Contrastive learning-based video-language representation learning approaches, eg, CLIP,
have achieved outstanding performance, which pursue semantic interaction upon pre …

Most video-and-language representation learning approaches employ contrastive learning,
eg, CLIP, to project the video and text features into a common latent space according to the …

Video captioning (VC) is a fast-moving, cross-disciplinary area of research that bridges work
in the fields of computer vision, natural language processing (NLP), linguistics, and human …

Vision-language alignment learning for video-text retrieval arouses a lot of attention in
recent years. Most of the existing methods either transfer the knowledge of image-text …

Despite the recent developments in the field of cross-modal retrieval, there has been less
research focusing on low-resource languages due to the lack of manually annotated …

Applying large scale pre-trained image-language model to video-language tasks has
recently become a trend, which brings two challenges. One is how to effectively transfer …

In this work, we present a post-processing solution to address the hubness problem in cross-
modal retrieval, a phenomenon where a small number of gallery data points are frequently …

Video-text retrieval (VTR) is an attractive yet challenging task for multi-modal understanding,
which aims to search for relevant video (text) given a query (video). Existing methods …

Generating consecutive descriptions for videos, that is, video captioning, requires taking full
advantage of visual representation along with the generation process. Existing video …

Cross-modal text-to-video retrieval aims to find semantically related videos for a text query.
Since video and text are distinct modalities, the major challenge comes from building the …