Multi-task Learning with Extended Temporal Shift Module for Temporal Action Localization
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arXiv
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915670987374592 |
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| author | Duong, Anh-Kiet Gomez-Krämer, Petra |
| author_facet | Duong, Anh-Kiet Gomez-Krämer, Petra |
| contents | We present our solution to the BinEgo-360 Challenge at ICCV 2025, which focuses on temporal action localization (TAL) in multi-perspective and multi-modal video settings. The challenge provides a dataset containing panoramic, third-person, and egocentric recordings, annotated with fine-grained action classes. Our approach is built on the Temporal Shift Module (TSM), which we extend to handle TAL by introducing a background class and classifying fixed-length non-overlapping intervals. We employ a multi-task learning framework that jointly optimizes for scene classification and TAL, leveraging contextual cues between actions and environments. Finally, we integrate multiple models through a weighted ensemble strategy, which improves robustness and consistency of predictions. Our method is ranked first in both the initial and extended rounds of the competition, demonstrating the effectiveness of combining multi-task learning, an efficient backbone, and ensemble learning for TAL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11189 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-task Learning with Extended Temporal Shift Module for Temporal Action Localization Duong, Anh-Kiet Gomez-Krämer, Petra Computer Vision and Pattern Recognition We present our solution to the BinEgo-360 Challenge at ICCV 2025, which focuses on temporal action localization (TAL) in multi-perspective and multi-modal video settings. The challenge provides a dataset containing panoramic, third-person, and egocentric recordings, annotated with fine-grained action classes. Our approach is built on the Temporal Shift Module (TSM), which we extend to handle TAL by introducing a background class and classifying fixed-length non-overlapping intervals. We employ a multi-task learning framework that jointly optimizes for scene classification and TAL, leveraging contextual cues between actions and environments. Finally, we integrate multiple models through a weighted ensemble strategy, which improves robustness and consistency of predictions. Our method is ranked first in both the initial and extended rounds of the competition, demonstrating the effectiveness of combining multi-task learning, an efficient backbone, and ensemble learning for TAL. |
| title | Multi-task Learning with Extended Temporal Shift Module for Temporal Action Localization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.11189 |