most citedCombining Knowledge Graph and LLMs for Enhanced Zero-shot Visual Question Answering

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.AI2026

Uncertainty Reasoning with Large Language Models for Explainable Disease Diagnosis

Xiaoyang Fan, Yufan Cai, Zhe Hou +1

Clinical decision-making requires reasoning over incomplete, imprecise, and linguistically expressed patient narratives. While large language models (LLMs) excel at extracting late…

cs.CV2026

AgroTools: A Benchmark for Tool-Augmented Multimodal Agents in Agriculture

Zi Ye, Yibin Wen, Xiaoya Fan +10

Agricultural decision-making increasingly requires multimodal systems that can transform visual observations into reliable, executable actions. However, existing agricultural multi…

cs.CV2026

Beyond RGB: Benchmarking and Enhancing MLLMs for Hyperspectral Image Understanding via Training-Free Reasoning Framework

Xinyu Zhang, Zurong Mai, Qingmei Li +13

Multimodal Large Language Models (MLLMs) have achieved strong performance on RGB image understanding, yet their ability to use spectral evidence beyond the visible range remains la…

cs.AI2025

AgroCoT: A Chain-of-Thought Benchmark for Evaluating Reasoning in Vision-Language Models for Agriculture

Yibin Wen, Qingmei Li, Zi Ye +14

Recent advancements in Vision-Language Models (VLMs) have significantly impacted various industries. In agriculture, these multimodal capabilities hold great promise for applicatio…

cs.CV20251 cited

Combining Knowledge Graph and LLMs for Enhanced Zero-shot Visual Question Answering

Qian Tao, Xiaoyang Fan, Yong Xu +2

Zero-shot visual question answering (ZS-VQA), an emerged critical research area, intends to answer visual questions without providing training samples. Existing research in ZS-VQA…