This work breaks through the Base-New Tradeoff (BNT) dilemma in prompt tuning ie the
better the tuned model generalizes to the base (or target) task the worse it generalizes to …

As a fundamental problem in multimodal learning multimodal fusion aims to compensate for
the inherent limitations of a single modality. One challenge of multimodal fusion is that the …

Text-based video retrieval is a well-studied task aimed at retrieving relevant videos from a
large collection in response to a given text query. Most existing TVR works assume that …

Video Moment Retrieval (VMR) aims at retrieving the most relevant events from an
untrimmed video with natural language queries. Existing VMR methods suffer from two …

Given an untrimmed video and a text query, Video Moment Retrieval (VMR) aims at
retrieving a specific moment where the video content is semantically related to the text …

Multimodal Sentiment Analysis (MSA) aims at teaching computers or robotics to understand
human sentiment with diverse multimodal signals, including audio, vision, and text. Current …

Temporal answer grounding in instructional video (TAGV) is a new task naturally derived
from temporal sentence grounding in general video (TSGV). Given an untrimmed …

Temporal sentence grounding aims to detect the event timestamps described by the natural
language query from given untrimmed videos. The existing fully-supervised setting achieves …

Natural language moment localization aims to localize the target moment that matches a
given natural language query in an untrimmed video. The key to this challenging task is to …

Video moment retrieval and highlight detection have received attention in the current era of
video content proliferation, aiming to localize moments and estimate clip relevances based …