发表机构
University of Bristol; Wild Chimpanzee Foundation; Carnegie Mellon University; University of California; Peking University; Max Planck Institute for Evolutionary Anthropology; Senckenberg Museum of Natural History（布里斯托大学; 野生黑猩猩基金会; 卡内基梅隆大学; 加利福尼亚大学; 北京大学; 马克斯·普朗克进化人类学研究所; 森肯伯格自然历史博物馆）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在解决野生大猩猩社会行为识别数据不足问题，引入PanAf-SBR数据集，结合AlphaChimp架构建立基准，通过双向迁移学习实验发现跨数据集预训练对特定类别有益，还研究了背景上下文在其中的作用。
AI 中文摘要
野生大猩猩种群的行为变化，尤其是社会结构的瓦解，可作为种群衰退的早期指标。因此，自动化检测这些行为变化对保护工作至关重要。近期虽有一些用于大猩猩行为自动识别的数据集，但含细粒度社会行为注释的很少，且多在圈养环境或通过无人机等空中平台获取。我们引入了PanAf-SBR，首个标注有社会行为的野生大猩猩相机陷阱数据集，它扩展了PanAf500，包含100个视频及36063帧，还有81096个注释。我们用此数据和AlphaChimp架构建立了首个基于相机陷阱 footage的野生大猩猩细粒度社会行为识别基准。还在PanAf-SBR和圈养的ChimpACT数据集间进行双向迁移学习实验，发现跨数据集预训练对特定类别有益而非普遍有益。最后通过反转分割掩码抑制非猿像素来研究背景上下文的作用。
英文摘要
Behavioural shifts in wild great ape populations, particularly the breakdown of social structures, can serve as an early indicator of population decline. Automating the detection of behaviours indicative of these shifts is therefore a critical task for conservation. Several valuable datasets have recently been introduced for the automated recognition of great ape behaviour, yet few include fine-grained social behaviour annotations, and those that do are captured either in captive settings or via aerial platforms such as UAVs. We address this gap by introducing PanAf-SBR, the first wild great ape camera trap dataset annotated with social behaviours. PanAf-SBR extends PanAf500 with 100 additional videos covering 36,063 frames. These come with 81,096 annotations including bounding boxes, segmentation masks, intra-video identities, and seven social behaviour classes defined under the action giver and receiver convention of ChimpACT. We use this data together with the AlphaChimp architecture to establish the first benchmarks for fine-grained social behaviour recognition in wild great apes from camera trap footage. We further conduct bidirectional transfer learning experiments between PanAf-SBR and the captive ChimpACT dataset, finding that cross-dataset pre-training is highly beneficial for specific classes rather than of uniform benefit. Finally, we examine the role of background context by inverting the segmentation masks to suppress non-ape pixels.
