ELAN:通过位置感知增强时间动作检测

Guo Chen, Yin-Dong Zheng, Zhe Chen, Jiahao Wang, Tong Lu
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引用次数: 0

摘要

当前基于查询的时间动作检测方法缺乏多层次的位置感知,导致性能下降。在本文中,我们提出了一种新的基于查询的方法,称为增强位置感知网络(ELAN),用于时间动作检测。ELAN采用了一种轻量级的基于卷积的编码器,称为时间位置感知(TLA)编码器,用于模拟时间连续位置感知上下文。此外,ELAN可以通过我们提出的实例位置感知(Instance Location-Aware, ILA)解码器重新感知查询内部和查询之间的位置相关上下文。因此,ELAN可以学习到动作的强位置判别,有效地消除了稀疏动作解码带来的歧义,检测性能得到了显著提高。ELAN在两个时间动作检测基准上实现了最先进的性能,包括THUMOS-14和ActivityNet-1.3。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ELAN: Enhancing Temporal Action Detection with Location Awareness
Current query-based temporal action detection methods lack multiple levels of location awareness, leading to performance degradation. In this paper, we present a novel query-based method called Enhanced Location-Aware Network (ELAN) for temporal action detection. ELAN adopts a lightweight convolution-based encoder, termed Temporal Location-Aware (TLA) encoder, to model temporal continuous location-aware context. Moreover, ELAN can re-aware the location-related context inside and between queries through our proposed Instance Location-Aware (ILA) decoder. As a result, ELAN can learn strong position discrimination of actions and effectively eliminates the ambiguity caused by sparse action decoding, yielding significant improvement in detection performance. ELAN achieves state-of-the-art performance on two temporal action detection benchmarks, including THUMOS-14 and ActivityNet-1.3.
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