CARLOR @ Ego4D Step Grounding Challenge: Bayesian temporal-order priors for test time refinement
<|reference_start|>CARLOR @ Ego4D Step Grounding Challenge: Bayesian temporal-order priors for test time refinement: The goal of the Step Grounding task is to locate temporal boundaries of activities based on natural language descriptions. This technical report introduces a Bayesian-VSLNet to address the challenge of identifying such temporal segments in lengthy, untrimmed egocentric videos. Our model significantly improves upon traditional models by incorporating a novel Bayesian temporal-order prior during inference, enhancing the accuracy of moment predictions. This prior adjusts for cyclic and repetitive actions within videos. Our evaluations demonstrate superior performance over existing methods, achieving state-of-the-art results on the Ego4D Goal-Step dataset with a 35.18 Recall Top-1 at 0.3 IoU and 20.48 Recall Top-1 at 0.5 IoU on the test set.<|reference_end|>
@article{plou2024carlor,
    title={CARLOR @ Ego4D Step Grounding Challenge: Bayesian temporal-order priors
  for test time refinement},
    author={Carlos Plou, Lorenzo Mur-Labadia, Ruben Martinez-Cantin and Ana
  C.Murillo},
    journal={arXiv preprint arXiv:2406.09575},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.09575},
    primaryClass={cs.CV}
}