41 citations · 145 across the 11 of their papers we have counts for
9 papers · 1 filter
MUSE: Mamba is Efficient Multi-scale Learner for Text-video Retrieval
Haoran Tang, Meng Cao, Jinfa Huang +4
Text-Video Retrieval (TVR) aims to align and associate relevant video content with corresponding natural language queries. Most existing TVR methods are based on large-scale pre-tr…
RAP: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter
Meng Cao, Haoran Tang, Jinfa Huang +7
Text-Video Retrieval (TVR) aims to align relevant video content with natural language queries. To date, most state-of-the-art TVR methods learn image-to-video transfer learning bas…
MoE-LLaVA: Mixture of Experts for Large Vision-Language Models
Bin Lin, Zhenyu Tang, Yang Ye +7
Recent advances demonstrate that scaling Large Vision-Language Models (LVLMs) effectively improves downstream task performances. However, existing scaling methods enable all model…
Improving Scene Graph Generation with Superpixel-Based Interaction Learning
Jingyi Wang, Can Zhang, Jinfa Huang +2
Recent advances in Scene Graph Generation (SGG) typically model the relationships among entities utilizing box-level features from pre-defined detectors. We argue that an overlooke…
Text-Video Retrieval with Disentangled Conceptualization and Set-to-Set Alignment
Peng Jin, Hao Li, Zesen Cheng +5
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 de…
Cross-Modality Time-Variant Relation Learning for Generating Dynamic Scene Graphs
Jingyi Wang, Jinfa Huang, Can Zhang +1
Dynamic scene graphs generated from video clips could help enhance the semantic visual understanding in a wide range of challenging tasks such as environmental perception, autonomo…