most citedFD2-Net: Frequency-Driven Feature Decomposition Network for Infrared-Visible Object Detection

1 citations · 3 across the 6 of their papers we have counts for

collaborators

11 papers

cs.CV2025

Improving Few-Shot Change Detection Visual Question Answering via Decision-Ambiguity-guided Reinforcement Fine-Tuning

Fuyu Dong, Ke Li, Di Wang +5

Change detection visual question answering (CDVQA) requires answering text queries by reasoning about semantic changes in bi-temporal remote sensing images. A straightforward appro…

cs.CV2025

CheXPO-v2: Preference Optimization for Chest X-ray VLMs with Knowledge Graph Consistency

Xiao Liang, Yuxuan An, Di Wang +4

Medical Vision-Language Models (VLMs) are prone to hallucinations, compromising clinical reliability. While reinforcement learning methods like Group Relative Policy Optimization (…

cs.CV2025

Anatomical Region-Guided Contrastive Decoding: A Plug-and-Play Strategy for Mitigating Hallucinations in Medical VLMs

Xiao Liang, Chenxi Liu, Zhi Ma +4

Medical Vision-Language Models (MedVLMs) show immense promise in clinical applicability. However, their reliability is hindered by hallucinations, where models often fail to derive…

cs.CV2025

Enhancing Cross-View Geo-Localization Generalization via Global-Local Consistency and Geometric Equivariance

Xiaowei Wang, Di Wang, Ke Li +6

Cross-view geo-localization (CVGL) aims to match images of the same location captured from drastically different viewpoints. Despite recent progress, existing methods still face tw…

cs.CV2025

RSVG-ZeroOV: Exploring a Training-Free Framework for Zero-Shot Open-Vocabulary Visual Grounding in Remote Sensing Images

Ke Li, Di Wang, Ting Wang +6

Remote sensing visual grounding (RSVG) aims to localize objects in remote sensing images based on free-form natural language expressions. Existing approaches are typically constrai…

cs.LG20251 cited

EvoFormer: Learning Dynamic Graph-Level Representations with Structural and Temporal Bias Correction

Haodi Zhong, Liuxin Zou, Di Wang +3

Dynamic graph-level embedding aims to capture structural evolution in networks, which is essential for modeling real-world scenarios. However, existing methods face two critical ye…