Technical Report for ActivityNet Challenge 2022 -- Temporal Action Localization

• URL: http://arxiv.org/abs/2411.00883v1
• Date: Thu, 31 Oct 2024 14:16:56 GMT
• Title: Technical Report for ActivityNet Challenge 2022 -- Temporal Action Localization
• Authors: Shimin Chen, Wei Li, Jianyang Gu, Chen Chen, Yandong Guo,
• Abstract summary: We propose to locate the temporal boundaries of each action and predict action class in untrimmed videos. Faster-TAD simplifies the pipeline of TAD and gets remarkable performance.
• Score: 20.268572246761895
• License:
• Abstract: In the task of temporal action localization of ActivityNet-1.3 datasets, we propose to locate the temporal boundaries of each action and predict action class in untrimmed videos. We first apply VideoSwinTransformer as feature extractor to extract different features. Then we apply a unified network following Faster-TAD to simultaneously obtain proposals and semantic labels. Last, we ensemble the results of different temporal action detection models which complement each other. Faster-TAD simplifies the pipeline of TAD and gets remarkable performance, obtaining comparable results as those of multi-step approaches.

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