--- type: "post" id: "validated-on-56-hours-of-wild-lemur-footage-9761ce" slug: "validated-on-56-hours-of-wild-lemur-footage-9761ce" title: "Validated on 56 hours of wild lemur footage with ten interaction classes; paper here ." description: "**Thesis:** Interaction annotation can make video datasets useful for studying social behavior, not just individual actions." retrieval_nugget: "**Thesis:** Interaction annotation can make video datasets useful for studying social behavior, not just individual actions." published_at: "2026-10-21" updated_at: "2026-10-21" record_date: "2026-10-21" date_kind: "published_at" topics: ["multimodal"] source_urls: ["https://arxiv.org/abs/2511.03819"] schema_version: "newruntime-agent-readable-v0.2" stable_id: "post:validated-on-56-hours-of-wild-lemur-footage-9761ce" status: "published" visuals: [] editorial_provenance: {"schema_version":"newruntime-editorial-copy-v1","content_status":"source_grounded_final","final_copy_sha256":"sha256:28259b95aa330d197f1b68ea1fd238858143bd762efc852cfe65103b2293c49a","reviewed_at":"2026-10-21T02:02:14.625Z","source_evidence_count":1,"verified_claim_count":3,"site_analysis_schema_version":"newruntime-site-analysis-v1","site_object_kind":"field_note","observed_fact_count":3,"implication_count":2,"watch_condition_count":1,"related_record_count":0} analysis: {"schema_version":"newruntime-site-analysis-v1","object_kind":"field_note","thesis":"Interaction annotation can make video datasets useful for studying social behavior, not just individual actions.","observed_facts":[{"text":"The paper describes SiLVi as open-source software combining behavioral labeling and localization of individuals in video.","source_urls":["https://arxiv.org/abs/2511.03819"]},{"text":"The paper says SiLVi was validated using 56 hours of wild lemur footage.","source_urls":["https://arxiv.org/abs/2511.03819"]},{"text":"The abstract states that outputs are structured for training and validating computer-vision models.","source_urls":["https://arxiv.org/abs/2511.03819"]}],"mechanism":"The tool lets researchers label behaviors and interactions in video and export structured outputs that can be used to train or validate computer-vision models.","why_now":"The paper says video volumes from camera traps, drones, and direct observation are growing, while interaction annotation has received less attention than individual-action detection.","implications":["Researchers can assess whether the combined annotation workflow fits studies requiring fine-grained social-behavior data.","The source's mention of possible use for human-interaction video is a potential application, not demonstrated validation."],"evidence_boundary":"The supplied paper abstract establishes the tool's stated functions and a lemur-footage validation example, but does not provide evidence here of accuracy or transfer to other species, datasets, or human video.","watch_conditions":["Check whether subsequent evaluations report annotation reliability and model performance across additional datasets."],"related_records":[],"new_branch_reason":"No prior New Runtime record is supplied for this research tool or its interaction-annotation approach, so this begins a distinct record."} editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]} routes: {"html":"https://newruntime.com/posts/validated-on-56-hours-of-wild-lemur-footage-9761ce/","markdown":"https://newruntime.com/posts/validated-on-56-hours-of-wild-lemur-footage-9761ce.md","json":"https://newruntime.com/posts/validated-on-56-hours-of-wild-lemur-footage-9761ce.json"} --- # Validated on 56 hours of wild lemur footage with ten interaction classes; paper here . ## Retrieval answer **Thesis:** Interaction annotation can make video datasets useful for studying social behavior, not just individual actions. **Thesis:** Interaction annotation can make video datasets useful for studying social behavior, not just individual actions. The paper introduces SiLVi, an open-source labeling tool that combines behavior annotation and individual localization in video. **Mechanism:** The tool lets researchers label behaviors and interactions in video and export structured outputs that can be used to train or validate computer-vision models. **Observed fact:** The paper says the tool was validated on 56 hours of wild lemur footage. **Observed fact:** Its abstract says SiLVi joins behavioral labeling with individual localization and supports annotation of interactions. This pairing gives researchers a way to represent who is doing what with whom, rather than treating each animal's action as an isolated label. **Why now:** The paper says video volumes from camera traps, drones, and direct observation are growing, while interaction annotation has received less attention than individual-action detection. **First implication:** Researchers can assess whether the combined annotation workflow fits studies requiring fine-grained social-behavior data. **Evidence boundary:** The supplied paper abstract establishes the tool's stated functions and a lemur-footage validation example, but does not provide evidence here of accuracy or transfer to other species, datasets, or human video. **First watch condition:** Check whether subsequent evaluations report annotation reliability and model performance across additional datasets. The source describes possible use for annotating human interactions, but that is a proposed broader application rather than demonstrated validation. The next meaningful evidence would be reproducible annotation reliability and model-performance results on additional datasets. Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json ## Continue in the graph Next records for an agent, linked to their Markdown twins. 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