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Improving GAN Equilibrium by Raising Spatial Awareness

Improving GAN Equilibrium by Raising Spatial Awareness

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conference on computer vision and pattern recognition (CVPR)

作者： Jianyuan Wang Ceyuan Yang Yinghao Xu Yujun Shen Hongdong Li Bolei Zhou The Chinese University of Hong Kong The Australian National University University of California Los Angeles

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469470

The success of Generative Adversarial Networks (GANs) is largely built upon the adversarial training between a generator (G) and a discriminator (D). They are expected to reach a certain equilibrium where D cannot distinguish the generated images from the real ones. However, such an equilibrium is rarely achieved in practical GAN training, instead, D almost always surpasses G. We attribute one of its sources to the information asymmetry between D and G. We observe that D learns its own visual attention when determining whether an image is real or fake, but G has no explicit clue on which regions to focus on for a particular synthesis. To alleviate the issue of D dominating the competition in GANs, we aim to raise the spatial awareness of G. Randomly sampled multi-level heatmaps are encoded into the intermediate layers of G as an inductive bias. Thus G can purposefully improve the synthesis of certain image regions. We further propose to align the spatial awareness of G with the attention map induced from D. Through this way we effectively lessen the information gap between D and G. Extensive results show that our method pushes the two-player game in GANs closer to the equilibrium, leading to a better synthesis performance. As a byproduct, the intro-duced spatial awareness facilitates interactive editing over the output synthesis. Demo video and code are available at https://***/eqgan-sa/

关键词： Training Heating systems Visualization computer vision Image synthesis Computational modeling Games

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Virtual Immunohistochemistry Staining for Histological Images Assisted by Weakly-supervised Learning

Virtual Immunohistochemistry Staining for Histological Image...

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conference on computer vision and pattern recognition (CVPR)

作者： Jiahan Li Jiuyang Dong Shenjin Huang Xi Li Junjun Jiang Xiaopeng Fan Yongbing Zhang Harbin Institute of Technology Department of Gastroenterology Peking University Shenzhen Hospital Harbin Institute of Technology Shenzhen

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Recently, virtual staining technology has greatly promoted the advancement of histopathology. Despite the practical successes achieved, the outstanding performance of most virtual staining methods relies on hard-to-obtain paired images in training. In this paper, we propose a method for virtual immunohistochemistry (IHC) staining, named confusion-GAN, which does not require paired images and can achieve comparable performance to supervised algorithms. Specifically, we propose a multi-branch discriminator, which judges if the features of generated images can be embedded into the feature pool of target domain images, to improve the visual quality of generated images. Meanwhile, we also propose a novel patch-level pathology information extractor, which is assisted by multiple instance learning, to ensure pathological consistency during virtual staining. Extensive experiments were conducted on three types of IHC images, including a high-resolution hepatocel-lular carcinoma immunohistochemical dataset proposed by us. The results demonstrated that our proposed confusion-GAN can generate highly realistic images that are capable of deceiving even experienced pathologists. Furthermore, compared to using H&E images directly, the downstream diagnosis achieved higher accuracy when using images generated by confusion-GAN. Our dataset and codes will be available at https://***/jiahanli2022/confusion-GAN.

关键词： Training Visualization computer vision Accuracy Codes Image synthesis Histopathology

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GASP, a generalized framework for agglomerative clustering of signed graphs and its application to Instance Segmentation

GASP, a generalized framework for agglomerative clustering o...

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conference on computer vision and pattern recognition (CVPR)

作者： Alberto Bailoni Constantin Pape Nathan Hütsch Steffen Wolf Thorsten Beier Anna Kreshuk Fred A. Hamprecht Heidelberg Collaboratory for Image Processing (HCI/IWR) Heidelberg Germany EMBL Heidelberg Germany Institute for Computer Science University Göttingen Germany MRC Laboratory of Molecular Biology Cambridge UK

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469470

We propose a theoretical framework that generalizes simple and fast algorithms for hierarchical agglomerative clustering to weighted graphs with both attractive and repulsive interactions between the nodes. This framework defines GASP, a Generalized Algorithm for Signed graph Partitioning 1 1 Code available at: https://***/abailoni/GASP, and allows us to explore many combinations of different linkage criteria and cannotlink constraints. We prove the equivalence of existing clustering methods to some of those combinations and introduce new algorithms for combinations that have not been studied before. We study both theoretical and empirical properties of these combinations and prove that some of these define an ultrametric on the graph. We conduct a systematic comparison of various instantiations of GASP on a large variety of both synthetic and existing signed clustering problems, in terms of accuracy but also efficiency and robustness to noise. Lastly, we show that some of the algorithms included in our framework, when combined with the predictions from a CNN model, result in a simple bottom-up instance segmentation pipeline. Going all the way from pixels to final segments with a simple procedure, we achieve state-of-the-art accuracy on the CREMI 2016 EM segmentation benchmark without requiring domain-specific superpixels.

关键词： Couplings Systematics Pipelines Clustering algorithms Predictive models Prediction algorithms Robustness

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Fine-Grained Predicates Learning for Scene Graph Generation

Fine-Grained Predicates Learning for Scene Graph Generation

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conference on computer vision and pattern recognition (CVPR)

作者： Xinyu Lyu Lianli Gao Yuyu Guo Zhou Zhao Hao Huang Heng Tao Shen Jingkuan Song Center for Future Media & School of Computer Science and Engineering University of Electronic Science and Technology of China China Zhejiang University China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469470

The performance of current Scene Graph Generation models is severely hampered by some hard-to-distinguish predicates, e.g., “woman-on/standing on/walking on-beach” or “woman-near/looking at/in front of-child”. While general SGG models are prone to predict head predicates and existing re-balancing strategies prefer tail categories, none of them can appropriately handle these hard-to-distinguish predicates. To tackle this issue, inspired by fine-grained image classification, which focuses on differentiating among hard-to-distinguish object classes, we propose a method named Fine-Grained Predicates Learning (FGPL) which aims at differentiating among hard-to-distinguish predicates for Scene Graph Generation task. Specifically, we first introduce a Predicate Lattice that helps SGG models to figure out fine-grained predicate pairs. Then, utilizing the Predicate Lattice, we propose a Category Discriminating Loss and an Entity Discriminating Loss, which both contribute to distinguishing fine-grained predicates while maintaining learned discriminatory power over recognizable ones. The proposed model-agnostic strategy significantly boosts the performances of three benchmark models (Transformer, VCTree, and Motif) by 22.8%, 24.1% and 21.7% of Mean Recall (mR@100) on the Predicate Classification sub-task, respectively. Our model also outperforms state-of-the-art methods by a large margin (i.e., 6.1%, 4.6%, and 3.2% of Mean Recall (mR@100)) on the Visual Genome dataset. Codes are publicly available 1 1 https://***/XinyuLyu/FGPL.

关键词： Visualization Head Lattices Tail Benchmark testing Predictive models Transformers

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NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

NTIRE 2024 Challenge on Low Light Image Enhancement: Methods...

引用

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

作者： Xiaoning Liu Zongwei Wu Ao Li Florin-Alexandru Vasluianu Yulun Zhang Shuhang Gu Le Zhang Ce Zhu Radu Timofte Zhi Jin Hongjun Wu Chenxi Wang Haitao Ling Yuanhao Cai Hao Bian Yuxin Zheng Jing Lin Alan Yuille Ben Shao Jin Guo Tianli Liu Mohao Wu Yixu Feng Shuo Hou Haotian Lin Yu Zhu Peng Wu Wei Dong Jinqiu Sun Yanning Zhang Qingsen Yan Wenbin Zou Weipeng Yang Yunxiang Li Qiaomu Wei Tian Ye Sixiang Chen Zhao Zhang Suiyi Zhao Bo Wang Yan Luo Zhichao Zuo Mingshen Wang Junhu Wang Yanyan Wei Xiaopeng Sun Yu Gao Jiancheng Huang Hongming Chen Xiang Chen Hui Tang Yuanbin Chen Yuanbo Zhou Xinwei Dai Xintao Qiu Wei Deng Qinquan Gao Tong Tong Mingjia Li Jin Hu Xinyu He Xiaojie Guo Sabarinathan Sabarinathan K Uma A Sasithradevi B Sathya Bama S. Mohamed Mansoor Roomi V. Srivatsav Jinjuan Wang Long Sun Qiuying Chen Jiahong Shao Yizhi Zhang Marcos V. Conde Daniel Feijoo Juan C. Benito Alvaro García Jaeho Lee Seongwan Kim Sharif S M A Nodirkhuja Khujaev Roman Tsoy Ali Murtaza Uswah Khairuddin Ahmad ’Athif Mohd Faudzi Sampada Malagi Amogh Joshi Nikhil Akalwadi Chaitra Desai Ramesh Ashok Tabib Uma Mudenagudi Wenyi Lian Wenjing Lian Jagadeesh Kalyanshetti Vijayalaxmi Ashok Aralikatti Palani Yashaswini Nitish Upasi Dikshit Hegde Ujwala Patil Sujata C Xingzhuo Yan Wei Hao Minghan Fu Pooja Choksy Anjali Sarvaiya Kishor Upla Kiran Raja Hailong Yan Yunkai Zhang Baiang Li Jingyi Zhang Huan Zheng

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and visually appealing results when dealing with a variety of conditions, including ultra-high resolution (4K and beyond), non-uniform illumination, backlighting, extreme darkness, and night scenes. A notable total of 428 participants registered for the challenge, with 22 teams ultimately making valid submissions. This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field.

关键词： computer vision Image resolution Reviews conferences Lighting pattern recognition Image enhancement

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NTIRE 2024 Quality Assessment of AI-Generated Content Challenge

NTIRE 2024 Quality Assessment of AI-Generated Content Challe...

引用

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

作者： Xiaohong Liu Xiongkuo Min Guangtao Zhai Chunyi Li Tengchuan Kou Wei Sun Haoning Wu Yixuan Gao Yuqin Cao Zicheng Zhang Xiele Wu Radu Timofte Fei Peng Huiyuan Fu Anlong Ming Chuanming Wang Huadong Ma Shuai He Zifei Dou Shu Chen Huacong Zhang Haiyi Xie Chengwei Wang Baoying Chen Jishen Zeng Jianquan Yang Weigang Wang Xi Fang Xiaoxin Lv Jun Yan Tianwu Zhi Yabin Zhang Yaohui Li Yang Li Jingwen Xu Jianzhao Liu Yiting Liao Junlin Li Zihao Yu Fengbin Guan Yiting Lu Xin Li Hossein Motamednia S. Farhad Hosseini-Benvidi Ahmad Mahmoudi-Aznaveh Azadeh Mansouri Ganzorig Gankhuyag Kihwan Yoon Yifang Xu Haotian Fan Fangyuan Kong Shiling Zhao Weifeng Dong Haibing Yin Li Zhu Zhiling Wang Bingchen Huang Avinab Saha Sandeep Mishra Shashank Gupta Rajesh Sureddi Oindrila Saha Luigi Celona Simone Bianco Paolo Napoletano Raimondo Schettini Junfeng Yang Jing Fu Wei Zhang Wenzhi Cao Limei Liu Han Peng Weijun Yuan Zhan Li Yihang Cheng Yifan Deng Haohui Li Bowen Qu Yao Li Shuqing Luo Shunzhou Wang Wei Gao Zihao Lu Marcos V. Conde Xinrui Wang Zhibo Chen Ruling Liao Yan Ye Qiulin Wang Bing Li Zhaokun Zhou Miao Geng Rui Chen Xin Tao Xiaoyu Liang Shangkun Sun Xingyuan Ma Jiaze Li Mengduo Yang Haoran Xu Jie Zhou Shiding Zhu Bohan Yu Pengfei Chen Xinrui Xu Jiabin Shen Zhichao Duan Erfan Asadi Jiahe Liu Qi Yan Youran Qu Xiaohui Zeng Lele Wang Renjie Liao

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact *** video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC.

关键词： Image quality computer vision conferences Computational modeling Market research Quality assessment pattern recognition

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NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results

NTIRE 2018 Challenge on Single Image Super-Resolution: Metho...

引用

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

作者： Radu Timofte Shuhang Gu Jiqing Wu Luc Van Gool Lei Zhang Ming-Hsuan Yang Muhammad Haris Greg Shakhnarovich Norimichi Ukita Shijia Hu Yijie Bei Zheng Hui Xiao Jiang Yanan Gu Jie Liu Yifan Wang Federico Perazzi Brian McWilliams Alexander Sorkine-Hornung Olga Sorkine-Hornung Christopher Schroers Jiahui Yu Yuchen Fan Jianchao Yang Ning Xu Zhaowen Wang Xinchao Wang Thomas S. Huang Xintao Wang Ke Yu Tak-Wai Hui Chao Dong Liang Lin Chen Change Loy Dongwon Park Kwanyoung Kim Se Young Chun Kai Zhang Pengjv Liu Wangmeng Zuo Shi Guo Jiye Liu Jinchang Xu Yijiao Liu Fengye Xiong Yuan Dong Hongliang Bai Alexandru Damian Nikhil Ravi Sachit Menon Cynthia Rudin Junghoon Seo Taegyun Jeon Jamyoung Koo Seunghyun Jeon Soo Ye Kim Jae-Seok Choi Sehwan Ki Soomin Seo Hyeonjun Sim Saehun Kim Munchurl Kim Rong Chen Kun Zeng Jinkang Guo Yanyun Qu Cuihua Li Namhyuk Ahn Byungkon Kang Kyung-Ah Sohn Yuan Yuan Jiawei Zhang Jiahao Pang Xiangyu Xu Yan Zhao Wei Deng Sibt Ul Hussain Muneeb Aadil Rafia Rahim Xiaowang Cai Fang Huang Yueshu Xu Pablo Navarrete Michelini Dan Zhu Hanwen Liu Jun-Hyuk Kim Jong-Seok Lee Yiwen Huang Ming Qiu Liting Jing Jiehang Zeng Ying Wang Manoj Sharma Rudrabha Mukhopadhyay Avinash Upadhyay Sriharsha Koundinya Ankit Shukla Santanu Chaudhury Zhe Zhang Yu Hen Hu Lingzhi Fu ETH Zurich Zurich Switzerland Merantix Germany NTIRE Eidgenossische Technische Hochschule Zurich Zurich ZH CH ESAT KU Leuven Belgium University of California at Merced CA USA Toyota Technological Institute TTI-Chicago Chicago US Duke University US School of Electronic Engineering Xidian University China Disney Research Facebook Oculus University of Illinois at Urbana-Champaign Illinois US Snap Inc. Snap Inc. Adobe Research Stevens Institute of Technology US University of Illinois at Urbana-Champaign US The Chinese University of Hong Kong Hong Kong China SenseTime Research School of Electrical and Computer Engineering National Institute of Science and Technolog Korea School of Electrical and Computer Engineering National Institute of Science and Technology Korea School of Computer Science and Technology Harbin Institute of Technology Harbin China Beijing University of Posts and Telecommunications China Beijing Faceall Technology Co. Ltd China Satrec Initiative R&D Center Korea Advanced Institute of Science and Technology Korea Xiamen University China Xiamen Universi China Ajou University Korea Electrical and Computer Engineering University of Alberta Canada Fuzhou University China National University of Computer & Emerging Sciences Islamabad Pakistan BOE Technology Group Co. Ltd. Yonsei University Korea Wenhua College China Software School Xiamen University China CSIR-CEERI India University of Wisconsin-Madison Madison Wisconsin US Institute of Integrated Automation School of Electronic and Information Engineering Xian Jiaotong University China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661017

This paper reviews the 2nd NTIRE challenge on single image super-resolution (restoration of rich details in a low resolution image) with focus on proposed solutions and results. The challenge had 4 tracks. Track 1 employed the standard bicubic downscaling setup, while Tracks 2, 3 and 4 had realistic unknown downgrading operators simulating camera image acquisition pipeline. The operators were learnable through provided pairs of low and high resolution train images. The tracks had 145, 114, 101, and 113 registered participants, resp., and 31 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution.

关键词： Image resolution Degradation Training Runtime computer architecture Testing Tracking

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