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 …

To meet users' demands for video retrieval, text-video cross-modal retrieval technology
continues to evolve. Methods based on pre-trained models and transfer learning are widely …

Multimodal image–text classification endeavors to deduce the correct category based on the
information encapsulated in image–text pairs. Despite the commendable performance …

Recent works in 3D multimodal learning have made remarkable progress. However,
typically 3D multimodal models are only capable of handling point clouds. Compared to the …

Video question answering (VideoQA) is the challenging task of accurately responding to
natural language questions based on a given video. Most previous methods focus on …

Text-video retrieval aims to establish a matching relationship between a video and its
corresponding text. However, previous works have primarily focused on salient video …

Text-based person retrieval aims to match a specific pedestrian image with textual
descriptions. Traditional approaches have largely focused on utilizing a “single-shot” query …

Compositional temporal grounding (CTG) aims to localize the most relevant segment from
an untrimmed video based on a given natural language sentence, and the test samples for …

Cross-modal retrieval is crucial in understanding latent correspondences across modalities.
However, existing methods implicitly assume well-matched training data, which is …

Adapting large-scale image-text pre-training models, eg, CLIP, to the video domain
represents the current state-of-the-art for text-video retrieval. The primary approaches …