Long-Tail Temporal Action Segmentation with Group-wise Temporal Logit Adjustment
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914915855368192 |
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| author | Pang, Zhanzhong Sener, Fadime Ramasubramanian, Shrinivas Yao, Angela |
| author_facet | Pang, Zhanzhong Sener, Fadime Ramasubramanian, Shrinivas Yao, Angela |
| contents | Procedural activity videos often exhibit a long-tailed action distribution due to varying action frequencies and durations. However, state-of-the-art temporal action segmentation methods overlook the long tail and fail to recognize tail actions. Existing long-tail methods make class-independent assumptions and struggle to identify tail classes when applied to temporal segmentation frameworks. This work proposes a novel group-wise temporal logit adjustment~(G-TLA) framework that combines a group-wise softmax formulation while leveraging activity information and action ordering for logit adjustment. The proposed framework significantly improves in segmenting tail actions without any performance loss on head actions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_09919 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Long-Tail Temporal Action Segmentation with Group-wise Temporal Logit Adjustment Pang, Zhanzhong Sener, Fadime Ramasubramanian, Shrinivas Yao, Angela Computer Vision and Pattern Recognition Procedural activity videos often exhibit a long-tailed action distribution due to varying action frequencies and durations. However, state-of-the-art temporal action segmentation methods overlook the long tail and fail to recognize tail actions. Existing long-tail methods make class-independent assumptions and struggle to identify tail classes when applied to temporal segmentation frameworks. This work proposes a novel group-wise temporal logit adjustment~(G-TLA) framework that combines a group-wise softmax formulation while leveraging activity information and action ordering for logit adjustment. The proposed framework significantly improves in segmenting tail actions without any performance loss on head actions. |
| title | Long-Tail Temporal Action Segmentation with Group-wise Temporal Logit Adjustment |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.09919 |