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The 2021 Image Similarity Dataset and Challenge [article]

Matthijs Douze and Giorgos Tolias and Ed Pizzi and Zoë Papakipos and Lowik Chanussot and Filip Radenovic and Tomas Jenicek and Maxim Maximov and Laura Leal-Taixé and Ismail Elezi and Ondřej Chum and Cristian Canton Ferrer
2022 arXiv   pre-print
This paper introduces a new benchmark for large-scale image similarity detection. This benchmark is used for the Image Similarity Challenge at NeurIPS'21 (ISC2021).  ...  We expect the DISC21 benchmark to promote image copy detection as an important and challenging computer vision task and refresh the state of the art.  ...  We call the dataset Dataset for ISC 2021, or DISC21.  ... 
arXiv:2106.09672v4 fatcat:cqeu7wkmp5djtac453t5ao5ix4

The 2021 Hotel-ID to Combat Human Trafficking Competition Dataset [article]

Rashmi Kamath, Gregory Rolwes, Samuel Black, Abby Stylianou
2021 arXiv   pre-print
Here, we present the 2021 Hotel-ID dataset to help raise awareness for this problem and generate novel approaches.  ...  The dataset consists of hotel room images that have been crowd-sourced and uploaded through the TraffickCam mobile application.  ...  The dataset and challenge can be found at https: //www.kaggle.com/c/hotel-id-2021-fgvc8.  ... 
arXiv:2106.05746v2 fatcat:pqan3dxsrjeo5a5ruz2g7ikkfq

MultiEarth 2022 – Multimodal Learning for Earth and Environment Workshop and Challenge [article]

Miriam Cha, Kuan Wei Huang, Morgan Schmidt, Gregory Angelides, Mark Hamilton, Sam Goldberg, Armando Cabrera, Phillip Isola, Taylor Perron, Bill Freeman, Yen-Chen Lin, Brandon Swenson (+1 others)
2022 arXiv   pre-print
This paper presents the challenge guidelines, datasets, and evaluation metrics for the three sub-challenges. Our challenge website is available at https://sites.google.com/view/rainforest-challenge.  ...  MultiEarth 2022 will have three sub-challenges: 1) matrix completion, 2) deforestation estimation, and 3) image-to-image translation.  ...  Newey for helping the team labeling the August 2021 time slice data, and Scale AI, for its efforts to label, in a short period of time, the remaining ten time slices considered in this MultiEarth2022.  ... 
arXiv:2204.07649v3 fatcat:j25ualr5zfeqlk6r7qvs6q7fs4

The Herbarium 2021 Half-Earth Challenge Dataset [article]

Riccardo de Lutio, Damon Little, Barbara Ambrose, Serge Belongie
2021 arXiv   pre-print
We present the Herbarium Half-Earth dataset, the largest and most diverse dataset of herbarium specimens to date for automatic taxon recognition.  ...  Furthermore, aggregating multiple datasets is difficult as taxa exist under a multitude of different names and the taxonomy requires alignment to a common reference.  ...  (Gillian Brown, Gordon Guymer, Andrew Franks), Naturalis Biodiversity Center (Jan Wieringa) and for contributing images to this dataset.  ... 
arXiv:2105.13808v1 fatcat:wwa23cvfsbhaxmvy2fzrokxbqy

Perceptual Image Quality Assessment with Transformers [article]

Manri Cheon, Sung-Jun Yoon, Byungyeon Kang, Junwoo Lee
2021 arXiv   pre-print
The proposed IQT was ranked first among 13 participants in the NTIRE 2021 perceptual image quality assessment challenge.  ...  The extracted feature maps are fed into the transformer encoder and decoder in order to compare a reference and distorted images.  ...  participants on testing dataset of the NTIRE 2021 challenge.  ... 
arXiv:2104.14730v2 fatcat:bmfrjkuyungkfau7lvihief5re

ForgeryNet – Face Forgery Analysis Challenge 2021: Methods and Results [article]

Yinan He, Lu Sheng, Jing Shao, Ziwei Liu, Zhaofan Zou, Zhizhi Guo, Shan Jiang, Curitis Sun, Guosheng Zhang, Keyao Wang, Haixiao Yue, Zhibin Hong (+10 others)
2021 arXiv   pre-print
This paper reports methods and results in the ForgeryNet - Face Forgery Analysis Challenge 2021, which employs the ForgeryNet benchmark.  ...  Recently, a mega-scale deep face forgery dataset, ForgeryNet which comprised of 2.9 million images and 221,247 videos has been released.  ...  Acknowledgments.We sincerely thank the codebase from CelebA-Spoof Challenge 4 , especially for the helpful discussions from Yuanhan Zhang.  ... 
arXiv:2112.08325v1 fatcat:rtwj6i7kfrffjawxtutqtgzwuq

The Herbarium 2021 Half–Earth Challenge Dataset and Machine Learning Competition

Riccardo de Lutio, John Y. Park, Kimberly A. Watson, Stefano D'Aronco, Jan D. Wegner, Jan J. Wieringa, Melissa Tulig, Richard L. Pyle, Timothy J. Gallaher, Gillian Brown, Gordon Guymer, Andrew Franks (+6 others)
2022 Frontiers in Plant Science  
We introduce the Herbarium 2021 Half–Earth dataset: the largest and most diverse dataset of herbarium specimen images, to date, for automatic taxon recognition.  ...  We also present the results of the Herbarium 2021 Half–Earth challenge, a competition that was part of the Eighth Workshop on Fine-Grained Visual Categorization (FGVC8) and hosted by Kaggle to encourage  ...  We would also like to thank everyone who entered the Herbarium 2021 Half-Earth Challenge.  ... 
doi:10.3389/fpls.2021.787127 pmid:35178056 pmcid:PMC8846375 fatcat:qunsi3zi3vfbzgo6glmhto5k24

3rd Place: A Global and Local Dual Retrieval Solution to Facebook AI Image Similarity Challenge [article]

Xinlong Sun, Yangyang Qin, Xuyuan Xu, Guoping Gong, Yang Fang, Yexin Wang
2021 arXiv   pre-print
This paper presents our 3rd place solution to the matching track of Image Similarity Challenge (ISC) 2021 organized by Facebook AI.  ...  As a basic task of computer vision, image similarity retrieval is facing the challenge of large-scale data and image copy attacks.  ...  Conclusion In this paper, we proposed a global and local dual recall architecture for the Image Similarity Challenge (ISC) 2021 organized by Facebook AI, and won the 3rd place in matching track.  ... 
arXiv:2112.02373v2 fatcat:7rwuh2fncbgpffubtspus7cuiu

NTIRE 2021 Challenge on Perceptual Image Quality Assessment [article]

Jinjin Gu and Haoming Cai and Chao Dong and Jimmy S. Ren and Yu Qiao and Shuhang Gu and Radu Timofte and Manri Cheon and Sungjun Yoon and Byungyeon Kang and Junwoo Lee and Qing Zhang and Haiyang Guo and Yi Bin and Yuqing Hou and Hengliang Luo and Jingyu Guo and Zirui Wang and Hai Wang and Wenming Yang and Qingyan Bai and Shuwei Shi and Weihao Xia and Mingdeng Cao and Jiahao Wang and Yifan Chen and Yujiu Yang and Yang Li and Tao Zhang and Longtao Feng and Yiting Liao and Junlin Li and William Thong and Jose Costa Pereira and Ales Leonardis and Steven McDonagh and Kele Xu and Lehan Yang and Hengxing Cai and Pengfei Sun and Seyed Mehdi Ayyoubzadeh and Ali Royat and Sid Ahmed Fezza and Dounia Hammou and Wassim Hamidouche and Sewoong Ahn and Gwangjin Yoon and Koki Tsubota and Hiroaki Akutsu and Kiyoharu Aizawa
2021 arXiv   pre-print
In comparison with previous IQA challenges, the training and testing datasets in this challenge include the outputs of perceptual image processing algorithms and the corresponding subjective scores.  ...  This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR  ...  Acknowledgements We thank the NTIRE 2021 sponsors: Huawei, Facebook Reality Labs, Wright Brothers Institute, MediaTek, OPPO and ETH Zurich (Computer Vision Lab).  ... 
arXiv:2105.03072v3 fatcat:mf4q3hvz4jbepgyhkzg7wbylqq

Document Visual Question Answering Challenge 2020 [article]

Minesh Mathew, Ruben Tito, Dimosthenis Karatzas, R. Manmatha, C.V. Jawahar
2021 arXiv   pre-print
The challenge introduces a new problem - Visual Question Answering on document images. The challenge comprised two tasks. The first task concerns with asking questions on a single document image.  ...  This paper presents results of Document Visual Question Answering Challenge organized as part of "Text and Documents in the Deep Learning Era" workshop, in CVPR 2020.  ...  Task 1 -VQA on Document Images Task 1 of the challenge is similar to the typical VQA setting , i.e answer a question asked on an image; here a document image.  ... 
arXiv:2008.08899v2 fatcat:vdkunwqbt5b3rbxq5fooafdp24

PUC Chile team at VQA-Med 2021: approaching VQA as a classification task via fine-tuning a pretrained CNN

Ricardo Schilling, Pablo Messina, Denis Parra, Hans Löbel
2021 Conference and Labs of the Evaluation Forum  
We took a DenseNet121 with its weights pre-trained in ImageNet and fine-tuned it with the VQA-Med 2020 dataset labels to predict the answer.  ...  Our participation was rather simple: we approached the problem as image classification.  ...  Acknowledgments This work was partially funded by ANID -Millennium Science Initiative Program -Code ICN17_002 and by ANID, FONDECYT grant 1191791.  ... 
dblp:conf/clef/SchillingMPL21 fatcat:tdgeipsh7jh2ximy63r4uksbna

Results and findings of the 2021 Image Similarity Challenge [article]

Zoë Papakipos, Giorgos Tolias, Tomas Jenicek, Ed Pizzi, Shuhei Yokoo, Wenhao Wang, Yifan Sun, Weipu Zhang, Yi Yang, Sanjay Addicam, Sergio Manuel Papadakis, Cristian Canton Ferrer (+2 others)
2022 arXiv   pre-print
The 2021 Image Similarity Challenge introduced a dataset to serve as a new benchmark to evaluate recent image copy detection methods. There were 200 participants to the competition.  ...  This paper presents a quantitative and qualitative analysis of the top submissions.  ...  Acknowledgements Thanks to Meta, which funded the competition and the prizes. We thank Driven Data and in particular Greg Lipstein, Jay Qi and Mike Schlauch for organizing the competition.  ... 
arXiv:2202.04007v1 fatcat:5z4pqfkc35gdlhhconzaxhfn3q

Overview of the VQA-Med Task at ImageCLEF 2021: Visual Question Answering and Generation in the Medical Domain

Asma Ben Abacha, Mourad Sarrouti, Dina Demner-Fushman, Sadid A. Hasan, Henning Müller
2021 Conference and Labs of the Evaluation Forum  
Thirteen teams participated in VQA-Med 2021 and submitted a total of 75 runs. The best teams achieved a BLEU score of 0.416 in the VQA task and 0.383 in the VQG task.  ...  VQA-Med 2021 includes a task on Visual Question Answering (VQA), where participants are tasked with answering questions from the visual content of radiology images, and a second task on Visual Question  ...  Acknowledgments This work was partially supported by the intramural research program at the U.S. National Library of Medicine, National Institutes of Health.  ... 
dblp:conf/clef/AbachaSDHM21 fatcat:76fzt5dmcfentdqkhturjsci4y

2nd Place Solution to Facebook AI Image Similarity Challenge Matching Track [article]

SeungKee Jeon
2021 arXiv   pre-print
This paper presents the 2nd place solution to the Facebook AI Image Similarity Challenge : Matching Track on DrivenData.  ...  The main breaktrough comes from concatenating query and reference image to form as one image and asking ViT to directly predict from the image if query image used reference image.  ...  1.Introduction Facebook AI Image Similarity Challenge[1] aims to detect if reference(copyrighted) image is used in the given query image.  ... 
arXiv:2111.09113v1 fatcat:gvwk6i6skfd4jmv64l2uprd2ui

Foot Ulcer Segmentation Challenge 2021 [article]

Chuanbo Wang, Behrouz Rostami, Jeffrey Niezgoda, Sandeep Gopalakrishnan, Zeyun Yu
2021 Zenodo  
We provide this dataset to the challenge and aim at encouraging and supporting the development of new solutions for the automated and accurate segmentation of foot ulcers from natural images taken in common  ...  With the collaboration between the University of Wisconsin-Milwaukee and Advancing the Zenith of Healthcare Wound and Vascular Center, we build a dataset containing over 1000 foot ulcer images professionally  ...  We encourage participating teams to publish codes and files on GitHub. Foot Ulcer Segmentation Challenge 2021 and MICCAI 2021 should be mentioned in the repository.  ... 
doi:10.5281/zenodo.4575313 fatcat:e2gwi6yp2rbcjj7qwv5epwvsfi
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