Abstract Video-Text Retrieval has been a hot research topic with the growth of multimedia
data on the internet. Transformer for video-text learning has attracted increasing attention …

Almost all previous text-to-video retrieval works assume that videos are pre-trimmed with
short durations. However, in practice, videos are generally untrimmed containing much …

This paper aims for the task of text-to-video retrieval, where given a query in the form of a
natural-language sentence, it is asked to retrieve videos which are semantically relevant to …

Current methods for text-to-video retrieval (T2VR) are trained and tested on video-captioning
oriented datasets such as MSVD, MSR-VTT and VATEX. A key property of these datasets is …

Cross-modal text to video retrieval aims to find relevant videos given text queries, which is
crucial for various real-world applications. The key to address this task is to build the …

Video-text retrieval is an important yet challenging task in vision-language understanding,
which aims to learn a joint embedding space where related video and text instances are …

Abstract Visual Semantic Embedding (VSE) is a dominant method for vision-language
retrieval. Its purpose is to learn an embedding space so that visual data can be embedded in …

Due to the popularity of video contents on the Internet, the information retrieval between
videos and texts has attracted broad interest from researchers, which is a challenging cross …

With the current exponential growth of video-based social networks, video retrieval using
natural language is receiving ever-increasing attention. Most existing approaches tackle this …

Cross-modal retrieval aims to enable a flexible bi-directional retrieval experience across
different modalities (eg, searching for videos with texts). Many existing efforts tend to learn a …