Enriching Local and Global Contexts for Temporal Action Localization

• URL: http://arxiv.org/abs/2107.12960v1
• Date: Tue, 27 Jul 2021 17:25:40 GMT
• Title: Enriching Local and Global Contexts for Temporal Action Localization
• Authors: Zixin Zhu (Xi'an jiaotong University), Wei Tang (University of Illinois at Chicago), Le Wang (Xi'an Jiaotong University), Nanning Zheng (Xi'an Jiaotong University), Gang Hua (Wormpex AI Research)
• Abstract summary: We enrich both the local and global contexts in the popular two-stage temporal localization framework. Our proposed model, dubbed ContextLoc, can be divided into three sub-networks: L-Net, G-Net and P-Net. The efficacy of our proposed method is validated by experimental results on the THUMOS14 (54.3% at IoU@0.5) and ActivityNet v1.3 (51.24% at IoU@0.5) datasets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Effectively tackling the problem of temporal action localization (TAL) necessitates a visual representation that jointly pursues two confounding goals, i.e., fine-grained discrimination for temporal localization and sufficient visual invariance for action classification. We address this challenge by enriching both the local and global contexts in the popular two-stage temporal localization framework, where action proposals are first generated followed by action classification and temporal boundary regression. Our proposed model, dubbed ContextLoc, can be divided into three sub-networks: L-Net, G-Net and P-Net. L-Net enriches the local context via fine-grained modeling of snippet-level features, which is formulated as a query-and-retrieval process. G-Net enriches the global context via higher-level modeling of the video-level representation. In addition, we introduce a novel context adaptation module to adapt the global context to different proposals. P-Net further models the context-aware inter-proposal relations. We explore two existing models to be the P-Net in our experiments. The efficacy of our proposed method is validated by experimental results on the THUMOS14 (54.3\% at IoU@0.5) and ActivityNet v1.3 (51.24\% at IoU@0.5) datasets, which outperforms recent states of the art.

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