14 papers
View-Structured Conformal Prediction for 3D Gaussian Splatting
Junzheng Chu, Bin Pan, Zhenwei Shi
3D Gaussian Splatting (3DGS) renders novel views in real time, but an uncertainty heatmap does not certify that a rendered view meets a certain prediction coverage. We treat novel-…
SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation
Chuyu Zhong, Keyan Chen, Qinzhe Yang +3
Pixel count and geographical coverage are two key characteristics of remote sensing images. Existing remote sensing image segmentation methods typically focus on images with either…
Radiative-Structured Neural Operator for Continuous Spectral Super-Resolution
Ziye Zhang, Bin Pan, Zhenwei Shi
Spectral super-resolution (SSR) aims to reconstruct hyperspectral images (HSIs) from multispectral observations, with broad applications in computer vision and remote sensing. Deep…
A Copula-Guided Temporal Dependency Method for Multitemporal Hyperspectral Images Unmixing
Ruiying Li, Bin Pan, Qiaoying Qu +2
Multitemporal hyperspectral unmixing (MTHU) aims to model variable endmembers and dynamical abundances, which emphasizes the critical temporal information. However, existing method…
SMILE: A Super-resolution Guided Multi-task Learning Method for Hyperspectral Unmixing
Ruiying Li, Bin Pan, Qiaoying Qu +2
The performance of hyperspectral unmixing may be constrained by low spatial resolution, which can be enhanced using super-resolution in a multitask learning way. However, integrati…
Preserving Domain Generalization in Fine-Tuning via Joint Parameter Selection
Bin Pan, Shiyu Shen, Zongbin Wang +2
Domain generalization seeks to develop models trained on a limited set of source domains that are capable of generalizing effectively to unseen target domains. While the predominan…
Hyperspectral Image Generation with Unmixing Guided Diffusion Model
Shiyu Shen, Bin Pan, Ziye Zhang +1
We address hyperspectral image (HSI) synthesis, a problem that has garnered growing interest yet remains constrained by the conditional generative paradigms that limit sample diver…
Multitemporal Latent Dynamical Framework for Hyperspectral Images Unmixing
Ruiying Li, Bin Pan, Lan Ma +2
Multitemporal hyperspectral unmixing can capture dynamical evolution of materials. Despite its capability, current methods emphasize variability of endmembers while neglecting dyna…
Transformer for Multitemporal Hyperspectral Image Unmixing
Hang Li, Qiankun Dong, Xueshuo Xie +3
Multitemporal hyperspectral image unmixing (MTHU) holds significant importance in monitoring and analyzing the dynamic changes of surface. However, compared to single-temporal unmi…
CDG: Conditional Domain Generalization for Hyperspectral Imagery Classification with Convergence and Constrained-risk Theories
Zhe Gao, Bin Pan, Zhenwei Shi
Hyperspectral imagery (HSI) classification may suffer the challenge of hyperspectral-monospectra, where different classes present similar spectra. Joint spatial-spectral feature ex…
Domain Generalization Guided by Large-Scale Pre-Trained Priors
Zongbin Wang, Bin Pan, Shiyu Shen +2
Domain generalization (DG) aims to train a model from limited source domains, allowing it to generalize to unknown target domains. Typically, DG models only employ large-scale pre-…
Domain Agnostic Conditional Invariant Predictions for Domain Generalization
Zongbin Wang, Bin Pan, Zhenwei Shi
Domain generalization aims to develop a model that can perform well on unseen target domains by learning from multiple source domains. However, recent-proposed domain generalizatio…
Bayesian Domain Invariant Learning via Posterior Generalization of Parameter Distributions
Shiyu Shen, Bin Pan, Tianyang Shi +2
Domain invariant learning aims to learn models that extract invariant features over various training domains, resulting in better generalization to unseen target domains. Recently,…
Be Bayesian by Attachments to Catch More Uncertainty
Shiyu Shen, Bin Pan, Tianyang Shi +2
Bayesian Neural Networks (BNNs) have become one of the promising approaches for uncertainty estimation due to the solid theorical foundations. However, the performance of BNNs is a…