VLCap: Vision-Language with Contrastive Learning for Coherent Video Paragraph Captioning

Published:

PWC

Abstract

In this paper, we leverage the human perceiving process, that involves vision and language interaction, to generate a coherent paragraph description of untrimmed videos. We propose vision-language (VL) features consisting of two modalities, i.e., (i) vision modality to capture global visual content of the entire scene and (ii) language modality to extract scene elements description of both human and non-human objects (e.g. animals, vehicles, etc), visual and non-visual elements (e.g. relations, activities, etc). Furthermore, we propose to train our proposed VLCap under a contrastive learning VL loss. The experiments and ablation studies on ActivityNet Captions and YouCookII datasets show that our VLCap outperforms existing SOTA methods on both accuracy and diversity metrics.

Citation

@ARTICLE{2022arXiv220612972Y,
       author = {Yamazaki, Kashu and Truong, Sang and Vo, Khoa and Kidd, Michael and Rainwater, Chase and Luu, Khoa and Le, Ngan},
        title = "{VLCap: Vision-Language with Contrastive Learning for Coherent Video Paragraph Captioning}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Computer Vision and Pattern Recognition},
         year = 2022,
        month = jun,
          eid = {arXiv:2206.12972},
        pages = {arXiv:2206.12972},
archivePrefix = {arXiv},
       eprint = {2206.12972},
 primaryClass = {cs.CV},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv220612972Y},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}