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 …

Video recognition has been dominated by the end-to-end learning paradigm–first initializing
a video recognition model with weights of a pretrained image model and then conducting …

Text-Video retrieval is a task of great practical value and has received increasing attention,
among which learning spatial-temporal video representation is one of the research hotspots …

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

Most existing text-video retrieval methods focus on cross-modal matching between the
visual content of videos and textual query sentences. However, in real-world scenarios …

The last several years have witnessed remarkable progress in video-and-language (VidL)
understanding. However, most modern VidL approaches use complex and specialized …

The pre-trained image-text models, like CLIP, have demonstrated the strong power of vision-
language representation learned from a large scale of web-collected image-text data. In light …

We study open-world 3D scene understanding, a family of tasks that require agents to
reason about their 3D environment with an open-set vocabulary and out-of-domain visual …

Abstract Video-Text Retrieval (VTR) aims to search for the most relevant video related to the
semantics in a given sentence, and vice versa. In general, this retrieval task is composed of …

Image-text pretrained models, eg, CLIP, have shown impressive general multi-modal
knowledge learned from large-scale image-text data pairs, thus attracting increasing …