Camera traps enable the automatic collection of large quantities of image data. Ecologists use camera traps to monitor animal populations all over the world. In order to estimate the abundance of a species from camera trap data, ecologists need to know not just which species were seen, but also how many individuals of each species were seen. Object detection techniques can be used to find the number of individuals in each image. However, since camera traps collect images in motion-triggered bursts, simply adding up the number of detections over all frames is likely to lead to an incorrect estimate. Overcoming these obstacles may require incorporating spatio-temporal reasoning or individual re-identification in addition to traditional species detection and classification.
We have prepared a challenge where the training data and test data are from different cameras spread across the globe. The set of species seen in each camera overlap, but are not identical. The challenge is to classify species and count individual animals across sequences in the test cameras.
California Institute of Technology（加州理工学院）；Google（谷歌公司）
Camera traps enable the automatic collection of large quantities of image data. Biologists all over the world use camera traps to monitor animal populations. We have recently been making strides towards automatic species classification in camera trap images. However, as we try to expand the geographic scope of these models we are faced with an interesting question: how do we train models that perform well on new (unseen during training) camera trap locations? Can we leverage data from other modalities, such as citizen science data and remote sensing data? In order to tackle this problem, we ha…
Camera traps are a valuable tool for studying biodiversity, but research using this data is limited by the speed of human annotation. With the vast amounts of data now available it is imperative that we develop automatic solutions for annotating camera trap data in order to allow this research to scale. A promising approach is based on deep networks trained on human-annotated images. We provide a challenge dataset to explore whether such solutions generalize to novel locations, since systems that are trained once and may be deployed to operate automatically in new locations would be most usefu…
Camera Traps (or Wild Cams) enable the automatic collection of large quantities of image data. Biologists all over the world use camera traps to monitor biodiversity and population density of animal species. The computer vision community has been making strides towards automating the species classification challenge in camera traps, but as we try to expand the scope of these models from specific regions where we have collected training data to different areas we are faced with an interesting problem: how do you classify a species in a new region that you may not have seen in previous training …
Automatic species classification in camera traps would greatly help the biodiversity monitoring and species analysis in the earth. In order to accelerate the development of automatic species classification task, "Microsoft AI for Earth" have prepared a challenge in FGVC6 workshop at CVPR 2019, which called "iWildCam 2019 competition". In this work, we propose an efficient method for categorizing animals in the wild. We transfer the state-of-the-art ImagaNet pretrained models to the problem. To improve the generalization and robustness of the model, we utilize efficient image augmentation and r…
National Internet Emergency Center(CNCERT)（国家互联网应急中心）；Baidu（百度）
Simi-Supervised Recognition Challenge-FGVC7 is a challenging fine-grained recognition competition. One of the difficulties of this competition is how to use unlabeled data. We adopted pseudo-tag data mining to increase the amount of training data. The other one is how to identify similar birds with a very small difference, especially those have a relatively tiny main-body in examples. We combined generic image recognition and fine-grained image recognition method to solve the problem. All generic image recognition models were training using PaddleClas . Using the combination of two different w…
SDFR: Synthetic Data for Face Recognition Competition
Idiap Research Institute（伊迪亚普研究所）；École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；Université de Lausanne (UNIL)（洛桑大学）；University of Bologna（博洛尼亚大学）；Fraunhofer Institute for Computer Graphics Research (IGD)（弗劳恩霍夫计算机图形研究所）；TU Darmstadt（达姆施塔特工业大学）；Universidad de Alicante（阿利坎特大学）；Facephi Biometria SA（Facephi生物识别公司）；Federal University of Paraná (UFPR)（巴拉那联邦大学）；Federal Institute of Mato Grosso (IFMT)（马托格罗索联邦学院）；unico - idTech（unico - 身份技术公司）；Universidad Autonoma de Madrid (UAM)（马德里自治大学）；Università degli Studi di Cagliari (UNICA)（卡利亚里大学）
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Institut de Recherche et d’Histoire des Textes (Centre National de la Recherche Scientifique - UPR841)（文本研究与历史研究所（法国国家科学研究中心-UPR841））
This competition succeeds upon a line of competitions for writer and style analysis of historical document images. In particular, we investigate the performance of large-scale retrieval of historical document fragments in terms of style and writer identification. The analysis of historic fragments is a difficult challenge commonly solved by trained humanists. In comparison to previous competitions, we make the results more meaningful by addressing the issue of sample granularity and moving from writer to page fragment retrieval. The two approaches, style and author identification, provide info…
The Swiss AI Lab, IDSIA, University of Lugano (USI) & SUPSI（瑞士人工智能实验室 IDSIA，卢加诺大学 (USI) 与南瑞士应用科学与艺术大学 (SUPSI)）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学 (KAUST)）
We share our experience with the recently released WILDS benchmark, a collection of ten datasets dedicated to developing models and training strategies which are robust to domain shifts. Several experiments yield a couple of critical observations which we believe are of general interest for any future work on WILDS. Our study focuses on two datasets: iWildCam and FMoW. We show that (1) Conducting separate cross-validation for each evaluation metric is crucial for both datasets, (2) A weak correlation between validation and test performance might make model development difficult for iWildCam, (…
Fraunhofer Institute for Computer Graphics Research IGD（弗劳恩霍夫计算机图形学研究所）；TU Darmstadt（达姆施塔特工业大学）；Norwegian University of Science and Technology（挪威科技大学）；TYAI；VICOMTECH；University of the Basque Country（巴斯克大学）；Istanbul Technical University（伊斯坦布尔理工大学）；ACES, Inc（ACES公司）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
This paper presents a summary of the Masked Face Recognition Competitions (MFR) held within the 2021 International Joint Conference on Biometrics (IJCB 2021). The competition attracted a total of 10 participating teams with valid submissions. The affiliations of these teams are diverse and associated with academia and industry in nine different countries. These teams successfully submitted 18 valid solutions. The competition is designed to motivate solutions aiming at enhancing the face recognition accuracy of masked faces. Moreover, the competition considered the deployability of the proposed…
For computers to recognize human emotions, expression classification is an equally important problem in the human-computer interaction area. In the 3rd Affective Behavior Analysis In-The-Wild competition, the task of expression classification includes eight classes with six basic expressions of human faces from videos. In this paper, we employ a transformer mechanism to encode the robust representation from the backbone. Fusion of the robust representations plays an important role in the expression classification task. Our approach achieves 30.35\% and 28.60\% for the $F_1$ score on the valida…
INESC TEC（国家科学与工程计算技术研究所（INESC TEC））；University of Porto（波尔图大学）；Fraunhofer Institute for Computer Graphics Research IGD（弗劳恩霍夫计算机图形学研究所IGD）；TU Darmstadt（达姆施塔特工业大学）；CDTA Centre de Développement des Technologies Avancées（先进技术发展中心CDTA）；ESI Ecole Nationale Supérieure d’Informatique（国立高等计算机学院ESI）；SiMiT Lab（SiMiT实验室）
This work summarizes the IJCB Occluded Face Recognition Competition 2022 (IJCB-OCFR-2022) embraced by the 2022 International Joint Conference on Biometrics (IJCB 2022). OCFR-2022 attracted a total of 3 participating teams, from academia. Eventually, six valid submissions were submitted and then evaluated by the organizers. The competition was held to address the challenge of face recognition in the presence of severe face occlusions. The participants were free to use any training data and the testing data was built by the organisers by synthetically occluding parts of the face images using a w…
University of Sussex（萨塞克斯大学）；Imperial College London（伦敦帝国学院）；BCAM Severo Ochoa Strategic Lab on Trustworthy Machine Learning（BCAM塞韦罗·奥乔亚可信机器学习战略实验室）；Monash University, Indonesia（莫纳什大学印度尼西亚校区）
Machine learning classifiers are typically trained to minimise the average error across a dataset. Unfortunately, in practice, this process often exploits spurious correlations caused by subgroup imbalance within the training data, resulting in high average performance but highly variable performance across subgroups. Recent work to address this problem proposes model patching with CAMEL. This previous approach uses generative adversarial networks to perform intra-class inter-subgroup data augmentations, requiring (a) the training of a number of computationally expensive models and (b) suffici…
Huazhong University of Science and Technology（华中科技大学）；Megvii Technology Inc.（旷视科技有限公司）；Cornell University（康奈尔大学）；Cornell Tech（康奈尔理工学院）；Nanyang Technological University（南洋理工大学）
Chinese is the most widely used language in the world. Algorithms that read Chinese text in natural images facilitate applications of various kinds. Despite the large potential value, datasets and competitions in the past primarily focus on English, which bares very different characteristics than Chinese. This report introduces RCTW, a new competition that focuses on Chinese text reading. The competition features a large-scale dataset with 12,263 annotated images. Two tasks, namely text localization and end-to-end recognition, are set up. The competition took place from January 20 to May 31, 2…
Advanced Concepts Team of the European Space Agency（欧洲空间局先进概念团队）；Wisk Aero LLC（Wisk Aero有限责任公司）；Stanford University（斯坦福大学）
Reliable pose estimation of uncooperative satellites is a key technology for enabling future on-orbit servicing and debris removal missions. The Kelvins Satellite Pose Estimation Challenge aims at evaluating and comparing monocular vision-based approaches and pushing the state-of-the-art on this problem. This work is based on the Satellite Pose Estimation Dataset, the first publicly available machine learning set of synthetic and real spacecraft imageries. The choice of dataset reflects one of the unique challenges associated with spaceborne computer vision tasks, namely the lack of spaceborne…
University of California, Berkeley（加州大学伯克利分校）；National University of Singapore（新加坡国立大学）；Meta；Tencent Holdings Ltd and Tsinghua University（腾讯控股有限公司和清华大学）
University of Edinburgh（爱丁堡大学）；Nanyang Technological University（南洋理工大学）；Shanghai AI Laboratory（上海人工智能实验室）
Generative models for audio-conditioned dance motion synthesis map music features to dance movements. Models are trained to associate motion patterns to audio patterns, usually without an explicit knowledge of the human body. This approach relies on a few assumptions: strong music-dance correlation, controlled motion data and relatively simple poses and movements. These characteristics are found in all existing datasets for dance motion synthesis, and indeed recent methods can achieve good results.We introduce a new dataset aiming to challenge these common assumptions, compiling a set of dynam…
Robust and reliable traffic sign detection is necessary to bring autonomous vehicles onto our roads. State-of-the-art algorithms successfully perform traffic sign detection over existing databases that mostly lack severe challenging conditions. VIP Cup 2017 competition focused on detecting such traffic signs under challenging conditions. To facilitate such task and competition, we introduced a video dataset denoted as CURE-TSD that includes a variety of challenging conditions. The goal of this challenge was to implement traffic sign detection algorithms that can robustly perform under such cha…
Saint Louis University（圣路易斯大学）；Temple University（天普大学）
Hotel recognition is an important task for human trafficking investigations since victims are often photographed in hotel rooms. Identifying these hotels is vital to trafficking investigations since they can help track down current and future victims who might be taken to the same places. Hotel recognition is a challenging fine grained visual classification task as there can be little similarity between different rooms within the same hotel, and high similarity between rooms from different hotels (especially if they are from the same chain). Hotel recognition to combat human trafficking poses …
Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science problems. In plant phenotyping, data competitions have a rich history, and new outdoor field datasets have potential for new data competitions. We developed the Global Wheat Challenge as a generalization competition to see if solutions for wheat head detection from field images would work in different regions around the world. In this paper, we analyze the winning challenge solutions in terms of their robustness and the relative importance of model and data augment…
Computer Vision Center, UAB（巴塞罗那自治大学计算机视觉中心）；CVIT, IIIT Hyderabad（海得拉巴国际信息技术研究所计算机视觉与智能技术中心）
This paper presents final results of ICDAR 2019 Scene Text Visual Question Answering competition (ST-VQA). ST-VQA introduces an important aspect that is not addressed by any Visual Question Answering system up to date, namely the incorporation of scene text to answer questions asked about an image. The competition introduces a new dataset comprising 23,038 images annotated with 31,791 question/answer pairs where the answer is always grounded on text instances present in the image. The images are taken from 7 different public computer vision datasets, covering a wide range of scenarios. The com…
Georgia Institute of Technology（佐治亚理工学院）；Anonymous University（匿名大学）
In this work, we describe our method for tackling the valence-arousal estimation challenge from ABAW FG-2020 Competition. The competition organizers provide an in-the-wild Aff-Wild2 dataset for participants to analyze affective behavior in real-life settings. We use MIMAMO Net \cite{deng2020mimamo} model to achieve information about micro-motion and macro-motion for improving video emotion recognition and achieve Concordance Correlation Coefficient (CCC) of 0.415 and 0.511 for valence and arousal on the reselected validation set.
In this paper, we present our solution, which placed 5th in the kaggle Google Universal Image Embedding Competition in 2022. We use the ViT-H visual encoder of CLIP from the openclip repository as a backbone and train a head model composed of BatchNormalization and Linear layers using ArcFace. The dataset used was a subset of products10K, GLDv2, GPR1200, and Food101. And applying TTA for part of images also improves the score. With this method, we achieve a score of 0.684 on the public and 0.688 on the private leaderboard. Our code is available. https://github.com/riron1206/kaggle-Google-Unive…
University of Malaya（马来亚大学）；ViTrox Technologies Sdn. Bhd.（ViTrox科技有限公司）；The University of Adelaide（阿德莱德大学）；The Chinese University of Hong Kong（香港中文大学）；South China University of Technology（华南理工大学）；Baidu（百度）；Webank（微众银行）
With hundreds of thousands of electronic chip components are being manufactured every day, chip manufacturers have seen an increasing demand in seeking a more efficient and effective way of inspecting the quality of printed texts on chip components. The major problem that deters this area of research is the lacking of realistic text on chips datasets to act as a strong foundation. Hence, a text on chips dataset, ICText is used as the main target for the proposed Robust Reading Challenge on Integrated Circuit Text Spotting and Aesthetic Assessment (RRC-ICText) 2021 to encourage the research on …
Netease Fuxi AI Lab（网易伏羲人工智能实验室）；Southwest University（西南大学）
Automatic affective recognition has been an important research topic in human computer interaction (HCI) area. With recent development of deep learning techniques and large scale in-the-wild annotated datasets, the facial emotion analysis is now aimed at challenges in the real world settings. In this paper, we introduce our submission to the 2nd Affective Behavior Analysis in-the-wild (ABAW2) Competition. In dealing with different emotion representations, including Categorical Emotions (CE), Action Units (AU), and Valence Arousal (VA), we propose a multi-task streaming network by a heuristic t…
Recently, text detection and recognition in natural scenes are becoming increasing popular in the computer vision community as well as the document analysis community. However, majority of the existing ideas, algorithms and systems are specifically designed for English. This technical report presents the final results of the ICDAR 2015 Text Reading in the Wild (TRW 2015) competition, which aims at establishing a benchmark for assessing detection and recognition algorithms devised for both Chinese and English scripts and providing a playground for researchers from the community. In this article…
Kitware Inc（Kitware公司）；Air Force Research Lab（空军研究实验室）
The xView2 competition and xBD dataset spurred significant advancements in overhead building damage detection, but the competition's pixel level scoring can lead to reduced solution performance in areas with tight clusters of buildings or uninformative context. We seek to advance automatic building damage assessment for disaster relief by proposing an auxiliary challenge to the original xView2 competition. This new challenge involves a new dataset and metrics indicating solution performance when damage is more local and limited than in xBD. Our challenge measures a network's ability to identif…
First Competition on Presentation Attack Detection on ID Card
Hochschule Darmstadt (h_da)（达姆施塔特应用技术大学）；Fraunhofer Institute for Computer Graphics Research IGD（计算机图形研究弗劳恩霍夫研究所）；Department of Computer Science, TU Darmstadt（达姆施塔特技术大学计算机科学系）；Facephi company（Facephi公司）；Faculty of Computer and Information Science, University of Ljubljana（卢布尔雅那大学计算机与信息科学学院）；Norwegian University of Science and Technology (NTNU)（挪威科学与技术大学）；ID VisionCenter (IDVC)（ID视觉中心）
CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction
Fudan University（复旦大学）；The Hong Kong Polytechnic University（香港理工大学）；Imperial College London（帝国理工学院）；Xiamen University（厦门大学）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）
University of Surrey（萨里大学）；University of Stirling（斯特灵大学）；Jiangnan University（江南大学）；Sichuan University（四川大学）；Reutlingen University（罗伊特林根大学）
This paper investigates the evaluation of dense 3D face reconstruction from a single 2D image in the wild. To this end, we organise a competition that provides a new benchmark dataset that contains 2000 2D facial images of 135 subjects as well as their 3D ground truth face scans. In contrast to previous competitions or challenges, the aim of this new benchmark dataset is to evaluate the accuracy of a 3D dense face reconstruction algorithm using real, accurate and high-resolution 3D ground truth face scans. In addition to the dataset, we provide a standard protocol as well as a Python script fo…
We present the 2017 DAVIS Challenge on Video Object Segmentation, a public dataset, benchmark, and competition specifically designed for the task of video object segmentation. Following the footsteps of other successful initiatives, such as ILSVRC and PASCAL VOC, which established the avenue of research in the fields of scene classification and semantic segmentation, the DAVIS Challenge comprises a dataset, an evaluation methodology, and a public competition with a dedicated workshop co-located with CVPR 2017. The DAVIS Challenge follows up on the recent publication of DAVIS (Densely-Annotated…
Human Identification at a Distance: Challenges, Methods and Results on the Competition HID 2025
Shenzhen Polytechnic University（深圳职业技术大学）；Fujitsu Research and Development Center Company Ltd.（富士通研发有限公司）；Beijing Jiaotong University（北京交通大学）；South China University of Technology（华南理工大学）；Shandong University（山东大学）；EVERSPRY；Wuhan University of Technology（武汉理工大学）；Sun Yat-sen University（中山大学）；Shanghai Jiao Tong University（上海交通大学）；Fujitsu Ltd.（富士通株式会社）；Huazhong University of Science and Technology（华中科技大学）；Hefei University of Technology（合肥工业大学）；Peking University, Shenzhen Graduate School（北京大学深圳研究生院）；Beijing Normal University（北京师范大学）；Watrix Technology Limited Co. Ltd.（华智科技有限公司）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Cordoba（科尔多瓦大学）；University of East London（东伦敦大学）；Southern University of Science and Technology（南方科技大学）
Shenzhen University（深圳大学）；Huazhong University of Science and Technology（华中科技大学）；Guangdong University of Technology（广东工业大学）；University of Science and Technology of China（中国科学技术大学）；iFlytek Co., Ltd.（科大讯飞股份有限公司）；iFlytek Research（讯飞研究院）
This paper presents a summary of the DFGC 2021 competition. DeepFake technology is developing fast, and realistic face-swaps are increasingly deceiving and hard to detect. At the same time, DeepFake detection methods are also improving. There is a two-party game between DeepFake creators and detectors. This competition provides a common platform for benchmarking the adversarial game between current state-of-the-art DeepFake creation and detection methods. In this paper, we present the organization, results and top solutions of this competition and also share our insights obtained during this e…
Beijing Seek Truth Data Technology Co.,Ltd.（北京求真数据科技有限公司）
The Affective Behavior Analysis in-the-wild (ABAW) 2022 Competition gives Affective Computing a large promotion. In this paper, we present our method of AU challenge in this Competition. We use improved IResnet100 as backbone. Then we train AU dataset in Aff-Wild2 on three pertained models pretrained by our private au and expression dataset, and Glint360K respectively. Finally, we ensemble the results of our models. We achieved F1 score (macro) 0.731 on AU validation set.
Skolkovo Institute of Science and Technology (Skoltech)（斯科尔科沃科学技术学院（Skoltech））；NtechLab（NtechLab公司）；Samsung AI Center, Moscow（三星莫斯科人工智能中心）；Reshetnev Siberian State University of Science and Technology（列舍特涅夫西伯利亚国立科技大学）
With the advancements of various autonomous car projects aiming to achieve SAE Level 5, real-time detection of traffic signs in real-life scenarios has become a highly relevant problem for the industry. Even though a great progress has been achieved in this field, there is still no clear consensus on what the state-of-the-art in this field is. Moreover, it is important to develop and test systems in various regions and conditions. This is why the "Ice Vision" competition has focused on the detection of Russian traffic signs in winter conditions. The IceVisionSet dataset used for this competiti…
Sber（俄罗斯联邦储蓄银行）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Lomonosov MSU（罗蒙诺索夫莫斯科国立大学）；STC-innovations（STC创新公司）；University of London（伦敦大学）；OCRV；AIRI（AIRI人工智能研究所）
This paper presents a new dataset of Peter the Great's manuscripts and describes a segmentation procedure that converts initial images of documents into the lines. The new dataset may be useful for researchers to train handwriting text recognition models as a benchmark for comparing different models. It consists of 9 694 images and text files corresponding to lines in historical documents. The open machine learning competition Digital Peter was held based on the considered dataset. The baseline solution for this competition as well as more advanced methods on handwritten text recognition are d…
Samsung AI Center（三星人工智能中心）；Steklov Institute of Mathematics at St. Petersburg（圣彼得堡斯捷克洛夫数学研究所）；Neuromation OU
We present the winning solution for the Inclusive Images Competition organized as part of the Conference on Neural Information Processing Systems (NeurIPS 2018) Competition Track. The competition was organized to study ways to cope with domain shift in image processing, specifically geographical shift: the training and two test sets in the competition had different geographical distributions. Our solution has proven to be relatively straightforward and simple: it is an ensemble of several CNNs where only the last layer is fine-tuned with the help of a small labeled set of tuning labels made av…
In this work, we describe our method for tackling the valence-arousal estimation challenge from ABAW2 ICCV-2021 Competition. The competition organizers provide an in-the-wild Aff-Wild2 dataset for participants to analyze affective behavior in real-life settings. We use a two stream model to learn emotion features from appearance and action respectively. To solve data imbalanced problem, we apply label distribution smoothing (LDS) to re-weight labels. Our proposed method achieves Concordance Correlation Coefficient (CCC) of 0.591 and 0.617 for valence and arousal on the validation set of Aff-wi…
We present here a model to take advantage of the multi-task nature of complex datasets by learning to separate tasks and subtasks in and end to end manner by biasing competitive interactions in the network. This method does not require additional labelling or reformatting of data in a dataset. We propose an alternate view to the monolithic one-task-fits-all learning of multi-task problems, and describe a model based on a theory of neuronal attention from neuroscience, proposed by Desimone. We create and exhibit a new toy dataset, based on the MNIST dataset, which we call MNIST-QA, for testing …
In the interactive image segmentation task, the Particle Competition and Cooperation (PCC) model is fed with a complex network, which is built from the input image. In the network construction phase, a weight vector is needed to define the importance of each element in the feature set, which consists of color and location information of the corresponding pixels, thus demanding a specialist's intervention. The present paper proposes the elimination of the weight vector through modifications in the network construction phase. The proposed model and the reference model, without the use of a weigh…
Millions of stray animals suffer on the streets or are euthanized in shelters every day around the world. In order to better adopt stray animals, scoring the pawpularity (cuteness) of stray animals is very important, but evaluating the pawpularity of animals is a very labor-intensive thing. Consequently, there has been an urgent surge of interest to develop an algorithm that scores pawpularity of animals. However, the dataset in Kaggle not only has images, but also metadata describing images. Most methods basically focus on the most advanced image regression methods in recent years, but there …
University of Maryland School of Medicine（马里兰大学医学院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Chest X-ray (CXR) datasets hosted on Kaggle, though useful from a data science competition standpoint, have limited utility in clinical use because of their narrow focus on diagnosing one specific disease. In real-world clinical use, multiple diseases need to be considered since they can co-exist in the same patient. In this work, we demonstrate how federated learning (FL) can be used to make these toy CXR datasets from Kaggle clinically useful. Specifically, we train a single FL classification model (`global`) using two separate CXR datasets -- one annotated for presence of pneumonia and the …
Indian Institute of Technology, Gandhinagar（印度理工学院甘地纳格尔分校）
Tables present important information concisely in many scientific documents. Visual features like mathematical symbols, equations, and spanning cells make structure and content extraction from tables embedded in research documents difficult. This paper discusses the dataset, tasks, participants' methods, and results of the ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX. Specifically, the task of the competition is to convert a tabular image to its corresponding LaTeX source code. We proposed two subtasks. In Subtask 1, we ask the participants to reconstruct the LaTeX str…
