In recent years, considerable progress on the task of text-video retrieval has been achieved
by leveraging large-scale pretraining on visual and audio datasets to construct powerful …

Profiting from large-scale training datasets, advances in neural architecture design and
efficient inference, joint embeddings have become the dominant approach for tackling cross …

Cross-modal retrieval methods are the preferred tool to search databases for the text that
best matches a query image and vice versa However, image-text retrieval models commonly …

Video indexing and retrieval have a wide spectrum of promising applications, motivating the
interest of researchers worldwide. This paper offers a tutorial and an overview of the …

This paper addresses the problem of similar image retrieval, especially in the setting of large-
scale datasets with millions to billions of images. The core novel contribution is an approach …

Weakly supervised temporal sentence grounding has better scalability and practicability
than fully supervised methods in real-world application scenarios. However, most of existing …

The goal of high-level event recognition is to automatically detect complex high-level events
in a given video sequence. This is a difficult task especially when videos are captured under …

In this paper, we tackle the problem of retrieving videos using complex natural language
queries. Towards this goal, we first parse the sentential descriptions into a semantic graph …

Current state-of-the-art systems for visual content analysis require large training sets for
each class of interest, and performance degrades rapidly with fewer examples. In this paper …

The thriving of video sharing services brings new challenges to video retrieval, eg the rapid
growth in video duration and content diversity. Meeting such challenges calls for new …