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Separating lungs in CT scans for improved COVID19 detection

Separating lungs in CT scans for improved COVID19 detection

引用

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

作者： Robert Turnbull Simon Mutch Melbourne Data Analytics Platform University of Melbourne Grattan St. Carlton VIC Australia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper outlines our submission for the 4th COV19D competition as part of the ‘Domain adaptation, Explainability, Fairness in AI for Medical Image Analysis’ (DEFAI-MIA) workshop at the computer vision and pattern recognition conference (CVPR). The competition consists of two challenges. The first is to train a classifier to detect the presence of COVID-19 from over one thousand CT scans from the COV19-CT-DB database. The second challenge is to perform domain adaptation by taking the dataset from Challenge 1 and adding a small number of scans (some annotated and other not) for a different distribution. We preprocessed the CT scans to segment the lungs, and output volumes with the lungs individually and together. We then trained 3D ResNet and Swin Transformer models on these inputs. We annotated the unlabeled CT scans using an ensemble of these models and chose the high-confidence predictions as pseudo-labels for fine-tuning. This achieved the winning macro F1 score of 94.89% for Challenge 1 of the competition. It also achieved a second-best macro F1 score of 77.21% for Challenge 2.

关键词： COVID-19 Training computer vision Three-dimensional displays Computed tomography conferences Lung

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Domain adaptation, Explainability & Fairness in AI for Medical Image Analysis: Diagnosis of COVID-19 based on 3-D Chest CT-scans

Domain adaptation, Explainability & Fairness in AI for Medic...

引用

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

作者： Dimitrios Kollias Anastasios Arsenos Stefanos Kollias Queen Mary University of London UK National Technical University of Athens Greece National Infrastructures for Research and Technology Greece

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The paper presents the DEF-AI-MIA COV19D Competition, which is organized in the framework of the ’Domain adaptation, Explainability, Fairness in AI for Medical Image Analysis (DEF-AI-MIA)’ workshop of the 2024 computer vision and pattern recognition (CVPR) conference. The Competition is the 4th in the series, following the first three Competitions held in the framework of ICCV 2021, ECCV 2022 and ICASSP 2023 International conferences respectively. It includes two Challenges on: i) Covid-19 Detection and ii) Covid-19 Domain Adaptation. The Competition use data from COV19-CT-DB database, which is described in the paper and includes a large number of chest CT scan series. Each chest CT scan series consists of a sequence of 2-D CT slices, the number of which is between 50 and 700. Training, validation and test datasets have been extracted from COV19-CT-DB and provided to the participants in both Challenges. The paper presents the baseline models used in the Challenges and the performance which was obtained respectively, together with the best corresponding performances of the methods submitted and evaluated in the Challenges.

关键词： COVID-19 Training computer vision Image analysis Databases Computed tomography conferences

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Technical Report of NICE Challenge at CVPR 2024: Caption Re-ranking Evaluation Using Ensembled CLIP and Consensus Scores

Technical Report of NICE Challenge at CVPR 2024: Caption Re-...

引用

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

作者： Kiyoon Jeong Woojun Lee Woongchan Nam Minjeong Ma Pilsung Kang School of Industrial and Management Engineering Korea University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This report presents the ECO (Ensembled Clip score and cOnsensus score) pipeline from team DSBALAB, which is a new framework used to evaluate and rank captions for a given image. ECO selects the most accurate caption describing image. It is made possible by combining an Ensembled CLIP score, which considers the semantic alignment between the image and captions, with a Consensus score that accounts for the essentialness of the captions. Using this framework, we achieved notable success in the CVPR 2024 workshop Challenge on Caption Re-ranking Evaluation at the New Frontiers for Zero-Shot Image Captioning Evaluation (NICE). Specifically, we secured third place based on the CIDEr metric, second in both the SPICE and METEOR metrics, and first in the ROUGE-L and all BLEU Score metrics. The code and configuration for the ECO framework are available at https://***/DSBA-Lab/ECO.

关键词： Measurement Training computer vision conferences Semantics Pipelines Writing

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The 6th Affective Behavior Analysis in-the-wild (ABAW) Competition

The 6th Affective Behavior Analysis in-the-wild (ABAW) Compe...

引用

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

作者： Dimitrios Kollias Panagiotis Tzirakis Alan Cowen Stefanos Zafeiriou Irene Kotsia Alice Baird Chris Gagne Chunchang Shao Guanyu Hu Queen Mary University of London UK Hume AI USA Imperial College London UK Cogitat UK Xi’an Jiaotong University China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper describes the 6th Affective Behavior Analysis in-the-wild (ABAW) Competition, which is part of the respective workshop held in conjunction with IEEE CVPR 2024. The 6th ABAW Competition addresses contemporary challenges in understanding human emotions and behaviors, crucial for the development of human-centered technologies. In more detail, the Competition focuses on affect related benchmarking tasks and comprises of five sub-challenges: i) Valence-Arousal Estimation (the target is to estimate two continuous affect dimensions, valence and arousal), ii) Expression recognition (the target is to recognise between the mutually exclusive classes of the 7 basic expressions and ’other’), iii) Action Unit Detection (the target is to detect 12 action units), iv) Compound Expression recognition (the target is to recognise between the 7 mutually exclusive compound expression classes), and v) Emotional Mimicry Intensity Estimation (the target is to estimate six continuous emotion dimensions). In the paper, we present these Challenges, describe their respective datasets and challenge protocols (we outline the evaluation metrics) and present the baseline systems and top performing teams’ per Challenge, as well as their obtained performance. More information for the Competition can be found in: https://***/6th.

关键词： Measurement Emotion recognition computer vision Protocols Target recognition Databases conferences

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Thermal Image Super-Resolution Challenge Results - PBVS 2024

Thermal Image Super-Resolution Challenge Results - PBVS 2024

引用

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

作者： Rafael E. Rivadeneira Angel D. Sappa Chenyang Wang Junjun Jiang Zhiwei Zhong Peilin Chen Shiqi Wang Escuela Superior Politécnica del Litoral ESPOL Guayaquil Ecuador Computer Vision Center Campus UAB Barcelona Spain

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper outlines the advancements and results of the Fifth Thermal Image Super-Resolution challenge, hosted at the Perception Beyond the Visible Spectrum CVPR 2024 workshop. The challenge employed a novel benchmark cross-spectral dataset consisting of 1000 thermal images, each paired with its corresponding registered RGB image. The challenge featured two tracks: Track-1 focused on Single Thermal Image Super-Resolution with an ×8 upscale factor, while Track-2 extended its evaluation to include both ×8 and ×16 scaling factors, utilizing high-resolution RGB images to guide the super-resolution process for low-resolution thermal images. The participation of over 175 teams highlights the research community’s strong engagement and dedication to enhancing image resolution techniques across both single and cross-spectral methodologies. This year’s challenge sets new benchmarks and provides valuable insights into future directions for research in thermal image super-resolution.

关键词： Image quality computer vision Thermal factors conferences Superresolution Benchmark testing Transformers

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NTIRE 2021 Multi-modal Aerial View Object Classification Challenge

NTIRE 2021 Multi-modal Aerial View Object Classification Cha...

引用

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

作者： Liu, Jerrick Inkawhich, Nathan Nina, Oliver Timofte, Radu Duan, Yuru Li, Gongzhe Geng, Xueli Cai, Huanqia Air Force Res Lab Albuquerque NM 87117 USA Univ Illinois Urbana IL 61801 USA Duke Univ Durham NC 27706 USA Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Northwestern Polytech Univ Changan Campus Xian Shaanxi Peoples R China Beihang Univ Beijing Peoples R China Xidian Univ Key Lab Intelligent Percept & Image Understanding Xian Peoples R China Dengzhuang South Rd Beijing Peoples R China

ISBN: (纸本)9781665448994

In this paper, we introduce the first Challenge on Multi-modal Aerial View Object Classification (MAVOC) in conjunction with the NTIRE 2021 workshop at CVPR. This challenge is composed of two different tracks using EO and SAR imagery. Both EO and SAR sensors possess different advantages and drawbacks. The purpose of this competition is to analyze how to use both sets of sensory information in complementary ways. We discuss the top methods submitted for this competition and evaluate their results on our blind test set. Our challenge results show significant improvement of more than 15% accuracy from our current baselines for each track of the competition.

关键词： Image sensors conferences Radar tracking Radar polarimetry Sensors pattern recognition Task analysis

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Wide Receptive Field and Channel Attention Network for JPEG Compressed Image Deblurring

Wide Receptive Field and Channel Attention Network for JPEG ...

引用

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

作者： Lee, Donghyeon Lee, Chulhee Kim, Taesung Samsung Elect Hwaseong South Korea Sunmoon Univ Asan South Korea

ISBN: (纸本)9781665448994

A motion blurred image stored in the joint photographic experts group (JPEG) image compression format contains both motion blur and JPEG artifacts. Therefore, it is very difficult to restore the original image from a blurred and JPEG-compressed image. To address this problem, this paper proposes two methods: a wide receptive field and channel attention network (WRCAN), and JPEG autoencoder loss. First, the WRCAN utilizes a large receptive field and considers the interdependencies among channels of a feature map. Second, the proposed JPEG auto-encoder loss enables the WRCAN to learn prior knowledge of JPFG compression artifacts such that the proposed WRCAN can effectively restore the original image from JPEG-compressed images. The proposed methods are evaluated on the JPEG-compressed REDS dataset by participating in the NTIRE 2021 workshop challenges on Image Deblurring Track 2 JPEG artifacts. The WRCAN trained with the proposed loss ranked third with an output of 29.60dB on the REDS test set, indicating that the proposed methods provide state-of-the-art results. The source codes, model, and data are available at https://***/dhyeonlee/WRCAN-PyTorch.

关键词： computer vision Image coding Convolution conferences Transform coding computer architecture Data models

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Thermal Image Super-Resolution Challenge Results - PBVS 2023

Thermal Image Super-Resolution Challenge Results - PBVS 2023

引用

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

作者： Rafael E. Rivadeneira Angel D. Sappa Boris X. Vintimilla Chenyang Wang Junjun Jiang Xianming Liu Zhiwei Zhong Dai Bin Li Ruodi Li Shengye Escuela Superior Politécnica del Litoral ESPOL Guayaquil Ecuador Computer Vision Center Campus UAB Bellaterra Barcelona Spain

This paper presents the results of two tracks from the fourth Thermal Image Super-Resolution (TISR) challenge, held at the Perception Beyond the Visible Spectrum (PBVS) 2023 workshop. Track-1 uses the same thermal image dataset as previous challenges, with 951 training images and 50 validation images at each resolution. In this track, two evaluations were conducted: the first consists of generating a SR image from a HR thermal noisy image downsampled by four, and the second consists of generating a SR image from a mid-resolution image and compare it with its semi-registered HR image (acquired with another camera). The results of Track-1 outperformed those from last year’s challenge. On the other hand, Track-2 uses a new acquired dataset consisting of 160 registered visible and thermal images of the same scenario for training and 30 validation images. This year, more than 150 teams participated in the challenge tracks, demonstrating the community’s ongoing interest in this topic.

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Language-guided Multi-modal Emotional Mimicry Intensity Estimation

Language-guided Multi-modal Emotional Mimicry Intensity Esti...

引用

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

作者： Feng Qiu Wei Zhang Chen Liu Lincheng Li Heming Du Tianchen Guo Xin Yu Netease Fuxi AI Lab The University of Queensland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Emotional Mimicry Intensity (EMI) estimation aims to identify the intensity of mimicry exhibited by individuals in response to observed emotions. The challenge in EMI estimation lies in discerning nuanced facial expression cues on mimicry behaviors based on the seed video and the text instructions. In this paper, we propose a multi-modal EMI estimation framework by leveraging visual, auditory, and textual modalities to capture a comprehensive emotional profile. We first extract representations for each modality separately and then fuse the modality-specific representations via a Temporal Segment Network, optimizing for temporal coherence and emotional context. Furthermore, we find that participants demonstrate notable proficiency in mimicking text instructions, yet exhibit less effectiveness in replicating facial expressions and vocal tones. In light of this, we design a contrastive learning mechanism to refine the extracted feature based on textual guidance. By doing so, features derived from similar text instructions are closely aligned, enhancing the estimation of emotional mimicry intensity by leveraging the dominant textual modality. Experiments conducted on the Hume-Vidmimic2 dataset illustrate the effectiveness of our framework in EMI estimation. Our framework is recognized as the leading solution in the Emotional Mimicry Intensity (EMI) Estimation Challenge at the 6th workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW). More information for the Competition can be found in: 6th ABAW.

关键词： Visualization Emotion recognition computer vision Fuses conferences Electromagnetic interference Estimation

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NTIRE 2023 Image Shadow Removal Challenge Report

NTIRE 2023 Image Shadow Removal Challenge Report

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China

ISBN: (纸本)9798350302493

This work reviews the results of the NTIRE 2023 Challenge on Image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods. © 2023 IEEE.

关键词： Data acquisition

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