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20242026
most citedEnhanced Multimodal RAG-LLM for Accurate Visual Question Answering

2 citations · 2 across the 3 of their papers we have counts for

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cs.CV2026

Model Guides You How to Draw: Adaptive Visual Gating for Unified Multimodal Reasoning

Wenxi Gao, Guanxi Lu, Didi Zhu +5

Unified multimodal models (UMMs) with interleaved reasoning, which generate both textual and visual steps as part of intermediate reasoning traces, have demonstrated great potentia…

cs.CV2026

AeroRAG: Structured Multimodal Retrieval-Augmented LLM for Fine-Grained Aerial Visual Reasoning

Junxiao Xue, Quan Deng, Tingqi Hu +4

Despite recent progress in multimodal large language models (MLLMs), reliable visual question answering in aerial scenes remains challenging. In such scenes, task-critical evidence…

cs.CV2025

Open-World 3D Scene Graph Generation for Retrieval-Augmented Reasoning

Fei Yu, Quan Deng, Shengeng Tang +2

Understanding 3D scenes in open-world settings poses fundamental challenges for vision and robotics, particularly due to the limitations of closed-vocabulary supervision and static…

cs.CV2025

Towards Comprehensive Interactive Change Understanding in Remote Sensing: A Large-scale Dataset and Dual-granularity Enhanced VLM

Junxiao Xue, Quan Deng, Xuecheng Wu +7

Remote sensing change understanding (RSCU) is essential for analyzing remote sensing images and understanding how human activities affect the environment. However, existing dataset…

cs.CV20242 cited

Enhanced Multimodal RAG-LLM for Accurate Visual Question Answering

Junxiao Xue, Quan Deng, Fei Yu +3

Multimodal large language models (MLLMs), such as GPT-4o, Gemini, LLaVA, and Flamingo, have made significant progress in integrating visual and textual modalities, excelling in tas…