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

Zhengwei Shen (), Ran Xu, Yongquan Zhang, Feiwei Qin, Ruiquan Ge, Changmiao Wang and Masahiro Toyoura
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Zhengwei Shen: School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
Ran Xu: School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
Yongquan Zhang: School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou 310018, China
Feiwei Qin: School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
Ruiquan Ge: School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
Changmiao Wang: Medical Big Data Lab, Shenzhen Research Institute of Big Data, Shenzhen 518172, China
Masahiro Toyoura: Department of Computer Science and Engineering, University of Yamanashi, Kofu 400-8511, Japan

Mathematics, 2024, vol. 12, issue 6, 1-18

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 B oundary- M atch U -shaped T emporal C onvolutional N etwork (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.

Keywords: vulgar action segmentation; boundary-match; U-shaped network; temporal convolutional network; adaptive internal block suppression (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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