{
  "abstract": "Temporal Action Localization (TAL) aims to identify all action categories along with their precise temporal boundaries in untrimmed videos. It serves as a fundamental task supporting various downstream applications, including intelligent video editing, automatic summarization, anomaly detection, and human–computer interaction. Despite significant progress, TAL in real-world scenarios remains challenging due to two critical factors: substantial intra-class variations in action duration, ranging from a few seconds to several minutes, and severe degradation of boundary localization accuracy caused by variations in illumination, background clutter, and camera viewpoints. To address these issues, this paper presents a Temporal Action Localization with Class-Guided Boundary Regression (TAL-CGBR) framework, which incorporates semantic information from the classification branch into the regression network to enhance boundary precision. In addition, a window-based attention mechanism and a temporal hierarchical feature structure are introduced to improve computational efficiency while maintaining high localization accuracy. Experimental evaluations on benchmark datasets demonstrate that the proposed TAL-CGBR achieves competitive performance, attaining 68.5% average mAP on THUMOS14 and 36.7% average mAP on ActivityNet v1.3, ranking second among recent methods and demonstrating a favorable balance between accuracy and computational efficiency.",
  "abstract_shown": true,
  "affiliations": [
    "School of Computer and Computing Science, Hangzhou City University, Hangzhou, China",
    "College of Computer Science and Technology, Zhejiang University, Hangzhou, China",
    "Department of Physical Education and Aesthetic Education, Hangzhou City University, Hangzhou, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
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        0
      ],
      "name": "Jinling Wei"
    },
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        1
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      "name": "Haolong Chai"
    },
    {
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        2
      ],
      "name": "Wenwu Hou"
    },
    {
      "name": "Hongwen Yuan"
    },
    {
      "name": "Xiao Huang"
    },
    {
      "name": "Sufen Huang"
    }
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  "doi": "10.1371/journal.pone.0360160",
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  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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      "openalex",
      "1707|Human Pose and Action Recognition"
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      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Attention"
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      "/Biology and life sciences/Psychology/Cognitive psychology/Learning"
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  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0360160&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/dmg5jnxkbmdbf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:30Z",
      "native_id": "10.1371/journal.pone.0360160",
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      "url": "https://doi.org/10.1371/journal.pone.0360160"
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  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
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  "title": "TAL-CGBR: A class-guided boundary regression approach for Temporal Action Localization",
  "title_text": "TAL-CGBR: A class-guided boundary regression approach for Temporal Action Localization",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:30Z",
  "url": "https://research.draconitic.com/paper/dmg5jnxkbmdbf",
  "venue": "PLOS One",
  "versions": []
}