Long-Tail Temporal Action Segmentation with Group-wise Temporal Logit Adjustment
<|reference_start|>Long-Tail Temporal Action Segmentation with Group-wise Temporal Logit Adjustment: 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.<|reference_end|>
@article{pang2024long-tail,
    title={Long-Tail Temporal Action Segmentation with Group-wise Temporal Logit
  Adjustment},
    author={Zhanzhong Pang, Fadime Sener, Shrinivas Ramasubramanian, Angela Yao},
    journal={arXiv preprint arXiv:2408.09919},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09919},
    primaryClass={cs.CV}
}