Symmetric Multi-Similarity Loss for EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2024
<|reference_start|>Symmetric Multi-Similarity Loss for EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2024: In this report, we present our champion solution for EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge in CVPR 2024. Essentially, this challenge differs from traditional visual-text retrieval tasks by providing a correlation matrix that acts as a set of soft labels for video-text clip combinations. However, existing loss functions have not fully exploited this information. Motivated by this, we propose a novel loss function, Symmetric Multi-Similarity Loss, which offers a more precise learning objective. Together with tricks and ensemble learning, the model achieves 63.76% average mAP and 74.25% average nDCG on the public leaderboard, demonstrating the effectiveness of our approach. Our code will be released at: https://github.com/xqwang14/SMS-Loss/tree/main<|reference_end|>
@article{wang2024symmetric,
    title={Symmetric Multi-Similarity Loss for EPIC-KITCHENS-100 Multi-Instance
  Retrieval Challenge 2024},
    author={Xiaoqi Wang, Yi Wang, Lap-Pui Chau},
    journal={arXiv preprint arXiv:2406.12256},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12256},
    primaryClass={cs.CV}
}