Deep learning has become the standard methodology to approach computer vision tasks when large amounts of labeled data are available. One area where traditional deep learning approaches fail to perform is one-shot learning tasks where a model must correctly classify a new category after seeing only one example. One such domain is animal re-identification, an application of computer vision which can be used globally as a method to automate species population estimates from camera trap images. Our work demonstrates both the application of similarity comparison networks to animal re-identification, as well as the capabilities of deep convolutional neural networks to generalize across domains. Few studies have considered animal re-identification methods across species. Here, we compare two similarity comparison methodologies: Siamese and Triplet-Loss, based on the AlexNet, VGG-19, DenseNet201, MobileNetV2, and InceptionV3 architectures considering mean average precision (mAP)@1 and mAP@5. We consider five data sets corresponding to five different species: humans, chimpanzees, humpback whales, fruit flies, and Siberian tigers, each with their own unique set of challenges. We demonstrate that Triplet Loss outperformed its Siamese counterpart for all species. Without any species-specific modifications, our results demonstrate that similarity comparison networks can reach a performance level beyond that of humans for the task of animal re-identification. The ability for researchers to re-identify an animal individual upon re-encounter is fundamental for addressing a broad range of questions in the study of population dynamics and community/behavioural ecology. Our expectation is that similarity comparison networks are the beginning of a major trend that could stand to revolutionize animal re-identification from camera trap data.
Calibrated Similarity and Graph Clustering for Open-Set Animal Re-Identification
Made In Alexandria Artificial Intelligence Team（亚历山大制造人工智能团队）；Faculty of Computer Science and Engineering, Alamein International University（阿拉曼国际大学计算机科学与工程学院）；Faculty of Computers and Data Science, Alexandria University（亚历山大大学计算机与数据科学学院）；Faculty of Engineering, Alexandria University（亚历山大大学工程学院）；Alexandria Higher Institute of Engineering and Technology（亚历山大高等工程与技术学院）；Moscow Pedagogical State University (MPGU University)（莫斯科国立师范大学（MPGU大学））
WildFusion: Individual Animal Identification with Calibrated Similarity Fusion
Czech Technical University in Prague（布拉格捷克技术大学）；University of West Bohemia（西波希米亚大学）；Inria（法国国家信息与自动化研究所）；University of Montpellier（蒙彼利埃大学）；CNRS（法国国家科学研究中心）
A non-invasive video-based method for individual identification of wildlife using gait dynamics
Department of Computer Science, University of Oxford（牛津大学计算机科学系）；Wildlife Conservation Research Unit, Department of Biology, University of Oxford（牛津大学生物系野生动物保护研究组）
A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment
Universität Bremen（不来梅大学）；Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；Technische Universität Berlin（柏林工业大学）；Berlin Institute of Technology（柏林工业大学）
Deep learning-based segmentation of T1 and T2 cardiac MRI maps for automated disease detection
Transilvania University of Brasov（布拉索夫特兰西瓦尼亚大学）；Siemens SRL（西门子有限责任公司）；Robert Bosch Hospital（罗伯特·博世医院）；Siemens Healthcare GmbH（西门子医疗有限责任公司）；Siemens Medical Solutions USA, Inc.（西门子医疗解决方案美国公司）；Siemens Healthineers AG（西门子医疗股份公司）
Comprehensive framework for evaluation of deep neural networks in detection and quantification of lymphoma from PET/CT images: clinical insights, pitfalls, and observer agreement analyses
University of British Columbia（英属哥伦比亚大学）；BC Cancer Research Institute（BC癌症研究所）；Microsoft AI for Good Lab（微软AI公益实验室）；Pomeranian Medical University（波美拉尼亚医科大学）；BC Children’s Hospital（BC儿童医院）；St. Mary’s Hospital（圣玛丽医院）；BC Cancer（BC癌症中心）
The Right Spin: Learning Object Motion from Rotation-Compensated Flow Fields
TU Berlin（柏林工业大学）；University of Massachusetts（马萨诸塞大学）；Inria（法国国家信息与自动化研究所）；École Normale Supérieure（巴黎高等师范学院）；CNRS（法国国家科学研究中心）；PSL Research University（巴黎文理研究大学）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Grenoble INP（格勒诺布尔理工学院）
