WildFin：用于鱼类行为识别的野外数据集
WildFin: An In-the-Wild Dataset for Fish Behavioral Recognition
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中文总结 AI 辅助
针对复杂海洋环境中鱼类行为识别模型失效问题，推出WildFin野外数据集，经大规模整理标注后对视觉基础模型测试，发现其与现实需求存在显著差距。
中文摘要 AI 辅助
野外技术的最新进展为生态科学带来了大量野外视频数据。利用这些数据的主要瓶颈是专家标注的高成本。虽然计算机视觉提供了潜在的解决方案，但当前模型在复杂海洋环境中部署时经常失效。为了表征这些失效情况，我们推出WildFin——一个由[本网址]收集并标注的鱼类行为识别新基准。该数据集涵盖两个关键现实场景：固定相机监测鱼群，以及动态潜水员追踪个体目标。该数据集代表了大规模整理工作，涉及1350小时野外工作和600小时专家标注，产出9小时行为数据及超过200万帧级标签。我们对现代视觉基础模型进行基准测试，量化静态与时空架构之间的权衡，揭示当前模型能力与现实水下行为分析需求之间仍存在显著差距。项目网站：[本网址]。
英文摘要
Recent advances in field technology have led to a massive influx of in-the-wild video data for ecological science. The primary bottleneck in leveraging this data is the high cost of expert annotation. While computer vision offers a potential solution, current models frequently fail when deployed in complex marine environments. To characterize these failures, we introduce WildFin, a novel benchmark for fish behavior recognition collected and annotated by ecologists. WildFin spans two critical real-world paradigms: stationary cameras monitoring groups of fish and dynamic divers following individual subjects. The dataset represents a massive curation effort, involving 1,350 hours of fieldwork and 600 hours of expert annotation to produce 9 hours of behavioral data with over 2 million frame-by-frame labels. We benchmark modern vision foundation models and quantify tradeoffs between static and spatiotemporal architectures, revealing the substantial gap that remains between current model capabilities and the demands of real-world underwater behavioral analysis. Project website: https://team-wildfin.github.io/.
发表机构
- Cornell University（康奈尔大学）
- University of Colorado Boulder（科罗拉多大学博尔德分校）
- HHMI Janelia Research Campus（HHMI珍妮亚研究园区）
机构由 AI 辅助整理，请以论文原文为准。
