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Mathematics, volume 12, issue 6, pages 899

Boundary-Match U-Shaped Temporal Convolutional Network for Vulgar Action Segmentation

Publication typeJournal Article
Publication date2024-03-18
Journal: Mathematics
scimago Q2
SJR0.475
CiteScore4.0
Impact factor2.3
ISSN22277390
General Mathematics
Computer Science (miscellaneous)
Engineering (miscellaneous)
Abstract

The advent of deep learning has provided solutions to many challenges posed by the Internet. However, efficient localization and recognition of vulgar segments within videos remain formidable tasks. This difficulty arises from the blurring of spatial features in vulgar actions, which can render them indistinguishable from general actions. Furthermore, issues of boundary ambiguity and over-segmentation complicate the segmentation of vulgar actions. To address these issues, we present the Boundary-Match U-shaped Temporal Convolutional Network (BMUTCN), a novel approach for the segmentation of vulgar actions. The BMUTCN employs a U-shaped architecture within an encoder–decoder temporal convolutional network to bolster feature recognition by leveraging the context of the video. Additionally, we introduce a boundary-match map that fuses action boundary inform ation with greater precision for frames that exhibit ambiguous boundaries. Moreover, we propose an adaptive internal block suppression technique, which substantially mitigates over-segmentation errors while preserving accuracy. Our methodology, tested across several public datasets as well as a bespoke vulgar dataset, has demonstrated state-of-the-art performance on the latter.

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