Fish-Vista：用于从图像中理解与识别性状的多用途数据集
Fish-Vista: A Multi-Purpose Dataset for Understanding & Identification of Traits from Images
AI总结：
Fish-Vista是一个包含69,126张图像、4,154种鱼类的生物数据集，用于物种分类、性状识别与分割，通过可复现流程构建，并基准测试现有方法以推动AI在生物多样性科学中的研究。
摘要：
我们推出了Fish-Visual Trait Analysis（Fish-Vista），这是首个面向直接从图像中分析水生物种视觉性状的生物图像数据集，其利用计算机视觉中的问题建模方法。Fish-Vista包含69,126张带标注的图像，涵盖4,154种鱼类，经过精心整理和组织，服务于物种分类、性状识别和性状分割三个下游任务。我们的工作有两项关键贡献。首先，我们构建了一个完全可复现的数据处理流程，用于处理来自多个博物馆馆藏的图像。我们利用从生物数据库和人工标注中精心整理的标签对这些图像进行标注，创建了一个AI就绪的视觉性状数据集，从而推动AI在生物多样性科学中的发展。其次，我们提出的下游任务为新颖的计算机视觉研究提供了肥沃的土壤，以应对长尾分布、分布外泛化、弱标签学习、可解释AI以及小目标分割等各类挑战。我们针对所提出的任务对多种现有方法的性能进行了基准测试，以揭示AI在涉及视觉性状的生物多样性科学问题中的未来研究机遇。
英文摘要：
We introduce Fish-Visual Trait Analysis (Fish-Vista), the first organismal image dataset designed for the analysis of visual traits of aquatic species directly from images using problem formulations in computer vision. Fish-Vista contains 69,126 annotated images spanning 4,154 fish species, curated and organized to serve three downstream tasks of species classification, trait identification, and trait segmentation. Our work makes two key contributions. First, we perform a fully reproducible data processing pipeline to process images sourced from various museum collections. We annotate these images with carefully curated labels from biological databases and manual annotations to create an AI-ready dataset of visual traits, contributing to the advancement of AI in biodiversity science. Second, our proposed downstream tasks offer fertile grounds for novel computer vision research in addressing a variety of challenges such as long-tailed distributions, out-of-distribution generalization, learning with weak labels, explainable AI, and segmenting small objects. We benchmark the performance of several existing methods for our proposed tasks to expose future research opportunities in AI for biodiversity science problems involving visual traits.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
