Spatio-temporal Prompting Network for Robust Video Feature Extraction
<|reference_start|>Spatio-temporal Prompting Network for Robust Video Feature Extraction: Frame quality deterioration is one of the main challenges in the field of video understanding. To compensate for the information loss caused by deteriorated frames, recent approaches exploit transformer-based integration modules to obtain spatio-temporal information. However, these integration modules are heavy and complex. Furthermore, each integration module is specifically tailored for its target task, making it difficult to generalise to multiple tasks. In this paper, we present a neat and unified framework, called Spatio-Temporal Prompting Network (STPN). It can efficiently extract robust and accurate video features by dynamically adjusting the input features in the backbone network. Specifically, STPN predicts several video prompts containing spatio-temporal information of neighbour frames. Then, these video prompts are prepended to the patch embeddings of the current frame as the updated input for video feature extraction. Moreover, STPN is easy to generalise to various video tasks because it does not contain task-specific modules. Without bells and whistles, STPN achieves state-of-the-art performance on three widely-used datasets for different video understanding tasks, i.e., ImageNetVID for video object detection, YouTubeVIS for video instance segmentation, and GOT-10k for visual object tracking. Code is available at https://github.com/guanxiongsun/vfe.pytorch.<|reference_end|>
@article{sun2024spatio-temporal,
    title={Spatio-temporal Prompting Network for Robust Video Feature Extraction},
    author={Guanxiong Sun, Chi Wang, Zhaoyu Zhang, Jiankang Deng, Stefanos
  Zafeiriou, Yang Hua},
    journal={2023 International Conference on Computer Vision (ICCV)
  13541-13551},
    year={2024},
    doi={10.1109/ICCV51070.2023.01250},
    archivePrefix={arXiv},
    eprint={2402.02574},
    primaryClass={cs.CV cs.LG}
}