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Thermal Image Super-Resolution Challenge Results - PBVS 2022

Thermal Image Super-Resolution Challenge Results - PBVS 2022

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 Jin Kim Dogun Kim Zhihao Li Yingchun Jian Bo Yan Leilei Cao Fengliang Qi Hongbin Wang Rongyuan Wu Lingchen Sun Yongqiang Zhao Lin Li Kai Wang Yicheng Wang Xuanming Zhang Huiyuan Wei Chonghua Lv Qigong Sun Xiaolin Tian Zhuang Jia Jiakui Hu Chenyang Wang Zhiwei Zhong Xianming Liu Junjun Jiang Escuela Superior Polit&#x00E9 cnica del Litoral ESPOL Guayaquil Ecuador Computer Vision Center Campus UAB Barcelona Spain

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper presents results from the third Thermal Image Super-Resolution (TISR) challenge organized in the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop. The challenge uses the same thermal image dataset as the first two challenges, with 951 training images and 50 validation images at each resolution. A set of 20 images was kept aside for testing. The evaluation tasks were to measure the PSNR and SSIM between the SR image and the ground truth (HR thermal noisy image downsampled by four), and also to measure the PSNR and SSIM between the SR image and the semi-registered HR image (acquired with another camera). The results outperformed those from last year’s challenge, improving both evaluation metrics. This year, almost 100 teams participants registered for the challenge, showing the community’s interest in this hot topic.

关键词： Training Deep learning conferences Superresolution Transformers Particle measurements pattern recognition

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Semi-Supervised Hyperspectral Object Detection Challenge Results - PBVS 2022

Semi-Supervised Hyperspectral Object Detection Challenge Res...

引用

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

作者： Aneesh Rangnekar Zachary Mulhollan Anthony Vodacek Matthew Hoffman Angel Sappa Erik Blasch Jun Yu Liwen Zhang Shenshen Du Hao Chang Keda Lu Zhong Zhang Fang Gao Ye Yu Feng Shuang Lei Wang Qiang Ling Pranjay Shyam Kuk-Jin Yoon Kyung-Soo Kim Rochester Institute of Technology Rochester NY USA ESPOL Polytechnic University Guayaquil Ecuador Computer Vision Center Campus UAB Barcelona Spain US Air Force Research Lab Rome NY

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper summarizes the top contributions to the first semi-supervised hyperspectral object detection (SSHOD) challenge, which was organized as a part of the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop at the computer vision and pattern recognition (CVPR) conference. The SSHODC challenge is a first-of-its-kind hyperspectral dataset with temporally contiguous frames collected from a university rooftop observing a 4-way vehicle intersection over a period of three days. The dataset contains a total of 2890 frames, captured at an average resolution of 1600 × 192 pixels, with 51 hyperspectral bands from 400nm to 900nm. SSHOD challenge uses 989 images as the training set, 605 images as validation set and 1296 images as the evaluation (test) set. Each set was acquired on a different day to maximize the variance in weather conditions. Labels are provided for 10% of the annotated data, hence formulating a semi-supervised learning task for the participants which is evaluated in terms of average precision over the entire set of classes, as well as individual moving object classes: namely vehicle, bus and bike. The challenge received participation registration from 38 individuals, with 8 participating in the validation phase and 3 participating in the test phase. This paper describes the dataset acquisition, with challenge formulation, proposed methods and qualitative and quantitative results.

关键词： Training computer vision conferences Training data Object detection Semisupervised learning Transformers

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Efficient CNN Architecture for Multi-modal Aerial View Object Classification

Efficient CNN Architecture for Multi-modal Aerial View Objec...

引用

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

作者： Casian Miron Alexandru Pasarica Radu Timofte MCC Resources S.R.L. "Gheorghe Asachi" Technical University Iasi Romania

The NTIRE 2021 workshop features a Multi-modal Aerial View Object Classification Challenge. Its focus is on multi-sensor imagery classification in order to improve the performance of automatic target recognition (ATR) systems. In this paper we describe our entry in this challenge, a method focused on efficiency and low computational time, while maintaining a high level of accuracy. The method is a convolutional neural network with 11 convolutions, 1 max pooling layers and 3 residual blocks which has a total of 373.130 parameters. The method ranks 3rd in the Track 2 (SAR+EO) of the challenge.

关键词： Training computer vision Target recognition conferences Systems architecture Graphics processing units computer architecture

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Thermal Image Super-Resolution Challenge - PBVS 2020

Thermal Image Super-Resolution Challenge - PBVS 2020

引用

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

作者： Rivadeneira, Rafael E. Sappa, Angel D. Vintimilla, Boris X. Guo, Lin Hou, Jiankun Mehri, Armin Behjati Ardakani, Parichehr Patel, Heena Chudasama, Vishal Prajapati, Kalpesh Upla, Kishor P. Ramachandra, Raghavendra Raja, Kiran Busch, Christoph Almasri, Feras Debeir, Olivier Nathan, Sabari Kansal, Priya Gutierrez, Nolan Mojra, Bardia Beksi, William J. ESPOL Escuela Super Politecn Litoral CIDIS Fac Ingn Elect & Comp 30-5 Via PerimetralPOB 09-01-5863 Guayaquil Ecuador Comp Vis Ctr Campus UAB Barcelona 08193 Spain Oklahoma State Univ Stillwater OK 74078 USA SVNIT Surat India NTNU Gjovik Norway Univ Libre Bruxelles Brussels Belgium Couger Inc Tokyo Japan Univ Texas Arlington Dept Comp Sci & Engn Robot Vis Lab Arlington TX 76019 USA

ISBN: (纸本)9781728193601

This paper summarizes the top contributions to the first challenge on thermal image super-resolution (TISR), which was organized as part of the Perception Beyond the Visible Spectrum (PBVS) 2020 workshop. In this challenge, a novel thermal image dataset is considered together with state-of-the-art approaches evaluated under a common framework. The dataset used in the challenge consists of 1021 thermal images, obtained from three distinct thermal cameras at different resolutions (low-resolution, mid-resolution, and high-resolution), resulting in a total of 3063 thermal images. From each resolution, 951 images are used for training and 50 for testing while the 20 remaining images are used for two proposed evaluations. The first evaluation consists of downsampling the low-resolution, mid-resolution, and high-resolution thermal images by x2, x3 and x4 respectively, and comparing their super-resolution results with the corresponding ground truth images. The second evaluation is comprised of obtaining the x2 super-resolution from a given mid-resolution thermal image and comparing it with the corresponding semi-registered high-resolution thermal image. Out of 51 registered participants, 6 teams reached the final validation phase.

关键词： computer vision

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NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods ...

引用

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

作者： Eduardo Pé rez-Pellitero Sibi Catley-Chandar Richard Shaw Aleš Leonardis Radu Timofte Zexin Zhang Cen Liu Yunbo Peng Yue Lin Gaocheng Yu Jin Zhang Zhe Ma Hongbin Wang Xiangyu Chen Xintao Wang Haiwei Wu Lin Liu Chao Dong Jiantao Zhou Qingsen Yan Song Zhang Weiye Chen Yuhang Liu Zhen Zhang Yanning Zhang Javen Qinfeng Shi Dong Gong Dan Zhu Mengdi Sun Guannan Chen Yang Hu Haowei Li Baozhu Zou Zhen Liu Wenjie Lin Ting Jiang Chengzhi Jiang Xinpeng Li Mingyan Han Haoqiang Fan Jian Sun Shuaicheng Liu Juan Marí n-Vega Michael Sloth Peter Schneider-Kamp Richard Rö ttger Chunyang Li Long Bao Gang He Ziyao Xu Li Xu Gen Zhan Ming Sun Xing Wen Junlin Li Jinjing Li Chenghua Li Ruipeng Gang Fangya Li Chenming Liu Shuang Feng Fei Lei Rui Liu Junxiang Ruan Tianhong Dai Wei Li Zhan Lu Hengyan Liu Peian Huang Guangyu Ren Yonglin Luo Chang Liu Qiang Tu Sai Ma Yizhen Cao Steven Tel Barthelemy Heyrman Dominique Ginhac Chul Lee Gahyeon Kim Seonghyun Park An Gia Vien Truong Thanh Nhat Mai Howoon Yoon Tu Vo Alexander Holston Sheir Zaheer Chan Y. Park Huawei Noah&#x2019 s Ark Laboratory ETH Z&#x00FC rich University of W&#x00FC rzburg

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2022. This manuscript focuses on the competition set-up, datasets, the proposed methods and their results. The challenge aims at estimating an HDR image from multiple respective low dynamic range (LDR) observations, which might suffer from under-or over-exposed regions and different sources of noise. The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i.e. solutions can not exceed a given number of operations). In Track 2, participants are asked to minimize the complexity of their solutions while imposing a constraint on fidelity scores (i.e. solutions are required to obtain a higher fidelity score than the prescribed baseline). Both tracks use the same data and metrics: Fidelity is measured by means of PSNR with respect to a ground-truth HDR image (computed both directly and with a canonical tonemapping operation), while complexity metrics include the number of Multiply-Accumulate (MAC) operations and runtime (in seconds).

关键词： computer vision Runtime conferences Imaging Dynamic range Particle measurements Market research

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WiCV 2019: The Sixth Women In computer vision workshop 32

WiCV 2019: The Sixth Women In Computer Vision Workshop

引用

32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Amerini, Irene Balashova, Elena Ebrahimi, Sayna Leonard, Kathryn Nagrani, Arsha Salvador, Amaia Univ Florence Florence Italy Princeton Univ Princeton NJ 08544 USA Univ Calif Berkeley Berkeley CA USA Occident Coll Los Angeles CA USA Univ Oxford Oxford England Univ Politen Catalunya Barcelona Spain

ISBN: (纸本)9781728125060

In this paper we present the Women in computer vision workshop - WiCV 2019, organized in conjunction with CVPR 2019. This event is meant for increasing the visibility and inclusion of women researchers in computer vision field. computer vision and machine learning have made incredible progress over the past years, but the number of female researchers is still low both in the academia and in the industry. WiCV is organized especially for this reason: to raise visibility of female researchers, to increase collaborations between them, and to provide mentorship to female junior researchers in the field. In this paper, we present a report of trends over the past years, along with a summary of statistics regarding presenters, attendees, and sponsorship for the current workshop.

关键词： computer vision

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Low Bitrate Image Compression with Discretized Gaussian Mixture Likelihoods

Low Bitrate Image Compression with Discretized Gaussian Mixt...

引用

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

作者： Zhengxue Cheng Heming Sun Jiro Katto Department of Computer Science and Communication Engineering Waseda University Tokyo Japan Waseda Research Institute for Science and Engineering Tokyo Japan

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

In this paper, we provide a detailed description on our submitted method Kattolab to workshop and Challenge on Learned Image Compression (CLIC) 2020. Our method mainly incorporates discretized Gaussian Mixture Likeli-hoods to previous state-of-the-art learned compression algorithms. Besides, we also describes the acceleration strategies and bit optimization with the low-rate constraint. Experimental results have demonstrated that our approach Kattolab achieves 0.9761 in terms of MS-SSIM at the rate constraint of 0.15 bpp during the validation phase.

关键词： Image coding Convolution Entropy Training Decoding pattern recognition Bit rate

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WiCV 2018: The Fourth Women In computer vision workshop 31

WiCV 2018: The Fourth Women In Computer Vision Workshop

引用

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

作者： Demir, Ilke Bazazian, Dena Romero, Adriana Sharmanska, Viktoriia Tchapmi, Lyne P. Facebook Menlo Pk CA 94025 USA Comp Vis Ctr Barcelona Spain Facebook AI Res Montreal PQ Canada Imperial Coll London London England Stanford Univ Stanford CA 94305 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We present WiCV 2018 - Women in computer vision workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration, and to provide mentorship and give opportunities to female-identifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations.

关键词： conferences computer vision Industries Object recognition Engineering profession Collaboration Machine learning

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WiCV 2019: The Sixth Women In computer vision workshop

WiCV 2019: The Sixth Women In Computer Vision Workshop

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

作者： Irene Amerini Elena Balashova Sayna Ebrahimi Kathryn Leonard Arsha Nagrani Amaia Salvador University of Florence Princeton University UC Berkeley Occidental College University of Oxford Universitat Politcnica de Catalunya

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

关键词： conferences computer vision Machine learning Engineering profession Facebook Buildings

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Disguised Faces in the Wild 31

Disguised Faces in the Wild

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

作者： Kushwaha, Vineet Singh, Maneet Singh, Richa Vatsa, Mayank Ratha, Nalini Chellappa, Rama IIIT Delhi Delhi India IBM TJ Watson Res Ctr Ossining NY USA Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781538661000

Existing research in the field of face recognition with variations due to disguises focuses primarily on images captured in controlled settings. Limited research has been performed on images captured in unconstrained environments, primarily due to the lack of corresponding disguised face datasets. In order to overcome this limitation, this work presents a novel Disguised Faces in the Wild (DFW) dataset, consisting of over 11,000 images for understanding and pushing the current state-of-the-art for disguised face recognition. To the best of our knowledge, DFW is a first-of-a-kind dataset containing images pertaining to both obfuscation and impersonation for understanding the effect of disguise variations. A major portion of the dataset has been collected from the Internet, thereby encompassing a wide variety of disguise accessories and variations across other covariates. As part of CVPR2018, a competition and workshop are organized to facilitate research in this direction. This paper presents a description of the dataset, the baseline protocols and performance, along with the phase-I results of the competition.

关键词： computer vision pattern recognition conferences

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