Exploring large-scale pretrained foundation models is of significant interest in computer
vision because these models can be quickly transferred to many downstream tasks. This …

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 …

We present VideoCLIP, a contrastive approach to pre-train a unified model for zero-shot
video and text understanding, without using any labels on downstream tasks. VideoCLIP …

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 …

Disentangled Representation Learning (DRL) aims to learn a model capable of identifying
and disentangling the underlying factors hidden in the observable data in representation …

Contrastive language image pretraining (CLIP) encoders have been shown to be beneficial
for a range of visual tasks from classification and detection to captioning and image …

In text-video retrieval, the objective is to learn a cross-modal similarity function between a
text and a video that ranks relevant text-video pairs higher than irrelevant pairs. However …

Pre-training a model to learn transferable video-text representation for retrieval has attracted
a lot of attention in recent years. Previous dominant works mainly adopt two separate …

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 …