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 have prepared a challenge where the training data and test data are from different cameras spread across the globe. For each camera, we provide a series of remote sensing imagery that is tied to the location of the camera. We also provide citizen science imagery from the set of species seen in our data. The challenge is to correctly classify species in the test camera traps.
相机陷阱（或野生凸轮），使自动收集大量的图像数据。世界各地的生物学家都使用相机陷阱来监测动物物种的生物多样性和种群密度。计算机视觉社区一直在朝着自动化相机陷阱中的物种分类挑战迈进，但是当我们试图将这些模型的范围从我们收集训练数据的特定区域扩展到不同区域时，我们面临着一个有趣的问题：如何对新区域中的物种进行分类，而这些区域可能在以前的训练数据中没有看到？为了解决这个问题，我们准备了一个数据集和挑战，其中训练数据和测试数据来自不同的地区，即美国西南部和美国西北部。我们使用从美国西南部收集的Caltech Camera Traps数据集作为训练数据。我们添加了一个来自美国西北部的新数据集，该数据集由爱达荷州渔业和游戏部（IDFG）提供的数据整理而成，作为我们的测试数据集。测试数据与训练数据有一些类重叠，在两个数据集中都发现了一些物种，但在训练期间看到的两个物种在测试期间都没有看到，反之亦然。为了帮助填补训练物种的空白，我们允许竞争对手利用来自两个替代领域的迁移学习：iNaturalist的人工策划图像和Microsoft的TrapCam-AirSim模拟环境的合成图像。
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, (…
California Institute of Technology（加州理工学院）；Google（谷歌）
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 esti…
Efficient Method for Categorize Animals in the Wild
Peking University（北京大学）
相机陷阱中的物种自动分类将对地球上的生物多样性监测和物种分析有很大的帮助。为了加速自动物种分类任务的开发，“Microsoft AI for Earth”在CVPR 2019的FGVC 6研讨会上准备了一项挑战，称为“iWildCam 2019竞赛”。在这项工作中，我们提出了一种有效的方法来分类野生动物。我们将最先进的ImagaNet预训练模型转移到问题中。为了提高模型的泛化能力和鲁棒性，我们采用了有效的图像增强和正则化策略，如剪切，混合和标签平滑。最后，我们使用集成学习来提高模型的性能。由于先进的正则化策略和集成学习，我们在最终排行榜中获得了前7/336名。本工作的源代码可在https://github.com/Walleclipse/iWildCam_2019_FGVC6上获得
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…
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…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University at Buffalo, State University of New York（纽约州立大学布法罗分校）
This paper presents the summary report on our DFGC 2022 competition. The DeepFake is rapidly evolving, and realistic face-swaps are becoming more deceptive and difficult to detect. On the contrary, methods for detecting DeepFakes are also improving. There is a two-party game between DeepFake creators and defenders. This competition provides a common platform for benchmarking the game between the current state-of-the-arts in DeepFake creation and detection methods. The main research question to be answered by this competition is the current state of the two adversaries when competed with each o…
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.
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…
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…
From Affect to Complex Behavior: Advancing Multimodal Human-Centered AI at the 10th ABAW Workshop & Competition
Queen Mary University of London（伦敦皇后玛丽大学）；Hume AI；Google Deepmind（谷歌DeepMind）；Imperial College London（伦敦帝国理工学院）；Cogitat；LIVIA；ILLS；ETS Montreal（蒙特利尔ETS）；Concordia University（Concordia大学）；Xi’an Jiaotong University（西安交通大学）
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）
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…
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 …
Computer Vision Center, UAB（巴塞罗那自治大学计算机视觉中心）；CVIT, IIIT Hyderabad（海德拉巴国际信息技术学院视觉与信息技术中心）
In this report we present results of the ICDAR 2021 edition of the Document Visual Question Challenges. This edition complements the previous tasks on Single Document VQA and Document Collection VQA with a newly introduced on Infographics VQA. Infographics VQA is based on a new dataset of more than 5,000 infographics images and 30,000 question-answer pairs. The winner methods have scored 0.6120 ANLS in Infographics VQA task, 0.7743 ANLSL in Document Collection VQA task and 0.8705 ANLS in Single Document VQA. We present a summary of the datasets used for each task, description of each of the su…
The fifth Affective Behavior Analysis in-the-wild (ABAW) Competition is part of the respective ABAW Workshop which will be held in conjunction with IEEE Computer Vision and Pattern Recognition Conference (CVPR), 2023. The 5th ABAW Competition is a continuation of the Competitions held at ECCV 2022, IEEE CVPR 2022, ICCV 2021, IEEE FG 2020 and CVPR 2017 Conferences, and is dedicated at automatically analyzing affect. For this year's Competition, we feature two corpora: i) an extended version of the Aff-Wild2 database and ii) the Hume-Reaction dataset. The former database is an audiovisual one of…
Université Paris-Saclay（巴黎萨克雷大学）；Tsinghua University（清华大学）；MILA - Québec AI Institute（MILA - 魁北克人工智能研究所）；Polytechnique Montréal（蒙特利尔理工学院）；Universitat de Barcelona（巴塞罗那大学）
We present the design and baseline results for a new challenge in the ChaLearn meta-learning series, accepted at NeurIPS'22, focusing on "cross-domain" meta-learning. Meta-learning aims to leverage experience gained from previous tasks to solve new tasks efficiently (i.e., with better performance, little training data, and/or modest computational resources). While previous challenges in the series focused on within-domain few-shot learning problems, with the aim of learning efficiently N-way k-shot tasks (i.e., N class classification problems with k training examples), this competition challen…
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…
ICDAR2017 Competition on Reading Chinese Text in the Wild (RCTW-17)
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）
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）
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…
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 …
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）
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）
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…
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.
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…
