Abstract
We present our solution to the BinEgo-360 Challenge at ICCV 2025, which focuses on temporal action localization (TAL) in multi-perspective and multi-modal video settings. The challenge provides a dataset containing panoramic, third-person, and egocentric recordings, annotated with fine-grained action classes. Our approach is built on the Temporal Shift Module (TSM), which we extend to handle TAL by introducing a background class and classifying fixed-length non-overlapping intervals. We employ a multi-task learning framework that jointly optimizes for scene classification and TAL, leveraging contextual cues between actions and environments. Finally, we integrate multiple models through a weighted ensemble strategy, which improves robustness and consistency of predictions. Our method is ranked first in both the initial and extended rounds of the competition, demonstrating the effectiveness of combining multi-task learning, an efficient backbone, and ensemble learning for TAL.
Abstract (translated)
我们介绍了针对2025年ICCV BinEgo-360挑战赛的解决方案，该挑战专注于多视角和多模态视频环境下的时序动作定位（TAL）。此次比赛提供的数据集包括全景、第三人称和第一人称视角的记录，并详细标注了细粒度的动作类别。我们的方法基于时间偏移模块（Temporal Shift Module, TSM），并将其扩展以处理TAL，通过引入背景类以及对固定长度且不重叠的时间间隔进行分类来实现这一点。我们采用了一个多任务学习框架，在该框架中同时优化场景分类和TAL的性能，并利用动作与环境之间的上下文线索。最后，我们通过加权集成策略整合多个模型，这提升了预测结果的鲁棒性和一致性。 我们的方法在竞赛初始阶段以及扩展阶段均获得第一名，证明了将多任务学习、高效的骨干网络（backbone）及集成学习相结合，对于解决TAL问题的有效性。
URL
https://arxiv.org/abs/2512.11189
