WildFin: An In-the-Wild Dataset for Fish Behavioral Recognition

• URL: http://arxiv.org/abs/2608.21281v1
• Date: Fri, 21 Aug 2026 16:38:01 GMT
• Title: WildFin: An In-the-Wild Dataset for Fish Behavioral Recognition
• Abstract summary: We introduce WildFin, a novel benchmark for fish behavior recognition collected and annotated by ecologists.<n>The dataset represents a massive effort, involving 1,350 hours of fieldwork and 600 hours of expert annotation.<n>We benchmark modern vision foundation models and tradeoffs between static andtemporal architectures.
• Score: 4.0409326505680765
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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/.

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