COSA: Concatenated Sample Pretrained Vision-Language Foundation Model
Authors: Sihan Chen,Xingjian He,Handong Li,Xiaojie Jin,Jiashi Feng,Jing Liu
Presentation: ICLR 2024,Poster

Abstract: Due to the limited scale and quality of video-text training corpus, most  vision-language  foundation  models employ  image-text datasets for pretraining and primarily focus on modeling visually semantic representations while disregarding temporal semantic representations and correlations. To address this issue, we propose COSA, a COncatenated SAmple pretrained vision-language foundation model. COSA can jointly model visual contents and event-level temporal cues using only image-text corpora.  We achieve this by sequentially concatenating multiple image-text pairs as inputs for pretraining. This transformation effectively converts existing image-text corpora into a pseudo  video-paragraph corpus, enabling richer scene transformations and explicit event-description correspondence. Extensive experiments demonstrate that COSA consistently improves performance across a broad range of semantic vision-language downstream tasks, including paragraph-to-video retrieval, text-to-video/image retrieval, video/image captioning and video QA. Notably, COSA achieves state-of-the-art results on various competitive benchmarks. Code and model are released at https://github.com/TXH-mercury/COSA.