258 citations · 538 across the 39 of their papers we have counts for
29 papers · 1 filter
Model Merging for Medical LVLMs: A Benchmark and a Winner-Take-All Approach
Lichao Mou, Shilan Zhang, Chunlei Li +7
Large vision-language models (LVLMs) can be adapted to specialized medical imaging tasks via parameter-efficient fine-tuning approaches such as low-rank adaptation (LoRA), leading…
Towards Real-World Ultrasound Understanding: Large Vision-Language Models from Multi-Image Examinations with Long-Form Reports
Bingcong Yan, Chunlei Li, Jingliang Hu +3
Large vision-language models (LVLMs) have achieved strong performance across many medical imaging tasks, yet their application to ultrasound remains limited due to its inherent com…
On Revisiting Entropy for Identifying Mislabeled Images
Chunlei Li, Zixuan Zheng, Yilei Shi +5
Mislabeled samples in training datasets severely degrade the performance of deep networks, as overparameterized models tend to memorize erroneous labels. We address this challenge…
Learning Domain-Aware Task Prompt Representations for Multi-Domain All-in-One Image Restoration
Guanglu Dong, Chunlei Li, Chao Ren +4
Recently, significant breakthroughs have been made in all-in-one image restoration (AiOIR), which can handle multiple restoration tasks with a single model. However, existing metho…
GlobalBuildingAtlas: An Open Global and Complete Dataset of Building Polygons, Heights and LoD1 3D Models
Xiao Xiang Zhu, Sining Chen, Fahong Zhang +2
We introduce GlobalBuildingAtlas, a publicly available dataset providing global and complete coverage of building polygons, heights and Level of Detail 1 (LoD1) 3D building models.…
GlobalGeoTree: A Multi-Granular Vision-Language Dataset for Global Tree Species Classification
Yang Mu, Zhitong Xiong, Yi Wang +5
Global tree species mapping using remote sensing data is vital for biodiversity monitoring, forest management, and ecological research. However, progress in this field has been con…
CausalCLIPSeg: Unlocking CLIP's Potential in Referring Medical Image Segmentation with Causal Intervention
Yaxiong Chen, Minghong Wei, Zixuan Zheng +5
Referring medical image segmentation targets delineating lesions indicated by textual descriptions. Aligning visual and textual cues is challenging due to their distinct data prope…
Global High Categorical Resolution Land Cover Mapping via Weak Supervision
Xin-Yi Tong, Runmin Dong, Xiao Xiang Zhu
Land cover information is indispensable for advancing the United Nations' sustainable development goals, and land cover mapping under a more detailed category system would signific…
GlobalBuildingMap -- Unveiling the Mystery of Global Buildings
Xiao Xiang Zhu, Qingyu Li, Yilei Shi +3
Understanding how buildings are distributed globally is crucial to revealing the human footprint on our home planet. This built environment affects local climate, land surface albe…
ChatEarthNet: A Global-Scale Image-Text Dataset Empowering Vision-Language Geo-Foundation Models
Zhenghang Yuan, Zhitong Xiong, Lichao Mou +1
An in-depth comprehension of global land cover is essential in Earth observation, forming the foundation for a multitude of applications. Although remote sensing technology has adv…
Task Specific Pretraining with Noisy Labels for Remote Sensing Image Segmentation
Chenying Liu, Conrad M Albrecht, Yi Wang +1
Compared to supervised deep learning, self-supervision provides remote sensing a tool to reduce the amount of exact, human-crafted geospatial annotations. While image-level informa…
One for All: Toward Unified Foundation Models for Earth Vision
Zhitong Xiong, Yi Wang, Fahong Zhang +1
Foundation models characterized by extensive parameters and trained on large-scale datasets have demonstrated remarkable efficacy across various downstream tasks for remote sensing…
Deep Semantic Model Fusion for Ancient Agricultural Terrace Detection
Yi Wang, Chenying Liu, Arti Tiwari +4
Discovering ancient agricultural terraces in desert regions is important for the monitoring of long-term climate changes on the Earth's surface. However, traditional ground surveys…
Semi-Supervised Learning for hyperspectral images by non parametrically predicting view assignment
Shivam Pande, Nassim Ait Ali Braham, Yi Wang +3
Hyperspectral image (HSI) classification is gaining a lot of momentum in present time because of high inherent spectral information within the images. However, these images suffer…
DisasterNets: Embedding Machine Learning in Disaster Mapping
Qingsong Xu, Yilei Shi, Xiao Xiang Zhu
Disaster mapping is a critical task that often requires on-site experts and is time-consuming. To address this, a comprehensive framework is presented for fast and accurate recogni…
Overcoming Language Bias in Remote Sensing Visual Question Answering via Adversarial Training
Zhenghang Yuan, Lichao Mou, Xiao Xiang Zhu
The Visual Question Answering (VQA) system offers a user-friendly interface and enables human-computer interaction. However, VQA models commonly face the challenge of language bias…
GAMUS: A Geometry-aware Multi-modal Semantic Segmentation Benchmark for Remote Sensing Data
Zhitong Xiong, Sining Chen, Yi Wang +2
Geometric information in the normalized digital surface models (nDSM) is highly correlated with the semantic class of the land cover. Exploiting two modalities (RGB and nDSM (heigh…
Multilingual Augmentation for Robust Visual Question Answering in Remote Sensing Images
Zhenghang Yuan, Lichao Mou, Xiao Xiang Zhu
Aiming at answering questions based on the content of remotely sensed images, visual question answering for remote sensing data (RSVQA) has attracted much attention nowadays. Howev…
Universal Domain Adaptation for Remote Sensing Image Scene Classification
Qingsong Xu, Yilei Shi, Xin Yuan +1
The domain adaptation (DA) approaches available to date are usually not well suited for practical DA scenarios of remote sensing image classification, since these methods (such as…
EarthNets: Empowering AI in Earth Observation
Zhitong Xiong, Fahong Zhang, Yi Wang +2
Earth observation (EO), aiming at monitoring the state of planet Earth using remote sensing data, is critical for improving our daily lives and living environment. With a growing n…
Doubly Deformable Aggregation of Covariance Matrices for Few-shot Segmentation
Zhitong Xiong, Haopeng Li, Xiao Xiang Zhu
Training semantic segmentation models with few annotated samples has great potential in various real-world applications. For the few-shot segmentation task, the main challenge is h…
Self Supervised Learning for Few Shot Hyperspectral Image Classification
Nassim Ait Ali Braham, Lichao Mou, Jocelyn Chanussot +2
Deep learning has proven to be a very effective approach for Hyperspectral Image (HSI) classification. However, deep neural networks require large annotated datasets to generalize…
Self-supervised Learning in Remote Sensing: A Review
Yi Wang, Conrad M Albrecht, Nassim Ait Ali Braham +2
In deep learning research, self-supervised learning (SSL) has received great attention triggering interest within both the computer vision and remote sensing communities. While the…
Semi-Supervised Building Footprint Generation with Feature and Output Consistency Training
Qingyu Li, Yilei Shi, Xiao Xiang Zhu
Accurate and reliable building footprint maps are vital to urban planning and monitoring, and most existing approaches fall back on convolutional neural networks (CNNs) for buildin…