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<p>This paper introduces Animal Recognition Through Enhanced Multimodal Integration System (ARTEMIS), a transformer-based framework designed for multilabel animal action recognition by fusing video, image, and textual modalities. ARTEMIS utilizes state-of-the-art captioning and language models, such as BLIP2 and Llama 3, to generate textual descriptions from video frames, which are input to the model, significantly enhancing its performance unlikely previous results that do not consider this modality. Through comprehensive ablation studies, we explore the contribution of various model components and propose optimization strategies, including genetic algorithms and reinforcement learning, to dynamically adjust ensemble weights. Our feature alignment techniques-using contrastive and cosine similarity losses-further improve multimodal integration. Evaluations on the Animal Kingdom dataset, which includes 30,100 clips across 140 action classes, demonstrate that ARTEMIS achieves a new state-of-the-art mAP of 79.82, outperforming existing methods. The combination of multimodal fusion and ensemble strategies makes ARTEMIS a robust solution for complex animal action recognition tasks. The code of our fusion method is available at <a href="https://github.com/edofazza/ARTEMIS.">https://github.com/edofazza/ARTEMIS.</a></p>

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ARTEMIS: animal recognition through enhanced multimodal integration system

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月18日

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  • Edoardo Fazzari,
  • Donato Romano,
  • Fabrizio Falchi,
  • Cesare Stefanini

摘要

This paper introduces Animal Recognition Through Enhanced Multimodal Integration System (ARTEMIS), a transformer-based framework designed for multilabel animal action recognition by fusing video, image, and textual modalities. ARTEMIS utilizes state-of-the-art captioning and language models, such as BLIP2 and Llama 3, to generate textual descriptions from video frames, which are input to the model, significantly enhancing its performance unlikely previous results that do not consider this modality. Through comprehensive ablation studies, we explore the contribution of various model components and propose optimization strategies, including genetic algorithms and reinforcement learning, to dynamically adjust ensemble weights. Our feature alignment techniques-using contrastive and cosine similarity losses-further improve multimodal integration. Evaluations on the Animal Kingdom dataset, which includes 30,100 clips across 140 action classes, demonstrate that ARTEMIS achieves a new state-of-the-art mAP of 79.82, outperforming existing methods. The combination of multimodal fusion and ensemble strategies makes ARTEMIS a robust solution for complex animal action recognition tasks. The code of our fusion method is available at https://github.com/edofazza/ARTEMIS.