3 citations · 8 across the 15 of their papers we have counts for
16 papers
Data-Driven Vision-Language Models for Remote Sensing: A Survey
Yue Zhou, Shujun Zhao, Xue Yang +7
During the deep learning era, innovations in remote sensing (RS) vision models primarily focused on optimizing network architectures for specific tasks and conducting end-to-end tr…
UI-Mate: Advancing Open-Weight Foundation GUI Agents with In-Context Demonstrations
Zihan Ding, Longxu Dou, Qi Gao +26
Foundation GUI agents can automate complex digital tasks, but deployment is hindered by scarce and biased training data, ambiguous prompts, and unreliable execution. Routine workfl…
Let ViT Speak: Generative Language-Image Pre-training
Yan Fang, Mengcheng Lan, Zilong Huang +7
In this paper, we present \textbf{Gen}erative \textbf{L}anguage-\textbf{I}mage \textbf{P}re-training (GenLIP), a minimalist generative pretraining framework for Vision Transformers…
Multimodal Mathematical Reasoning Embedded in Aerial Vehicle Imagery: Benchmarking, Analysis, and Exploration
Yue Zhou, Litong Feng, Mengcheng Lan +5
Mathematical reasoning is critical for tasks such as precise distance and area computations, trajectory estimations, and spatial analysis in unmanned aerial vehicle (UAV) based rem…
Text4Seg++: Advancing Image Segmentation via Generative Language Modeling
Mengcheng Lan, Chaofeng Chen, Jiaxing Xu +6
Multimodal Large Language Models (MLLMs) have shown exceptional capabilities in vision-language tasks. However, effectively integrating image segmentation into these models remains…
NOCL: Node-Oriented Conceptualization LLM for Graph Tasks without Message Passing
Wei Li, Mengcheng Lan, Jiaxing Xu +1
Graphs are essential for modeling complex interactions across domains such as social networks, biology, and recommendation systems. Traditional Graph Neural Networks, particularly…
BrainPrompt: Multi-Level Brain Prompt Enhancement for Neurological Condition Identification
Jiaxing Xu, Kai He, Yue Tang +5
Neurological conditions, such as Alzheimer's Disease, are challenging to diagnose, particularly in the early stages where symptoms closely resemble healthy controls. Existing brain…
Directed Homophily-Aware Graph Neural Network
Aihu Zhang, Jiaxing Xu, Mengcheng Lan +2
Graph Neural Networks (GNNs) have achieved significant success in various learning tasks on graph-structured data. Nevertheless, most GNNs struggle to generalize to heterophilic ne…
BrainOOD: Out-of-distribution Generalizable Brain Network Analysis
Jiaxing Xu, Yongqiang Chen, Xia Dong +5
In neuroscience, identifying distinct patterns linked to neurological disorders, such as Alzheimer's and Autism, is critical for early diagnosis and effective intervention. Graph N…
GeoGround: A Unified Large Vision-Language Model for Remote Sensing Visual Grounding
Yue Zhou, Mengcheng Lan, Xiang Li +6
Remote sensing (RS) visual grounding aims to use natural language expression to locate specific objects (in the form of the bounding box or segmentation mask) in RS images, enhanci…
Text4Seg: Reimagining Image Segmentation as Text Generation
Mengcheng Lan, Chaofeng Chen, Yue Zhou +5
Multimodal Large Language Models (MLLMs) have shown exceptional capabilities in vision-language tasks; however, effectively integrating image segmentation into these models remains…
Multi-Atlas Brain Network Classification through Consistency Distillation and Complementary Information Fusion
Jiaxing Xu, Mengcheng Lan, Xia Dong +4
In the realm of neuroscience, identifying distinctive patterns associated with neurological disorders via brain networks is crucial. Resting-state functional magnetic resonance ima…
Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identification
Jiaxing Xu, Kai He, Mengcheng Lan +5
Understanding neurological disorder is a fundamental problem in neuroscience, which often requires the analysis of brain networks derived from functional magnetic resonance imaging…
ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation
Mengcheng Lan, Chaofeng Chen, Yiping Ke +3
Open-vocabulary semantic segmentation requires models to effectively integrate visual representations with open-vocabulary semantic labels. While Contrastive Language-Image Pre-tra…
ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference
Mengcheng Lan, Chaofeng Chen, Yiping Ke +3
Despite the success of large-scale pretrained Vision-Language Models (VLMs) especially CLIP in various open-vocabulary tasks, their application to semantic segmentation remains cha…
SmooSeg: Smoothness Prior for Unsupervised Semantic Segmentation
Mengcheng Lan, Xinjiang Wang, Yiping Ke +3
Unsupervised semantic segmentation is a challenging task that segments images into semantic groups without manual annotation. Prior works have primarily focused on leveraging prior…