4 citations · 4 across the 12 of their papers we have counts for
6 papers · 2 filters
Referencing Where to Focus: Improving VisualGrounding with Referential Query
Yabing Wang, Zhuotao Tian, Qingpei Guo +4
Visual Grounding aims to localize the referring object in an image given a natural language expression. Recent advancements in DETR-based visual grounding methods have attracted co…
DynFocus: Dynamic Cooperative Network Empowers LLMs with Video Understanding
Yudong Han, Qingpei Guo, Liyuan Pan +3
The challenge in LLM-based video understanding lies in preserving visual and semantic information in long videos while maintaining a memory-affordable token count. However, redunda…
SHE-Net: Syntax-Hierarchy-Enhanced Text-Video Retrieval
Xuzheng Yu, Chen Jiang, Xingning Dong +3
The user base of short video apps has experienced unprecedented growth in recent years, resulting in a significant demand for video content analysis. In particular, text-video retr…
M2-Encoder: Advancing Bilingual Image-Text Understanding by Large-scale Efficient Pretraining
Qingpei Guo, Furong Xu, Hanxiao Zhang +6
Vision-language foundation models like CLIP have revolutionized the field of artificial intelligence. Nevertheless, VLM models supporting multi-language, e.g., in both Chinese and…
M2-RAAP: A Multi-Modal Recipe for Advancing Adaptation-based Pre-training towards Effective and Efficient Zero-shot Video-text Retrieval
Xingning Dong, Zipeng Feng, Chunluan Zhou +3
We present a Multi-Modal Recipe for Advancing Adaptation-based Pre-training towards effective and efficient zero-shot video-text retrieval, dubbed M2-RAAP. Upon popular image-text…
SyCoCa: Symmetrizing Contrastive Captioners with Attentive Masking for Multimodal Alignment
Ziping Ma, Furong Xu, Jian Liu +2
Multimodal alignment between language and vision is the fundamental topic in current vision-language model research. Contrastive Captioners (CoCa), as a representative method, inte…