collaborators

5 papers

cs.CV2025

See Less, See Right: Bi-directional Perceptual Shaping For Multimodal Reasoning

Shuoshuo Zhang, Yizhen Zhang, Jingjing Fu +4

Large vision-language models (VLMs) often benefit from intermediate visual cues, either injected via external tools or generated as latent visual tokens during reasoning, but these…

cs.CV2025

PixelCraft: A Multi-Agent System for High-Fidelity Visual Reasoning on Structured Images

Shuoshuo Zhang, Zijian Li, Yizhen Zhang +6

Structured images (e.g., charts and geometric diagrams) remain challenging for multimodal large language models (MLLMs), as perceptual slips can cascade into erroneous conclusions.…

cs.IR2025

OMGM: Orchestrate Multiple Granularities and Modalities for Efficient Multimodal Retrieval

Wei Yang, Jingjing Fu, Rui Wang +3

Vision-language retrieval-augmented generation (RAG) has become an effective approach for tackling Knowledge-Based Visual Question Answering (KB-VQA), which requires external knowl…

cs.CV2025

Chain of Functions: A Programmatic Pipeline for Fine-Grained Chart Reasoning Data

Zijian Li, Jingjing Fu, Lei Song +3

Visual reasoning is crucial for multimodal large language models (MLLMs) to address complex chart queries, yet high-quality rationale data remains scarce. Existing methods leverage…

cs.CL2025

PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generation

Jinyu Wang, Jingjing Fu, Rui Wang +2

Despite notable advancements in Retrieval-Augmented Generation (RAG) systems that expand large language model (LLM) capabilities through external retrieval, these systems often str…