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Lorenzo Mur-Labadia, Ruben Martinez-Cantin, Jose J Guerrero

IEEE transactions on pattern analysis and machine intelligence

|January 12, 2026

概括

本研究介绍了STAformer和STAformer++用于使用基于注意力的模型改进短期对象交互预测 (STA). 该方法通过整合环境负担能力和交互热点来增强预测,提高基准数据集的性能.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
人与计算机的交互

背景情况:

短期对象交互预测 (STA) 对人工智能系统 (如可穿戴助手和机器人) 至关重要,以了解用户意图并及时提供支持.
目前的STA方法需要改进,以准确地从自我中心视频数据中预测对象交互.

研究的目的:

为了提高短期对象交互预测 (STA) 预测的性能.
引入新的基于注意力的架构和模块,以在人类行为和环境环境中为STA预测奠定基础.

主要方法:

拟议的STAformer和STAformer++:以注意力为基础的架构,具有框架导向的时间聚合,双图像视频注意力和多尺度特征融合.
引入了环境负担能力建模,用于场景内的持续交互记忆.
开发了适应性融合方法,以整合负担能力与端到端预测.
从手和物体轨迹进行综合交互热点预测,以完善STA本地化.

主要成果:

整体前五名mAP的显著改善,Ego4D的收益高达23%,精选的EPIC-Kitchens STA标签的收益高达31%.
证明了拟议的注意力架构和负担能力为基础的模块的有效性.

结论:

新的STAformer架构和基于负担能力的模块大大提升了短期对象交互预测.
发布的代码,注释和费用将促进未来对自我中心视频理解和人与人工智能交互的研究.

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