3 citations · 8 across the 12 of their papers we have counts for
8 papers · 1 filter
Residual Diffusion Bridge Model for Image Restoration
Hebaixu Wang, Jing Zhang, Haoyang Chen +4
Diffusion bridge models establish probabilistic paths between arbitrary paired distributions and exhibit great potential for universal image restoration. Most existing methods mere…
S5: Scalable Semi-Supervised Semantic Segmentation in Remote Sensing
Liang Lv, Di Wang, Jing Zhang +1
Semi-supervised semantic segmentation (S4) has advanced remote sensing (RS) analysis by leveraging unlabeled data through pseudo-labeling and consistency learning. However, existin…
SPEX: A Vision-Language Model for Land Cover Extraction on Spectral Remote Sensing Images
Dongchen Si, Di Wang, Erzhong Gao +9
Spectral information has long been recognized as a critical cue in remote sensing observations. Although numerous vision-language models have been developed for pixel-level interpr…
TiMo: Spatiotemporal Foundation Model for Satellite Image Time Series
Xiaolei Qin, Di Wang, Jing Zhang +4
Satellite image time series (SITS) provide continuous observations of the Earth's surface, making them essential for applications such as environmental management and disaster asse…
DGSolver: Diffusion Generalist Solver with Universal Posterior Sampling for Image Restoration
Hebaixu Wang, Jing Zhang, Haonan Guo +3
Diffusion models have achieved remarkable progress in universal image restoration. While existing methods speed up inference by reducing sampling steps, substantial step intervals…
GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K Resolution
Fengxiang Wang, Mingshuo Chen, Yueying Li +11
Ultra-high-resolution (UHR) remote sensing (RS) imagery offers valuable data for Earth observation but pose challenges for existing multimodal foundation models due to two key bott…
XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?
Fengxiang Wang, Hongzhen Wang, Mingshuo Chen +9
The astonishing breakthrough of multimodal large language models (MLLMs) has necessitated new benchmarks to quantitatively assess their capabilities, reveal their limitations, and…
RoMA: Scaling up Mamba-based Foundation Models for Remote Sensing
Fengxiang Wang, Yulin Wang, Mingshuo Chen +8
Recent advances in self-supervised learning for Vision Transformers (ViTs) have fueled breakthroughs in remote sensing (RS) foundation models. However, the quadratic complexity of…