7 citations · 14 across the 9 of their papers we have counts for
10 papers
ObsDriveBench: Benchmarking Multimodal Understanding under Adverse Weather with Observability Awareness
Qiao Yan, Yihan Wang, Zhenghao Xing +2
Autonomous driving under adverse weather remains a critical challenge, yet existing vision-language benchmarks mainly evaluate under standard conditions, synthetic corruptions, or…
Unifying Physically-Informed Weather Priors in A Single Model for Image Restoration Across Multiple Adverse Weather Conditions
Jiaqi Xu, Xiaowei Hu, Lei Zhu +1
Image restoration under multiple adverse weather conditions aims to develop a single model to recover the underlying scene with high visibility. Weather-related artifacts vary with…
MedHallTune: An Instruction-Tuning Benchmark for Mitigating Medical Hallucination in Vision-Language Models
Qiao Yan, Yuchen Yuan, Xiaowei Hu +6
The increasing use of vision-language models (VLMs) in healthcare applications presents great challenges related to hallucinations, in which the models may generate seemingly plaus…
Towards Real-World Adverse Weather Image Restoration: Enhancing Clearness and Semantics with Vision-Language Models
Jiaqi Xu, Mengyang Wu, Xiaowei Hu +3
This paper addresses the limitations of adverse weather image restoration approaches trained on synthetic data when applied to real-world scenarios. We formulate a semi-supervised…
Unveiling the Generalization Power of Fine-Tuned Large Language Models
Haoran Yang, Yumeng Zhang, Jiaqi Xu +3
While Large Language Models (LLMs) have demonstrated exceptional multitasking abilities, fine-tuning these models on downstream, domain-specific datasets is often necessary to yiel…
Video Dehazing via a Multi-Range Temporal Alignment Network with Physical Prior
Jiaqi Xu, Xiaowei Hu, Lei Zhu +4
Video dehazing aims to recover haze-free frames with high visibility and contrast. This paper presents a novel framework to effectively explore the physical haze priors and aggrega…
3D Perception based Imitation Learning under Limited Demonstration for Laparoscope Control in Robotic Surgery
Bin Li, Ruofeng Wei, Jiaqi Xu +6
Automatic laparoscope motion control is fundamentally important for surgeons to efficiently perform operations. However, its traditional control methods based on tool tracking with…
SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot Learning
Jiaqi Xu, Bin Li, Bo Lu +3
Autonomous surgical execution relieves tedious routines and surgeon's fatigue. Recent learning-based methods, especially reinforcement learning (RL) based methods, achieve promisin…
Deep Mining External Imperfect Data for Chest X-ray Disease Screening
Luyang Luo, Lequan Yu, Hao Chen +4
Deep learning approaches have demonstrated remarkable progress in automatic Chest X-ray analysis. The data-driven feature of deep models requires training data to cover a large dis…
IRNet: Instance Relation Network for Overlapping Cervical Cell Segmentation
Yanning Zhou, Hao Chen, Jiaqi Xu +2
Cell instance segmentation in Pap smear image remains challenging due to the wide existence of occlusion among translucent cytoplasm in cell clumps. Conventional methods heavily re…