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• W4386324939 abstract "Temporal action detection is a very important and challenging task in the field of video understanding, especially for datasets with significant differences in action duration. The temporal relationships between the action instances contained in these datasets are very complex. For such videos, it is necessary to capture information with a richer temporal distribution as much as possible. In this paper, we propose a dual-stream model that can model contextual information at multiple temporal scales. First, the input video is divided into two resolution streams, followed by a Multi-Resolution Context Aggregation module to capture multi-scale temporal information. Additionally, an Information Enhancement module is added after the high-resolution input stream to model both long-range and short-range contexts. Finally, the outputs of the two modules are merged to obtain features with rich temporal information for action localization and classification. We conducted experiments on three datasets to evaluate the proposed approach. On ActivityNet-v1.3, an average mAP (mean Average Precision) of 32.83% was obtained. On Charades, the best performance was obtained, with an average mAP of 27.3%. On TSU (Toyota Smarthome Untrimmed), an average mAP of 33.1% was achieved." @default.
• W4386324939 created "2023-09-01" @default.
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• W4386324939 date "2023-08-31" @default.
• W4386324939 modified "2023-09-27" @default.
• W4386324939 title "MCMNET: Multi-Scale Context Modeling Network for Temporal Action Detection" @default.
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• W4386324939 doi "https://doi.org/10.3390/s23177563" @default.
• W4386324939 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37688018" @default.
• W4386324939 hasPublicationYear "2023" @default.
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