24 citations · 114 across the 37 of their papers we have counts for
9 papers · 1 filter
VIRAL: Visual In-Context Reasoning via Analogy in Diffusion Transformers
Zhiwen Li, Zhongjie Duan, Jinyan Ye +4
Replicating In-Context Learning (ICL) in computer vision remains challenging due to task heterogeneity. We propose \textbf{VIRAL}, a framework that elicits visual reasoning from a…
VeriSciQA: An Auto-Verified Dataset for Scientific Visual Question Answering
Yuyi Li, Daoyuan Chen, Zhen Wang +2
Large Vision-Language Models (LVLMs) show promise for scientific applications, yet open-source models still struggle with Scientific Visual Question Answering (SVQA), namely answer…
AutoLoRA: Automatic LoRA Retrieval and Fine-Grained Gated Fusion for Text-to-Image Generation
Zhiwen Li, Zhongjie Duan, Die Chen +4
Despite recent advances in photorealistic image generation through large-scale models like FLUX and Stable Diffusion v3, the practical deployment of these architectures remains con…
DetailMaster: Can Your Text-to-Image Model Handle Long Prompts?
Qirui Jiao, Daoyuan Chen, Yilun Huang +3
While recent Text-to-Image (T2I) models show impressive capabilities in synthesizing images from brief descriptions, they struggle with the long, detailed prompts required for prof…
MindGYM: What Matters in Question Synthesis for Thinking-Centric Fine-Tuning?
Zhe Xu, Daoyuan Chen, Zhenqing Ling +2
Large foundation models face challenges in acquiring transferable, structured thinking abilities, especially when supervised with rigid templates or crowd-annotated instruction dat…
ArtAug: Enhancing Text-to-Image Generation through Synthesis-Understanding Interaction
Zhongjie Duan, Qianyi Zhao, Cen Chen +4
The emergence of diffusion models has significantly advanced image synthesis. The recent studies of model interaction and self-corrective reasoning approach in large language model…
HumanVBench: Probing Human-Centric Video Understanding in MLLMs with Automatically Synthesized Benchmarks
Ting Zhou, Daoyuan Chen, Qirui Jiao +3
Evaluating the nuanced human-centric video understanding capabilities of Multimodal Large Language Models (MLLMs) remains a great challenge, as existing benchmarks often overlook t…
Img-Diff: Contrastive Data Synthesis for Multimodal Large Language Models
Qirui Jiao, Daoyuan Chen, Yilun Huang +3
High-performance Multimodal Large Language Models (MLLMs) are heavily dependent on data quality. To advance fine-grained image recognition within MLLMs, we introduce a novel data s…
From Training-Free to Adaptive: Empirical Insights into MLLMs' Understanding of Detection Information
Qirui Jiao, Daoyuan Chen, Yilun Huang +2
Despite the impressive capabilities of Multimodal Large Language Models (MLLMs) in integrating text and image modalities, challenges remain in accurately interpreting detailed visu…