1 citations · 1 across the 3 of their papers we have counts for
5 papers
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation
Yiming Zhao, Yu Zeng, Wenxuan Huang +11
Large Vision-Language Models (LVLMs) have shown significant progress in video understanding, yet they face substantial challenges in tasks requiring precise spatiotemporal localiza…
FlowInOne:Unifying Multimodal Generation as Image-in, Image-out Flow Matching
Junchao Yi, Rui Zhao, Jiahao Tang +7
Multimodal generation has long been dominated by text-driven pipelines where language dictates vision but cannot reason or create within it. We challenge this paradigm by asking wh…
Knowledge-Aware Artifact Image Synthesis with LLM-Enhanced Prompting and Multi-Source Supervision
Shengguang Wu, Zhenglun Chen, Qi Su
Ancient artifacts are an important medium for cultural preservation and restoration. However, many physical copies of artifacts are either damaged or lost, leaving a blank space in…
DiffuVST: Narrating Fictional Scenes with Global-History-Guided Denoising Models
Shengguang Wu, Mei Yuan, Qi Su
Recent advances in image and video creation, especially AI-based image synthesis, have led to the production of numerous visual scenes that exhibit a high level of abstractness and…
Self-Evolved Diverse Data Sampling for Efficient Instruction Tuning
Shengguang Wu, Keming Lu, Benfeng Xu +3
Enhancing the instruction-following ability of Large Language Models (LLMs) primarily demands substantial instruction-tuning datasets. However, the sheer volume of these imposes a…