin University China Noah's Ark Lab Huawei Technologies McMaster University Canada Espresomedia Korea Republic of AntGroup China University of Science and Technology of China China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan ServiceNow United States China Mobile Research Institute China Chongqing University of Technology China Multimedia Department Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information and Communication Engineering Communication University of China China Smart Classroom Division Ruijie Networks Co. Ltd. China School of Remote Sensing and Information Engineering Wuhan University China Micro-Nano Electronics Department Shanghai Jiao Tong University Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Southeast University China University of Electronic Science and Technology of China Chengdu China Chongqing University of Posts and Telecommunications Chongqing China Information Technology Co. Ltd Hangzhou China Southwest Jiaotong University China National Space Science Center Chinese Academy of Science China Hefei University of Technology Hefei China Anhui University China Fried Ric

ISBN: (纸本)9798350302493

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. © 2023 IEEE.

关键词： Optical resolving power

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Fast and accurate single-image depth estimation on mobile devices, mobile AI 2021 challenge: Report

arXiv

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arXiv 2021年

作者： Ignatov, Andrey Malivenko, Grigory Plowman, David Shukla, Samarth Timofte, Radu Zhang, Ziyu Wang, Yicheng Huang, Zilong Luo, Guozhong Yu, Gang Fu, Bin Wang, Yiran Li, Xingyi Shi, Min Xian, Ke Cao, Zhiguo Du, Jin-Hua Wu, Pei-Lin Ge, Chao Yao, Jiaoyang Tu, Fangwen Li, Bo Yoo, Jung Eun Seo, Kwanggyoon Xu, Jialei Li, Zhenyu Liu, Xianming Jiang, Junjun Chen, Wei-Chi Joya, Shayan Fan, Huanhuan Kang, Zhaobing Li, Ang Feng, Tianpeng Liu, Yang Sheng, Chuannan Yin, Jian Benavides, Fausto T. Computer Vision Lab ETH Zurich Switzerland Ltd AI Witchlabs Switzerland Tencent GY-Lab China Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Nanjing Artificial Intelligence Chip Research Institute of Automation Chinese Academy of Sciences China Black Sesame Technologies Inc. Singapore Singapore Visual Media Lab KAIST Korea Republic of Harbin Institute of Technology China Peng Cheng Laboratory China Multimedia and Computer Vision Laboratory National Cheng Kung University Taiwan Samsung Research UK United Kingdom OPPO Research Institute China ETH Zurich Switzerland

Depth estimation is an important computer vision problem with many practical applications to mobile devices. While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based depth estimation solutions that can demonstrate a nearly real-time performance on smartphones and IoT platforms. For this, the participants were provided with a new large-scale dataset containing RGB-depth image pairs obtained with a dedicated stereo ZED camera producing high-resolution depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the popular Raspberry Pi 4 platform with a mobile ARM-based Broadcom chipset. The proposed solutions can generate VGA resolution depth maps at up to 10 FPS on the Raspberry Pi 4 while achieving high fidelity results, and are compatible with any Android or Linux-based mobile devices. A detailed description of all models developed in the challenge is provided in this paper. Copyright © 2021, The Authors. All rights reserved.

关键词： computer operating systems

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MIPI 2024 Challenge on Nighttime Flare Removal: Methods and Results

arXiv

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arXiv 2024年

作者： Dai, Yuekun Zhang, Dafeng Li, Xiaoming Yue, Zongsheng Li, Chongyi Zhou, Shangchen Feng, Ruicheng Yang, Peiqing Jin, Zhezhu Liu, Guanqun Loy, Chen Change Zhang, Lize Liu, Shuai Feng, Chaoyu Wang, Luyang Chen, Shuan Shao, Guangqi Wang, Xiaotao Lei, Lei Yang, Qirui Cheng, Qihua Xu, Zhiqiang Liu, Yihao Yue, Huanjing Yang, Jingyu Vasluianu, Florin-Alexandru Wu, Zongwei Ciubotariu, George Timofte, Radu Zhang, Zhao Zhao, Suiyi Wang, Bo Zuo, Zhichao Wei, Yanyan Teja, Kuppa Sai Sri Jayakar Reddy, A. Rongali, Girish Mitra, Kaushik Ma, Zhihao Liu, Yongxu Zhang, Wanying Shang, Wei He, Yuhong Peng, Long Yu, Zhongxin Luo, Shaofei Wang, Jian Miao, Yuqi Li, Baiang Wei, Gang Verma, Rakshank Maheshwari, Ritik Tekchandani, Rahul Hambarde, Praful Tazi, Satya Narayan Vipparthi, Santosh Kumar Murala, Subrahmanyam Zhang, Haopeng Hou, Yingli Yao, Mingde Levin, M.S. Sundararajan, Aniruth Hari Kumar, A. Xiaomi Inc. China School of Electrical and Information Engineering Tianjin University China Shenzhen MicroBT Electronics Technology Co. Ltd China Shanghai Artificial Intelligence Laboratory China Computer Vision Lab CAIDAS IFI University of Würzburg Germany Laboratory for Multimedia Computing Hefei University of Technology China Detect Technologies Pvt Ltd India Indian Institute of Technology Madras India Xidian University China Harbin Institute of Technology China Northeastern University China University of Science and Technology of China China Fujian Normal University China Snap Inc. United States Tongji University China Hefei University of Technology China GEC Ajmer India CVPR Lab IIT Ropar India SCSS Trinity College Dublin Ireland Faculty of Robot Science and Engineering Northeastern University China Software College Northeastern University China The Chinese University of Hong Kong China Shiv Nadar University India

The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). Building on the achievements of the previous MIPI Workshops held at ECCV 2022 and CVPR 2023, we introduce our third MIPI challenge including three tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Nighttime Flare Removal track on MIPI 2024. In total, 170 participants were successfully registered, and 14 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Nighttime Flare Removal. More details of this challenge and the link to the dataset can be found at https://***/MIPI2024. Copyright © 2024, The Authors. All rights reserved.

关键词： Image sensors

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Image Captioning with Clause-Focused Metrics in a Multi-Modal Setting for Marketing

arXiv

引用

arXiv 2019年

作者： Harzig, Philipp Zecha, Dan Lienhart, Rainer Kaiser, Carolin Schallner, René Multimedia Computing and Computer Vision Lab University of Augsburg Augsburg86159 Germany GfK Verein Nuremberg90419 Germany

Automatically generating descriptive captions for images is a well-researched area in computer vision. However, existing evaluation approaches focus on measuring the similarity between two sentences disregarding fine-grained semantics of the captions. In our setting of images depicting persons interacting with branded products, the subject, predicate, object and the name of the branded product are important evaluation criteria of the generated captions. Generating image captions with these constraints is a new challenge, which we tackle in this work. By simultaneously predicting integer-valued ratings that describe attributes of the human-product interaction, we optimize a deep neural network architecture in a multi-task learning setting, which considerably improves the caption quality. Furthermore, we introduce a novel metric that allows us to assess whether the generated captions meet our requirements (i.e., subject, predicate, object, and product name) and describe a series of experiments on caption quality and how to address annotator disagreements for the image ratings with an approach called soft targets. We also show that our novel clause-focused metrics are also applicable to other image captioning datasets, such as the popular MSCOCO dataset. Copyright © 2019, The Authors. All rights reserved.

关键词： Integer programming

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Feature intertwiner for object detection

arXiv

引用

arXiv 2019年

作者： Li, Hongyang Dai, Bo Shi, Shaoshuai Ouyang, Wanli Wang, Xiaogang Multimedia-SenseTime Joint Lab Chinese University of Hong Kong SenseTime Computer Vision Research Group University of Sydney

A well-trained model should classify objects with a unanimous score for every category. This requires the high-level semantic features should be as much alike as possible among samples. To achive this, previous works focus on re-designing the loss or proposing new regularization constraints. In this paper, we provide a new perspective. For each category, it is assumed that there are two feature sets: one with reliable information and the other with less reliable source. We argue that the reliable set could guide the feature learning of the less reliable set during training - in spirit of student mimicking teacher's behavior and thus pushing towards a more compact class centroid in the feature space. Such a scheme also benefits the reliable set since samples become closer within the same category - implying that it is easier for the classifier to identify. We refer to this mutual learning process as feature intertwiner and embed it into object detection. It is well-known that objects of low resolution are more difficult to detect due to the loss of detailed information during network forward pass (e.g., RoI operation). We thus regard objects of high resolution as the reliable set and objects of low resolution as the less reliable set. Specifically, an intertwiner is designed to minimize the distribution divergence between two sets. The choice of generating an effective feature representation for the reliable set is further investigated, where we introduce the optimal transport (OT) theory into the framework. Samples in the less reliable set are better aligned with aid of OT metric. Incorporated with such a plug-and-play intertwiner, we achieve an evident improvement over previous state-of-the-arts. Copyright © 2019, The Authors. All rights reserved.

关键词： Object recognition

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NTIRE 2024 Challenge on Image Super-Resolution (×4): Methods and Results

arXiv

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arXiv 2024年

作者： Chen, Zheng Wu, Zongwei Zamfir, Eduard Zhang, Kai Zhang, Yulun Timofte, Radu Yang, Xiaokang Yu, Hongyuan Wan, Cheng Hong, Yuxin Huang, Zhijuan Zou, Yajun Huang, Yuan Lin, Jiamin Han, Bingnan Guan, Xianyu Yu, Yongsheng Zhang, Daoan Yin, Xuanwu Zuo, Kunlong Hao, Jinhua Zhao, Kai Yuan, Kun Sun, Ming Zhou, Chao An, Hongyu Zhang, Xinfeng Song, Zhiyuan Dong, Ziyue Zhao, Qing Xu, Xiaogang Wei, Pengxu Dou, Zhi-Chao Wang, Gui-Ling Hsu, Chih-Chung Lee, Chia-Ming Chou, Yi-Shiuan Korkmaz, Cansu Tekalp, A. Murat Wei, Yubin Yan, Xiaole Li, Binren Chen, Haonan Zhang, Siqi Chen, Sihan Joshi, Amogh Akalwadi, Nikhil Malagi, Sampada Yashaswini, Palani Desai, Chaitra Tabib, Ramesh Ashok Patil, Ujwala Mudenagudi, Uma Sarvaiya, Anjali Choksy, Pooja Joshi, Jagrit Kawa, Shubh Upla, Kishor Patwardhan, Sushrut Ramachandra, Raghavendra Hossain, Sadat Park, Geongi Uddin, S.M. Nadim Xu, Hao Guo, Yanhui Urumbekov, Aman Yan, Xingzhuo Hao, Wei Fu, Minghan Orais, Isaac Smith, Samuel Liu, Ying Jia, Wangwang Xu, Qisheng Xu, Kele Yuan, Weijun Li, Zhan Kuang, Wenqin Guan, Ruijin Deng, Ruting Zhang, Zhao Wang, Bo Zhao, Suiyi Luo, Yan Wei, Yanyan Khan, Asif Hussain Micheloni, Christian Martinel, Niki Shanghai Jiao Tong University China University of Würzburg Germany Computer Vision Lab ETH Zurich Switzerland Multimedia Department Xiaomi Inc China Georgia Institute of Technology United States Lanzhou University China University of Rochester United States Kuaishou Technology China School of Computer Science and Technology University of Chinese Academy of Sciences China Sun Yat-sen University China Xi’an Jiaotong University China Zhejiang University China Shandong University of Science and Technology Qingdao China Institute of Data Science National Cheng Kung University Taiwan Koc University Turkey Xiamen University China KLE Technological University Karnataka Hubballi India School of Electronics and Communication Engineering KLE Technological University Karnataka Hubballi India School of Computer Science and Engineering KLE Technological University Karnataka Hubballi India Sardar Vallabhbhai National Institute of Technology India Norwegian University of Science and Technology Norway DeltaX Seoul Korea Republic of McMaster University HamiltonON Canada Kyrgyzstan Bosch Investment Ltd China Santa Clara University Santa ClaraCA United States National University of Defense Technology China Jinan University China Hefei University of Technology China University of Udine Italy

This paper reviews the NTIRE 2024 challenge on image super-resolution (×4), highlighting the solutions proposed and the outcomes obtained. The challenge involves generating corresponding high-resolution (HR) images, magnified by a factor of four, from low-resolution (LR) inputs using prior information. The LR images originate from bicubic downsampling degradation. The aim of the challenge is to obtain designs/solutions with the most advanced SR performance, with no constraints on computational resources (e.g., model size and FLOPs) or training data. The track of this challenge assesses performance with the PSNR metric on the DIV2K testing dataset. The competition attracted 199 registrants, with 20 teams submitting valid entries. This collective endeavour not only pushes the boundaries of performance in single-image SR but also offers a comprehensive overview of current trends in this field. Copyright © 2024, The Authors. All rights reserved.

关键词： Statistical tests

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Addressing data bias problems for chest X-ray image report generation

arXiv

引用

arXiv 2019年

作者： Harzig, Philipp Chen, Yan-Ying Chen, Francine Lienhart, Rainer Multimedia Computing and Computer Vision Lab University of Augsburg Augsburg Germany FX Palo Alto Laboratory 3174 Porter Drive Palo AltoCA United States

Automatic medical report generation from chest X-ray images is one possibility for assisting doctors to reduce their workload. However, the different patterns and data distribution of normal and abnormal cases can bias machine learning models. Previous attempts did not focus on isolating the generation of the abnormal and normal sentences in order to increase the variability of generated paragraphs. To address this, we propose to separate abnormal and normal sentence generation by using a dual word LSTM in a hierarchical LSTM model. In addition, we conduct an analysis on the distinctiveness of generated sentences compared to the BLEU score, which increases when less distinct reports are generated. Together with this analysis, we propose a way of selecting a model that generates more distinctive sentences. We hope our findings will help to encourage the development of new metrics to better verify methods of automatic medical report generation. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

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Activity-conditioned continuous human pose estimation for performance analysis of athletes using the example of swimming

arXiv

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arXiv 2018年

作者： Einfalt, Moritz Zecha, Dan Lienhart, Rainer Multimedia Computing and Computer Vision Lab University of Augsburg Germany

In this paper we consider the problem of human pose estimation in real-world videos of swimmers. Swimming channels allow filming swimmers simultaneously above and below the water surface with a single stationary camera. These recordings can be used to quantitatively assess the athletes’ performance. The quantitative evaluation, so far, requires manual annotations of body parts in each video frame. We therefore apply the concept of CNNs in order to automatically infer the required pose information. Starting with an off-the-shelf architecture, we develop extensions to leverage activity information – in our case the swimming style of an athlete – and the continuous nature of the video recordings. Our main contributions are threefold: (a) We apply and evaluate a fine-tuned Convolutional Pose Machine architecture as a baseline in our very challenging aquatic environment and discuss its error modes, (b) we propose an extension to input swimming style information into the fully convolutional architecture and (c) modify the architecture for continuous pose estimation in videos. With these additions we achieve reliable pose estimates with up to +16% more correct body joint detections compared to the baseline architecture. Copyright © 2018, The Authors. All rights reserved.

关键词： Convolution

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AIM 2020 Challenge on Image Extreme Inpainting 16th

AIM 2020 Challenge on Image Extreme Inpainting

引用

Workshops held at the 16th European Conference on computer vision, ECCV 2020

作者： Ntavelis, Evangelos Romero, Andrés Bigdeli, Siavash Timofte, Radu Hui, Zheng Wang, Xiumei Gao, Xinbo Shin, Chajin Kim, Taeoh Son, Hanbin Lee, Sangyoun Li, Chao Li, Fu He, Dongliang Wen, Shilei Ding, Errui Bai, Mengmeng Li, Shuchen Zeng, Yu Lin, Zhe Yang, Jimei Zhang, Jianming Shechtman, Eli Lu, Huchuan Zeng, Weijian Ni, Haopeng Cai, Yiyang Li, Chenghua Xu, Dejia Wu, Haoning Han, Yu Nadim, Uddin S. M. Jang, Hae Woong Ahmed, Soikat Hasan Yoon, Jungmin Jung, Yong Ju Li, Chu-Tak Liu, Zhi-Song Wang, Li-Wen Siu, Wan-Chi Lun, Daniel P. K. Suin, Maitreya Purohit, Kuldeep Rajagopalan, A.N. Narang, Pratik Mandal, Murari Chauhan, Pranjal Singh Computer Vision Lab ETH Zürich Zürich Switzerland CSEM Neuchâtel Switzerland School of Electronic Engineering Xidian University Xi’an China Image and Video Pattern Recognition Laboratory School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of Baidu Inc. Beijing China Beijing China Dalian University of Technology Dalian China Adobe San Jose United States Rensselaer Polytechnic Institute Troy United States Peking University Beijing China Lab Gachon University Seongnam Korea Republic of Centre for Multimedia Signal Processing Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong China Indian Institute of Technology Madras Chennai India BITS Pilani Pilani India MNIT Jaipur Jaipur India

ISBN: (纸本)9783030670696

This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting. The goal of track 1 is to inpaint large part of the image with no supervision. Similarly, the goal of track 2 is to inpaint the image by having access to the entire semantic segmentation map of the input. The challenge had 88 and 74 participants, respectively. 11 and 6 teams competed in the final phase of the challenge, respectively. This report gauges current solutions and set a benchmark for future extreme image inpainting methods. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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Pre-trainable reservoir computing with recursive neural gas

arXiv

引用

arXiv 2018年

作者： Carcano, Luca Plebani, Emanuele Pau, Danilo Pietro Piastra, Marco Computer Vision and Multimedia Lab Università Degli Studi Di Pavia STMicroelectronics

Echo State Networks (ESN) are a class of Recurrent Neural Networks (RNN) that has gained substantial popularity due to their effectiveness, ease of use and potential for compact hardware implementation. An ESN contains the three network layers input, reservoir and readout where the reservoir is the truly recurrent network. The input and reservoir layers of an ESN are initialized at random and never trained afterwards and the training of the ESN is applied to the readout layer only. The alternative of Recursive Neural Gas (RNG) is one of the many proposals of fully-trainable reservoirs that can be found in the literature. Although some improvements in performance have been reported with RNG, to the best of authors' knowledge, no experimental comparative results are known with benchmarks for which ESN is known to yield excellent results. This work describes an accurate model of RNG together with some extensions to the models presented in the literature and shows comparative results on three well-known and accepted datasets. The experimental results obtained show that, under specific circumstances, RNG-based reservoirs can achieve better performance. Copyright © 2018, The Authors. All rights reserved.

关键词： Recurrent neural networks

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