Prompting-MammAlps: Fine-Grained Text-to-Video Retrieval for Camera-Trap Data

• URL: http://arxiv.org/abs/2607.09876v1
• Date: Fri, 10 Jul 2026 18:09:41 GMT
• Title: Prompting-MammAlps: Fine-Grained Text-to-Video Retrieval for Camera-Trap Data
• Abstract summary: Text-to-Video retrieval (TVR) methods based on large video-language models (VLMs) have potential to retrieve events of interest by describing them with simple text queries.<n>In this work, we introduce Prompting-MammAlps, the first camera-trap TV benchmark, and propose a fine-grained and interpretable TVR method.<n>Specifically, we trained a vision transformer to performtemporal action localization, and convert its output to structured text, describing each video.<n>Independently, ethology-inspired queries are processed by a Large-Language Model (LLM) based agent
• Score: 49.717366362764956
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatically retrieving videos from large camera-trap datasets remains challenging. Text-to-Video retrieval (TVR) methods based on large video-language models (VLMs) have potential to retrieve events of interest by describing them with simple text queries. However, current methods often lack spatiotemporal understanding and do not generalize well to ecological data. In this work, we introduce Prompting-MammAlps, the first camera-trap TVR benchmark, and propose a fine-grained and interpretable TVR method. Specifically, we trained a vision transformer to perform spatiotemporal action localization, and convert its output to structured text, describing each video. Independently, ethology-inspired queries are processed by a Large-Language Model (LLM) based coding agent to parse the structured text per video and retrieve videos accordingly. We harnessed the LLM to use functions from a custom parsing library to minimize the risk of LLM hallucinations and to improve method interpretability. This retrieval approach applied on the Prompting-MammAlps benchmark achieved a set-based F1-score of 34\% on a test set of 135 ecologically-relevant queries and 775 candidate videos. In comparison the best zero-shot VLM achieved a F1-score of 18\%, while also lacking interpretability. Project page: https://cnai.epfl.ch/prompting-mammalps

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