Search Results for author:
Found 24 papers, 10 papers with code
Rethinking the Graph Polynomial Filter via Positive and Negative Coupling Analysis
Subsequently, PNCA is used to analyze the mainstream polynomial filters, and a novel simple basis that decouples the positive and negative activation and fully utilizes graph structure information is designed.
Class Similarity Transition: Decoupling Class Similarities and Imbalance from Generalized Few-shot Segmentation
This paper focuses on the relevance between base and novel classes, and improves GFSS in two aspects: 1) mining the similarity between base and novel classes to promote the learning of novel classes, and 2) mitigating the class imbalance issue caused by the volume difference between the support set and the training set.
CRS-Diff: Controllable Generative Remote Sensing Foundation Model
The emergence of diffusion models has revolutionized the field of image generation, providing new methods for creating high-quality, high-resolution images across various applications.
HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion Models
Specifically, the reduced image, which has a low spectral dimension, lies in the image field and can be inferred from our improved diffusion model where a new guidance function with total variation (TV) prior is designed to ensure that the reduced image can be well sampled.
A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection
Change detection (CD) is a critical task to observe and analyze dynamic processes of land cover.
Building change detection for remote sensing images
Change Detection
+1
Neural Gradient Regularizer
NGR is applicable to various image types and different image processing tasks, functioning in a zero-shot learning fashion, making it a versatile and plug-and-play regularizer.
A Low-rank Matching Attention based Cross-modal Feature Fusion Method for Conversational Emotion Recognition
By setting a matching weight and calculating attention scores between modal features row by row, LMAM contains fewer parameters than the self-attention method.
SFP: Spurious Feature-targeted Pruning for Out-of-Distribution Generalization
Based on the idea that in-distribution (ID) data with spurious features may have a lower experience risk, in this paper, we propose a novel Spurious Feature-targeted model Pruning framework, dubbed SFP, to automatically explore invariant substructures without referring to the above drawbacks.
Unsupervised Hyperspectral Pansharpening via Low-rank Diffusion Model
To address these issues, in this work, we propose a low-rank diffusion model for hyperspectral pansharpening by simultaneously leveraging the power of the pre-trained deep diffusion model and better generalization ability of Bayesian methods.
PanFlowNet: A Flow-Based Deep Network for Pan-sharpening
The experimental results verify that the proposed PanFlowNet can generate various HRMS images given an LRMS image and a PAN image.
Probability-based Global Cross-modal Upsampling for Pansharpening
Pansharpening is an essential preprocessing step for remote sensing image processing.
A Hyper-weight Network for Hyperspectral Image Denoising
Extensive experiments verify that the proposed HWnet can help improve the generalization ability of a weighted model to adapt to more complex noise, and can also strengthen the weighted model by transferring the knowledge from another weighted model.
Fast Noise Removal in Hyperspectral Images via Representative Coefficient Total Variation
The model-based methods have good generalization ability, while the runtime cannot meet the fast processing requirements of the practical situations due to the large size of an HSI data $ \mathbf{X} \in \mathbb{R}^{MN\times B}$.
Proximal PanNet: A Model-Based Deep Network for Pansharpening
To alleviate this issue, we propose a novel deep network for pansharpening by combining the model-based methodology with the deep learning method.
Memory-augmented Deep Unfolding Network for Guided Image Super-resolution
Guided image super-resolution (GISR) aims to obtain a high-resolution (HR) target image by enhancing the spatial resolution of a low-resolution (LR) target image under the guidance of a HR image.
Learning an Explicit Weighting Scheme for Adapting Complex HSI Noise
A general approach for handling hyperspectral image (HSI) denoising issue is to impose weights on different HSI pixels to suppress negative influence brought by noisy elements.
Polarimetric SAR Image Semantic Segmentation with 3D Discrete Wavelet Transform and Markov Random Field
Polarimetric synthetic aperture radar (PolSAR) image segmentation is currently of great importance in image processing for remote sensing applications.
New Interpretations of Normalization Methods in Deep Learning
In recent years, a variety of normalization methods have been proposed to help train neural networks, such as batch normalization (BN), layer normalization (LN), weight normalization (WN), group normalization (GN), etc.
Video Rain/Snow Removal by Transformed Online Multiscale Convolutional Sparse Coding
Furthermore, a transformation operator imposed on the background scenes is further embedded into the proposed model, which finely conveys the dynamic background transformations, such as rotations, scalings and distortions, inevitably existed in a real video sequence.
Discovering Influential Factors in Variational Autoencoder
In the field of machine learning, it is still a critical issue to identify and supervise the learned representation without manually intervening or intuition assistance to extract useful knowledge or serve for the downstream tasks.
Preliminary theoretical troubleshooting in Variational Autoencoder
This paper tries to preliminarily address VAE's intrinsic dimension, real factor, disentanglement and indicator issues theoretically in the idealistic situation and implementation issue practically through noise modeling perspective in the realistic case.
Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network
This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework.
Ranked #13 on
Hyperspectral Image Classification
on Indian Pines
(Overall Accuracy metric, using extra
training data)
Denoising Hyperspectral Image with Non-i.i.d. Noise Structure
In real scenarios, however, the noise existed in a natural HSI is always with much more complicated non-i. i. d.
Low-rank Matrix Factorization under General Mixture Noise Distributions
Many computer vision problems can be posed as learning a low-dimensional subspace from high dimensional data.
