SMC-NCA: Semantic-Guided Multi-Level Contrast for Semi-Supervised Temporal Action Segmentation

IF 8.4 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Feixiang Zhou;Zheheng Jiang;Huiyu Zhou;Xuelong Li
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引用次数: 0

Abstract

Semi-supervised temporal action segmentation (SS-TAS) aims to perform frame-wise classification in long untrimmed videos, where only a fraction of videos in the training set have labels. Recent studies have shown the potential of contrastive learning in unsupervised representation learning using unlabelled data. However, learning the representation of each frame by unsupervised contrastive learning for action segmentation remains an open and challenging problem. In this paper, we propose a novel Semantic-guided Multi-level Contrast scheme with a Neighbourhood-Consistency-Aware unit (SMC-NCA) to extract strong frame-wise representations for SS-TAS. Specifically, for representation learning, SMC is first used to explore intra- and inter-information variations in a unified and contrastive way, based on action-specific semantic information and temporal information highlighting relations between actions. Then, the NCA module, which is responsible for enforcing spatial consistency between neighbourhoods centered at different frames to alleviate over-segmentation issues, works alongside SMC for semi-supervised learning (SSL). Our SMC outperforms the other state-of-the-art methods on three benchmarks, offering improvements of up to 17.8 $\%$ and 12.6 $\%$ in terms of Edit distance and accuracy, respectively. Additionally, the NCA unit results in significantly better segmentation performance in the presence of only 5 $\%$ labelled videos. We also demonstrate the generalizability and effectiveness of the proposed method on our Parkinson's Disease Mouse Behaviour (PDMB) dataset.
SMC-NCA:用于半监督时态动作分割的语义引导多级对比技术
半监督时间动作分割(SS-TAS)旨在对未经剪辑的长视频进行按帧分类,在这种情况下,训练集中只有一小部分视频有标签。最近的研究表明,对比学习在使用无标签数据进行无监督表示学习方面具有潜力。然而,通过无监督对比学习来学习每帧动作分割的表示仍然是一个开放且具有挑战性的问题。在本文中,我们提出了一种新颖的语义引导多级对比方案,该方案具有邻域一致性感知单元(SMC-NCA),可为 SS-TAS 提取强帧表示。具体来说,在表征学习方面,首先使用 SMC 以统一和对比的方式,根据特定动作的语义信息和突出动作间关系的时间信息,探索信息内和信息间的变化。然后,NCA 模块与 SMC 一起进行半监督学习 (SSL),NCA 模块负责在以不同帧为中心的邻域之间执行空间一致性,以缓解过度分割问题。在三个基准测试中,我们的 SMC 优于其他最先进的方法,在 Edit 距离和准确度方面分别提高了 17.8%$ 和 12.6%$。此外,NCA 单元在仅有 5%$ 标记视频的情况下也能显著提高分割性能。我们还在帕金森病小鼠行为(PDMB)数据集上证明了所提方法的通用性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Multimedia
IEEE Transactions on Multimedia 工程技术-电信学
CiteScore
11.70
自引率
11.00%
发文量
576
审稿时长
5.5 months
期刊介绍: The IEEE Transactions on Multimedia delves into diverse aspects of multimedia technology and applications, covering circuits, networking, signal processing, systems, software, and systems integration. The scope aligns with the Fields of Interest of the sponsors, ensuring a comprehensive exploration of research in multimedia.
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