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- W4377692982 abstract "Temporal Action Proposal Generation (TAPG) is a challenging task at present, which aims to accurately generate temporal proposals that are likely to contain human actions in an untrimmed video. Inspired by the successful pre-training strategy applied by the transformer-based image object detection method, we propose a Self-Supervised Pre-Training transformer (SSPT) method for the TAPG task to train an action locator without any labels. As far as we know, this is the first work to explore self-supervised pre-training for transformer for TAPG. Specifically, the pre-training strategy leverages the transformer model to model the contextual information and we leverage transformer to capture contextual semantic information and global temporal dependencies of actions in the entire video can better accomplish the TAPG task. And in the pre-training process, we adapt Random Query Segments, which randomly crop multiple segments from the original video. We treat the segments as pseudo-labels and input them as queries to the transformer decoder. The pretext we designed is to locate the start time and end time of the pseudo-labels in the original video. Extensive experiments on THUMOS14 demonstrate the effectiveness of SSPT, the results show that it improves the performance of baseline and leads to a higher recall for the Temporal Action Proposal Generation task." @default.
- W4377692982 created "2023-05-24" @default.
- W4377692982 creator A5029696367 @default.
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- W4377692982 date "2023-05-23" @default.
- W4377692982 modified "2023-10-16" @default.
- W4377692982 title "Temporal action proposal generation with self-supervised pre-training transformer" @default.
- W4377692982 doi "https://doi.org/10.1117/12.2681096" @default.
- W4377692982 hasPublicationYear "2023" @default.
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