{"ID":2827366,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.16133","arxiv_id":"2512.16133","title":"Interaction-via-Actions: Cattle Interaction Detection with Joint Learning of Action-Interaction Latent Space","abstract":"This paper introduces a method and application for automatically detecting behavioral interactions between grazing cattle from a single image, which is essential for smart livestock management in the cattle industry, such as for detecting estrus. Although interaction detection for humans has been actively studied, a non-trivial challenge lies in cattle interaction detection, specifically the lack of a comprehensive behavioral dataset that includes interactions, as the interactions of grazing cattle are rare events. We, therefore, propose CattleAct, a data-efficient method for interaction detection by decomposing interactions into the combinations of actions by individual cattle. Specifically, we first learn an action latent space from a large-scale cattle action dataset. Then, we embed rare interactions via the fine-tuning of the pre-trained latent space using contrastive learning, thereby constructing a unified latent space of actions and interactions. On top of the proposed method, we develop a practical working system integrating video and GPS inputs. Experiments on a commercial-scale pasture demonstrate the accurate interaction detection achieved by our method compared to the baselines. Our implementation is available at https://github.com/rakawanegan/CattleAct.","short_abstract":"This paper introduces a method and application for automatically detecting behavioral interactions between grazing cattle from a single image, which is essential for smart livestock management in the cattle industry, such as for detecting estrus. Although interaction detection for humans has been actively studied, a no...","url_abs":"https://arxiv.org/abs/2512.16133","url_pdf":"https://arxiv.org/pdf/2512.16133v1","authors":"[\"Ren Nakagawa\",\"Yang Yang\",\"Risa Shinoda\",\"Hiroaki Santo\",\"Kenji Oyama\",\"Fumio Okura\",\"Takenao Ohkawa\"]","published":"2025-12-18T03:42:54Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false,"code_links":[{"ID":605801,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2827366,"paper_url":"https://arxiv.org/abs/2512.16133","paper_title":"Interaction-via-Actions: Cattle Interaction Detection with Joint Learning of Action-Interaction Latent Space","repo_url":"https://github.com/rakawanegan/CattleAct","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}