3 citations · 9 across the 7 of their papers we have counts for
7 papers
GIR-Bench: Versatile Benchmark for Generating Images with Reasoning
Hongxiang Li, Yaowei Li, Bin Lin +7
Unified multimodal models integrate the reasoning capacity of large language models with both image understanding and generation, showing great promise for advanced multimodal inte…
Image Conductor: Precision Control for Interactive Video Synthesis
Yaowei Li, Xintao Wang, Zhaoyang Zhang +5
Filmmaking and animation production often require sophisticated techniques for coordinating camera transitions and object movements, typically involving labor-intensive real-world…
Embracing Language Inclusivity and Diversity in CLIP through Continual Language Learning
Bang Yang, Yong Dai, Xuxin Cheng +3
While vision-language pre-trained models (VL-PTMs) have advanced multimodal research in recent years, their mastery in a few languages like English restricts their applicability in…
G2L: Semantically Aligned and Uniform Video Grounding via Geodesic and Game Theory
Hongxiang Li, Meng Cao, Xuxin Cheng +3
The recent video grounding works attempt to introduce vanilla contrastive learning into video grounding. However, we claim that this naive solution is suboptimal. Contrastive learn…
Unify, Align and Refine: Multi-Level Semantic Alignment for Radiology Report Generation
Yaowei Li, Bang Yang, Xuxin Cheng +3
Automatic radiology report generation has attracted enormous research interest due to its practical value in reducing the workload of radiologists. However, simultaneously establis…
SSVMR: Saliency-based Self-training for Video-Music Retrieval
Xuxin Cheng, Zhihong Zhu, Hongxiang Li +2
With the rise of short videos, the demand for selecting appropriate background music (BGM) for a video has increased significantly, video-music retrieval (VMR) task gradually draws…
Exploiting Auxiliary Caption for Video Grounding
Hongxiang Li, Meng Cao, Xuxin Cheng +3
Video grounding aims to locate a moment of interest matching the given query sentence from an untrimmed video. Previous works ignore the {sparsity dilemma} in video annotations, wh…