In the past few years, the emergence of pre-training models has brought uni-modal fields
such as computer vision (CV) and natural language processing (NLP) to a new era …

With the exponential surge in diverse multimodal data, traditional unimodal retrieval
methods struggle to meet the needs of users seeking access to data across various …

Large pretrained (eg," foundation") models exhibit distinct capabilities depending on the
domain of data they are trained on. While these domains are generic, they may only barely …

Our objective in this work is video-text retrieval-in particular a joint embedding that enables
efficient text-to-video retrieval. The challenges in this area include the design of the visual …

Video clip retrieval and captioning tasks play an essential role in multimodal research and
are the fundamental research problem for multimodal understanding and generation. The …

Video-text retrieval has been a crucial and fundamental task in multi-modal research. The
development of video-text retrieval has been considerably promoted by large-scale multi …

We present CLIP2Video network to transfer the image-language pre-training model to video-
text retrieval in an end-to-end manner. Leading approaches in the domain of video-and …

Video-text retrieval plays an essential role in multi-modal research and has been widely
used in many real-world web applications. The CLIP (Contrastive Language-Image Pre …

The task of retrieving video content relevant to natural language queries plays a critical role
in effectively handling internet-scale datasets. Most of the existing methods for this caption-to …

Videos are a rich source of multi-modal supervision. In this work, we learn representations
using self-supervision by leveraging three modalities naturally present in videos: visual …