Texture can be defined as the change of image intensity that forms repetitive patterns, resulting from physical properties of the object's roughness or differences in a reflection on the surface. Considering that texture forms a complex system of patterns in a non-deterministic way, biodiversity concepts can help texture characterization in images. This paper proposes a novel approach capable of quantifying such a complex system of diverse patterns through species diversity and richness and taxonomic distinctiveness. The proposed approach considers each image channel as a species ecosystem and computes species diversity and richness measures as well as taxonomic measures to describe the texture. The proposed approach takes advantage of ecological patterns' invariance characteristics to build a permutation, rotation, and translation invariant descriptor. Experimental results on three datasets of natural texture images and two datasets of histopathological images have shown that the proposed texture descriptor has advantages over several texture descriptors and deep methods.
DivShift: Exploring Domain-Specific Distribution Shifts in Large-Scale, Volunteer-Collected Biodiversity Datasets
Stanford University（斯坦福大学）；University of Freiburg（弗莱堡大学）；Carnegie Science（卡内基科学研究所）；Federal University of Minas Gerais（米纳斯吉拉斯联邦大学）；University of California, Berkeley（加州大学伯克利分校）；Howard Hughes Medical Institute（霍华德·休斯医学研究所）
BIOSCAN-5M: A Multimodal Dataset for Insect Biodiversity
Centre for Biodiversity Genomics（生物多样性基因组学中心）；University of Guelph（圭尔夫大学）；University of Waterloo（滑铁卢大学）；Simon Fraser University（西蒙菲莎大学）；Vector Institute（矢量研究所）；Alberta Machine Intelligence Institute (Amii)（阿尔伯塔机器智能研究所）；Aalborg University（奥尔堡大学）；Pioneer Centre for AI（人工智能先锋中心）
Friedrich Schiller University Jena（耶拿弗里德里希·席勒大学）；German Aerospace Center (DLR)（德国航空航天中心）
Deep-learning methods offer unsurpassed recognition performance in a wide range of domains, including fine-grained recognition tasks. However, in most problem areas there are insufficient annotated training samples. Therefore, the topic of transfer learning respectively domain adaptation is particularly important. In this work, we investigate to what extent unsupervised domain adaptation can be used for fine-grained recognition in a biodiversity context to learn a real-world classifier based on idealized training data, e.g. preserved butterflies and plants. Moreover, we investigate the influen…
TerraIncognita: A Dynamic Benchmark for Species Discovery Using Frontier Models
Iowa State University（爱荷华州立大学）；University of Arizona（亚利桑那大学）；University of Nevada（内华达大学）；Yanayacu Biological Station and Center for Creative Studies（亚纳亚库生物站和创意研究中心）；New York University（纽约大学）
Open-Insect: Benchmarking Open-Set Recognition of Novel Species in Biodiversity Monitoring
McGill University（麦吉尔大学）；Mila - Quebec Artificial Intelligence Institute（魁北克人工智能研究所）；University of Copenhagen（哥本哈根大学）；Agriculture and Agri-food Canada（加拿大农业与农业食品部）；Smithsonian Tropical Research Institute（史密松尼热带研究所）；Biology Center, Czech Academy of Sciences（捷克科学院生物学中心）；Maestria de Entomologia, University of Panama（巴拿马大学昆虫学系）；Massachusetts Institute of Technology（麻省理工学院）；Montréal Insectarium（蒙特利尔昆虫馆）
Automated identification of Ichneumonoidea wasps via YOLO-based deep learning: Integrating HiresCam for Explainable AI
São Carlos School of Engineering, University of São Paulo（圣卡洛斯工程学院，圣保罗大学）；Department of Ecology and Evolutionary Biology, Federal University of São Carlos（生态与进化生物学系，巴西联邦圣卡洛斯大学）；Federal University of Tocantins（托坎蒂斯联邦大学）；Department of Biology, University of São Paulo（生物学系，圣保罗大学）；State University of Piauí, Deputado Jesualdo Cavalcanti Campus（皮亚伊州立大学，德putado Jesualdo Cavalcanti校区）
A Step Towards Worldwide Biodiversity Assessment: The BIOSCAN-1M Insect Dataset
University of Guelph（圭尔夫大学）；Vector Institute for AI（向量人工智能研究所）；Centre for Biodiversity Genomics（生物多样性基因组学中心）；Aalborg University（奥尔堡大学）；Pioneer Centre for AI（先锋人工智能中心）；Dalhousie University（达尔豪斯大学）；Simon Fraser University（西蒙弗雷泽大学）；University of Waterloo（滑铁卢大学）；Alberta Machine Intelligence Institute (Amii)（阿尔伯塔机器智能研究所）
CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at Scale
Simon Fraser University（西蒙弗雷泽大学）；Aalborg University（奥胡斯大学）；Vector Institute（向量研究所）；University of Guelph（圭尔夫大学）；Alberta Machine Intelligence Institute (Amii)（阿尔伯塔人工智能研究所）
Optimizing Image Capture for Computer Vision-Powered Taxonomic Identification and Trait Recognition of Biodiversity Specimens
University of Maine（缅因大学）；The Ohio State University（俄亥俄州立大学）；University of Puerto Rico Rio Piedras（波多黎各大学里奥彼德拉斯分校）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；University of Guelph（圭尔夫大学）；Vector Institute（向量研究所）；University of Florida（佛罗里达大学）；Texas Tech University（德克萨斯理工大学）；McGill University（麦吉尔大学）；Arizona State University（亚利桑那州立大学）；National Ecological Observatory Network（国家生态观测站网络）；University of Michigan（密歇根大学）；Antioch College（安提阿学院）；University of Colorado（科罗拉多大学）；Duke University（杜克大学）
A multi-modal dataset for insect biodiversity with imagery and DNA at the trap and individual level
Department of Ecology, Swedish University of Agricultural Sciences（生态系，瑞典农业科学大学）；University of Guelph（圭尔夫大学）；Vector Institute（向量研究所）；University of Helsinki（赫尔辛基大学）；Department of Entomology, National Museum of Natural History, Smithsonian Institution（昆虫学系，国家自然历史博物馆，史密松尼研究所）；Department of Biological and Environmental Science, University of Jyväskylä（生物与环境科学系，于韦斯屈埃大学）；Faculty of Biological and Environmental Sciences, University of Helsinki（生物与环境科学学院，赫尔辛基大学）；Centre for Biodiversity Genomics, University of Guelph（生物多样性基因组中心，圭尔夫大学）；NVIDIA（NVIDIA公司）；University of Toronto（多伦多大学）；Kilpisjärvi Biological Station, University of Helsinki（基利皮斯杰里生物学站，赫尔辛基大学）；Unit for Field-based Forest Research, Swedish University of Agricultural Sciences（基于现场的森林研究单位，瑞典农业科学大学）
University of Lincoln（林肯大学）；Lincolnshire Wildlife Trust（林肯郡野生动物信托基金会）；Plantlife（植物生命组织）
Open space grassland is being increasingly farmed or built upon, leading to a ramping up of conservation efforts targeting roadside verges. Approximately half of all UK grassland species can be found along the country's 500,000 km of roads, with some 91 species either threatened or near threatened. Careful management of these "wildlife corridors" is therefore essential to preventing species extinction and maintaining biodiversity in grassland habitats. Wildlife trusts have often enlisted the support of volunteers to survey roadside verges and identify new "Local Wildlife Sites" as areas of hig…
INQUIRE-Search: Interactive Discovery in Large-Scale Biodiversity Databases
Massachusetts Institute of Technology（麻省理工学院）；McGill University（麦吉尔大学）；Cary Institute of Ecosystem Studies（生态系统研究所）；The Ohio State University（俄亥俄州立大学）；University of California Santa Barbara（加州大学圣塔芭芭拉分校）；Smithsonian’s National Zoo & Conservation Biology Institute（史密森尼国家动物园与保护生物学研究所）；University of Edinburgh（爱丁堡大学）
University College London（伦敦大学学院）；Niantic；University of Edinburgh（爱丁堡大学）
We address the problem of learning self-supervised representations from unlabeled image collections. Unlike existing approaches that attempt to learn useful features by maximizing similarity between augmented versions of each input image or by speculatively picking negative samples, we instead also make use of the natural variation that occurs in image collections that are captured using static monitoring cameras. To achieve this, we exploit readily available context data that encodes information such as the spatial and temporal relationships between the input images. We are able to learn repr…
From Images to Insights: Explainable Biodiversity Monitoring with Plain Language Habitat Explanations
Leibniz Centre for Agricultural Landscape Research (ZALF)（莱比锡农业景观研究中心（ZALF））；Brandenburg University of Technology Cottbus–Senftenberg（勃兰登堡技术大学科特博斯-森芬根堡）
Explainable AI for Biodiversity Monitoring and Ecological Image Analysis
Pratt School of Engineering, Duke University（杜克大学工程学院）；British Antarctic Survey（英国南极调查局）；Department of Applied Mathematics and Theoretical Physics, University of Cambridge（剑桥大学应用数学与理论物理系）；Duke University Division of Marine Science and Conservation, Nicholas School of the Environment, Duke University Marine Laboratory（杜克大学海洋科学与保护分会，尼古拉斯环境学院，杜克大学海洋实验室）
