Technical Report for ActivityNet Challenge 2022 -- Temporal Action Localization

Autor:	Chen, Shimin, Li, Wei, Gu, Jianyang, Chen, Chen, Guo, Yandong
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: arXiv admin note: substantial text overlap with arXiv:2204.02674
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.00883 Zobrazit plný text záznamu View this record from Arxiv