1 citations · 1 across the 15 of their papers we have counts for
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RS-HyRe-R1: A Hybrid Reward Mechanism to Overcome Perceptual Inertia for Remote Sensing Images Understanding
Gaozhi Zhou, Hu He, Peng Shen +8
Reinforcement learning (RL) post-training substantially improves remote sensing vision-language models (RS-VLMs). However, when handling complex remote sensing imagery (RSI) requir…
FarmMind: Reasoning-Query-Driven Dynamic Segmentation for Farmland Remote Sensing Images
Haiyang Wu, Weiliang Mu, Jipeng Zhang +4
Existing methods for farmland remote sensing image (FRSI) segmentation generally follow a static segmentation paradigm, where analysis relies solely on the limited information cont…
MMedExpert-R1: Strengthening Multimodal Medical Reasoning via Domain-Specific Adaptation and Clinical Guideline Reinforcement
Meidan Ding, Jipeng Zhang, Wenxuan Wang +4
Medical Vision-Language Models (MedVLMs) excel at perception tasks but struggle with complex clinical reasoning required in real-world scenarios. While reinforcement learning (RL)…
MarineEval: Assessing the Marine Intelligence of Vision-Language Models
YuK-Kwan Wong, Tuan-An To, Jipeng Zhang +2
We have witnessed promising progress led by large language models (LLMs) and further vision language models (VLMs) in handling various queries as a general-purpose assistant. VLMs,…
Skin-R1: Clinical Knowledge-Guided Dermatological Diagnosis Using Vision-Language Models
Zehao Liu, Weijieying Ren, Jipeng Zhang +4
Vision--language models (VLMs) have recently shown promise for assisting clinical reasoning in dermatological diagnosis. However, their trustworthiness and clinical utility remain…
Pointing to a Llama and Call it a Camel: On the Sycophancy of Multimodal Large Language Models
Renjie Pi, Kehao Miao, Li Peihang +4
Multimodal large language models (MLLMs) have demonstrated extraordinary capabilities in conducting conversations based on image inputs. However, we observe that MLLMs exhibit a pr…