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arXiv · 2408.09919

Long-Tail Temporal Action Segmentation with Group-wise Temporal Logit Adjustment

Zhanzhong Pang·Fadime Sener·Shrinivas Ramasubramanian·Angela Yao
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Abstract

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.

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Zhanzhong Pang, Fadime Sener, Shrinivas Ramasubramanian, Angela Yao. 2024-08-19. Long-Tail Temporal Action Segmentation with Group-wise Temporal Logit Adjustment. https://arxiv.org/abs/2408.09919

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