3 papers
cs.CV2026
Reversing the Flow: Generation-to-Understanding Synergy in Large Multimodal Models
Yujun Tong, Dongliang Chang, Zijin Yin +3
The long-standing goal of multimodal AI is to build unified models in which visual understanding and visual generation mutually enhance one another. Despite recent works such as BA…
cs.CV2025
Controllable-Continuous Color Editing in Diffusion Model via Color Mapping
Yuqi Yang, Dongliang Chang, Yuanchen Fang +3
In recent years, text-driven image editing has made significant progress. However, due to the inherent ambiguity and discreteness of natural language, color editing still faces cha…
cs.CL2025
Chart-HQA: A Benchmark for Hypothetical Question Answering in Charts
Xiangnan Chen, Yuancheng Fang, Qian Xiao +5
Multimodal Large Language Models (MLLMs) have garnered significant attention for their strong visual-semantic understanding. Most existing chart benchmarks evaluate MLLMs' ability…