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 …

Contrastive learning-based video-language representation learning approaches, eg, CLIP,
have achieved outstanding performance, which pursue semantic interaction upon pre …

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

Text-video retrieval is a challenging cross-modal task, which aims to align visual entities with
natural language descriptions. Current methods either fail to leverage the local details or are …

Video-text retrieval is an emerging stream in both computer vision and natural language
processing communities, which aims to find relevant videos given text queries. In this paper …

Cross-modal hashing hasrecently gained an increasing attention for its efficiency and fast
retrieval speed in indexing the multimedia data across different modalities. Nevertheless, the …

While Transformers have rapidly gained popularity in various computer vision applications
post-hoc explanations of their internal mechanisms remain largely unexplored. Vision …

Although deep cross-modal hashing methods have shown superiorities for cross-modal
retrieval recently, there is a concern about potential data privacy leakage when training the …

How to effectively handle label noise has been one of the most practical but challenging
tasks in Deep Neural Networks (DNNs). Recent popular methods for training DNNs with …

Localizing a desired moment within an untrimmed video via a given natural language query,
ie, cross-modal moment localization, has attracted widespread research attention recently …