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.
@article{arxiv.2408.09919,
title = {Long-Tail Temporal Action Segmentation with Group-wise Temporal Logit Adjustment},
author = {Zhanzhong Pang and Fadime Sener and Shrinivas Ramasubramanian and Angela Yao},
journal= {arXiv preprint arXiv:2408.09919},
year = {2024}
}