PCG-TAL : Progressive Cross-Granularity Cooperation for Temporal Action Localization
There are two major lines of works, i.e., anchor-based and frame-based approaches, in the field of temporal action localization. But each line of works is inherently limited to a certain detection granularity and cannot simultaneously achieve high recall rates with accurate action boundaries. In thi...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 30(2021) vom: 07., Seite 2103-2113 |
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| Weitere Verfasser: | , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2021
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Schlagworte: | Journal Article |
| Zusammenfassung: | There are two major lines of works, i.e., anchor-based and frame-based approaches, in the field of temporal action localization. But each line of works is inherently limited to a certain detection granularity and cannot simultaneously achieve high recall rates with accurate action boundaries. In this work, we propose a progressive cross-granularity cooperation (PCG-TAL) framework to effectively take advantage of complementarity between the anchor-based and frame-based paradigms, as well as between two-view clues (i.e., appearance and motion). Specifically, our new Anchor-Frame Cooperation (AFC) module can effectively integrate both two-granularity and two-stream knowledge at the feature and proposal levels, as well as within each AFC module and across adjacent AFC modules. Specifically, the RGB-stream AFC module and the flow-stream AFC module are stacked sequentially to form a progressive localization framework. The whole framework can be learned in an end-to-end fashion, whilst the temporal action localization performance can be gradually boosted in a progressive manner. Our newly proposed framework outperforms the state-of-the-art methods on three benchmark datasets the THUMOS14, ActivityNet v1.3 and UCF-101-24, which clearly demonstrates the effectiveness of our framework |
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| Beschreibung: | Date Revised 26.01.2021 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2020.3044218 |