Retrieval Enhanced Zero-Shot Video Captioning
<|reference_start|>Retrieval Enhanced Zero-Shot Video Captioning: Despite the significant progress of fully-supervised video captioning, zero-shot methods remain much less explored. In this paper, we propose to take advantage of existing pre-trained large-scale vision and language models to directly generate captions with test time adaptation. Specifically, we bridge video and text using three key models: a general video understanding model XCLIP, a general image understanding model CLIP, and a text generation model GPT-2, due to their source-code availability. The main challenge is how to enable the text generation model to be sufficiently aware of the content in a given video so as to generate corresponding captions. To address this problem, we propose using learnable tokens as a communication medium between frozen GPT-2 and frozen XCLIP as well as frozen CLIP. Differing from the conventional way to train these tokens with training data, we update these tokens with pseudo-targets of the inference data under several carefully crafted loss functions which enable the tokens to absorb video information catered for GPT-2. This procedure can be done in just a few iterations (we use 16 iterations in the experiments) and does not require ground truth data. Extensive experimental results on three widely used datasets, MSR-VTT, MSVD, and VATEX, show 4% to 20% improvements in terms of the main metric CIDEr compared to the existing state-of-the-art methods.<|reference_end|>
@article{ma2024retrieval,
    title={Retrieval Enhanced Zero-Shot Video Captioning},
    author={Yunchuan Ma, Laiyun Qing, Guorong Li, Yuankai Qi, Quan Z. Sheng,
  Qingming Huang},
    journal={arXiv preprint arXiv:2405.07046},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.07046},
    primaryClass={cs.CV}
}