285 citations · 298 across the 18 of their papers we have counts for
22 papers
DIAL: Dynamic and Informed Adjustment of the Native Stochastic Law for Reliable Weather Ensembles
Yucheng Wang, Haonan Guo, Tianhao Zhao +4
Ensemble weather forecasts can look reliable in global averages while hiding too little spread in some cases and too much in others. These errors vary across variables, spatial sca…
ReDiS: Reasoning-Diagnosis Self-Evolution for Multimodal Reasoning in Remote Sensing
Yi Liu, Wentao Jiang, Shuo Ni +5
Multimodal large language models (MLLMs) have shown strong potential for remote sensing understanding, yet reliable reasoning remains challenging when tasks require fine-grained vi…
Hallucination-aware Faithful Reasoning in Remote Sensing
Yi Liu, Jing Zhang, Xiang Feng +5
Multimodal large language models (MLLMs) have demonstrated promising capabilities for remote sensing reasoning, yet they frequently generate visually unsupported reasoning that con…
Echo-α: Large Agentic Multimodal Reasoning Model for Ultrasound Interpretation
Jing Zhang, Wentao Jiang, Tao Huang +8
Ultrasound interpretation requires both precise lesion localization and holistic clinical reasoning, yet existing methods typically excel at only one of these capabilities: special…
UniX: Unifying Autoregression and Diffusion for Chest X-Ray Understanding and Generation
Ruiheng Zhang, Jingfeng Yao, Huangxuan Zhao +9
Despite recent progress, medical foundation models still struggle to unify visual understanding and generation, as these tasks have inherently conflicting goals: semantic abstracti…
Neural Discrete Representation Learning for Sparse-View CBCT Reconstruction: From Algorithm Design to Prospective Multicenter Clinical Evaluation
Haoshen Wang, Lei Chen, Wei-Hua Zhang +13
Cone beam computed tomography (CBCT)-guided puncture has become an established approach for diagnosing and treating early- to mid-stage thoracic tumours, yet the associated radiati…
REX-RAG: Reasoning Exploration with Policy Correction in Retrieval-Augmented Generation
Wentao Jiang, Xiang Feng, Zengmao Wang +5
Reinforcement learning (RL) is emerging as a powerful paradigm for enabling large language models (LLMs) to perform complex reasoning tasks. Recent advances indicate that integrati…
When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need
Ziming Hong, Runnan Chen, Zengmao Wang +3
Data-free knowledge distillation (DFKD) transfers knowledge from a teacher to a student without access the real in-distribution (ID) data. Its common solution is to use a generator…
Intra-Trajectory Consistency for Reward Modeling
Chaoyang Zhou, Shunyu Liu, Zengmao Wang +4
Reward models are critical for improving large language models (LLMs), particularly in reinforcement learning from human feedback (RLHF) or inference-time verification. Current rew…
AnesSuite: A Comprehensive Benchmark and Dataset Suite for Anesthesiology Reasoning in LLMs
Xiang Feng, Wentao Jiang, Zengmao Wang +5
The application of large language models (LLMs) in the medical field has garnered significant attention, yet their reasoning capabilities in more specialized domains like anesthesi…
Dynamic Parallel Tree Search for Efficient LLM Reasoning
Yifu Ding, Wentao Jiang, Shunyu Liu +9
Tree of Thoughts (ToT) enhances Large Language Model (LLM) reasoning by structuring problem-solving as a spanning tree. However, recent methods focus on search accuracy while overl…
MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image Classification
Yapeng Li, Yong Luo, Lefei Zhang +2
Transformer has been extensively explored for hyperspectral image (HSI) classification. However, transformer poses challenges in terms of speed and memory usage because of its quad…
Detect Changes like Humans: Incorporating Semantic Priors for Improved Change Detection
Yuhang Gan, Wenjie Xuan, Zhiming Luo +4
When given two similar images, humans identify their differences by comparing the appearance (e.g., color, texture) with the help of semantics (e.g., objects, relations). However,…
What If the Input is Expanded in OOD Detection?
Boxuan Zhang, Jianing Zhu, Zengmao Wang +3
Out-of-distribution (OOD) detection aims to identify OOD inputs from unknown classes, which is important for the reliable deployment of machine learning models in the open world. V…
SAT3D: Image-driven Semantic Attribute Transfer in 3D
Zhijun Zhai, Zengmao Wang, Xiaoxiao Long +2
GAN-based image editing task aims at manipulating image attributes in the latent space of generative models. Most of the previous 2D and 3D-aware approaches mainly focus on editing…
LeMeViT: Efficient Vision Transformer with Learnable Meta Tokens for Remote Sensing Image Interpretation
Wentao Jiang, Jing Zhang, Di Wang +3
Due to spatial redundancy in remote sensing images, sparse tokens containing rich information are usually involved in self-attention (SA) to reduce the overall token numbers within…
GaussianGrasper: 3D Language Gaussian Splatting for Open-vocabulary Robotic Grasping
Yuhang Zheng, Xiangyu Chen, Yupeng Zheng +12
Constructing a 3D scene capable of accommodating open-ended language queries, is a pivotal pursuit, particularly within the domain of robotics. Such technology facilitates robots i…
Boosting Semi-Supervised Object Detection in Remote Sensing Images With Active Teaching
Boxuan Zhang, Zengmao Wang, Bo Du
The lack of object-level annotations poses a significant challenge for object detection in remote sensing images (RSIs). To address this issue, active learning (AL) and semi-superv…
Unsupervised Domain Adaptation with Implicit Pseudo Supervision for Semantic Segmentation
Wanyu Xu, Zengmao Wang, Wei Bian
Pseudo-labelling is a popular technique in unsuper-vised domain adaptation for semantic segmentation. However, pseudo labels are noisy and inevitably have confirmation bias due to…
Robust and Discriminative Labeling for Multi-label Active Learning Based on Maximum Correntropy Criterion
Bo Du, Zengmao Wang, Lefei Zhang +2
Multi-label learning draws great interests in many real world applications. It is a highly costly task to assign many labels by the oracle for one instance. Meanwhile, it is also h…
Exploring Representativeness and Informativeness for Active Learning
Bo Du, Zengmao Wang, Lefei Zhang +4
How can we find a general way to choose the most suitable samples for training a classifier? Even with very limited prior information? Active learning, which can be regarded as an…
Multi-class Active Learning: A Hybrid Informative and Representative Criterion Inspired Approach
Xi Fang, Zengmao Wang, Xinyao Tang +1
Labeling each instance in a large dataset is extremely labor- and time- consuming . One way to alleviate this problem is active learning, which aims to which discover the most valu…