works on

From the 1 of 5 linked papers with an AI index.

most citedMulti-Modal Cross-Domain Alignment Network for Video Moment Retrieval

7 citations · 18 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2026

RFMSR: Residual Flow Matching for Image Super-Resolution

Shuwei Huang, Tianyao Luo, Jicheng Liu +2

The paper introduces RFMSR, a vision‑only image super‑resolution method that uses residual flow matching centered on the low‑quality input latent to preserve structure and enable h…

cs.CV2026

Fewer Steps, Better Performance: Efficient Cross-Modal Clip Trimming for Video Moment Retrieval Using Language

Xiang Fang, Daizong Liu, Wanlong Fang +5

Given an untrimmed video and a sentence query, video moment retrieval using language (VMR) aims to locate a target query-relevant moment. Since the untrimmed video is overlong, alm…

cs.CV20266 cited

You Can Ground Earlier than See: An Effective and Efficient Pipeline for Temporal Sentence Grounding in Compressed Videos

Xiang Fang, Daizong Liu, Pan Zhou +1

Given an untrimmed video, temporal sentence grounding (TSG) aims to locate a target moment semantically according to a sentence query. Although previous respectable works have made…

cs.CV20267 cited

Multi-Modal Cross-Domain Alignment Network for Video Moment Retrieval

Xiang Fang, Daizong Liu, Pan Zhou +1

As an increasingly popular task in multimedia information retrieval, video moment retrieval (VMR) aims to localize the target moment from an untrimmed video according to a given la…

cs.MM20265 cited

Hierarchical Local-Global Transformer for Temporal Sentence Grounding

Xiang Fang, Daizong Liu, Pan Zhou +2

This paper studies the multimedia problem of temporal sentence grounding (TSG), which aims to accurately determine the specific video segment in an untrimmed video according to a g…