Search Results for author:
Found 17 papers, 13 papers with code
From Image- to Pixel-level: Label-efficient Hyperspectral Image Reconstruction
Current hyperspectral image (HSI) reconstruction methods primarily rely on image-level approaches, which are time-consuming to form abundant high-quality HSIs through imagers.
NukesFormers: Unpaired Hyperspectral Image Generation with Non-Uniform Domain Alignment
In this paper, we conquer these challenges by modeling the UnHIG to range space interaction and compensations of null space through Range-Null Space Decomposition (RND) methodology.
SCFlow2: Plug-and-Play Object Pose Refiner with Shape-Constraint Scene Flow
The key design of SCFlow2 is an introduction of geometry constraints into the training of recurrent match network, by combining the rigid-motion embeddings in 3D scene flow and 3D shape prior of the target.
Occlusion-Aware Deep Convolutional Neural Network via Homogeneous Tanh-transforms for Face Parsing
Based on homogeneous tanh-transforms, we propose an occlusion-aware convolutional neural network for occluded face parsing.
Pseudo Flow Consistency for Self-Supervised 6D Object Pose Estimation
Most self-supervised 6D object pose estimation methods can only work with additional depth information or rely on the accurate annotation of 2D segmentation masks, limiting their application range.
Shape-Constraint Recurrent Flow for 6D Object Pose Estimation
In this work, we propose a shape-constraint recurrent matching framework for 6D object pose estimation.
Rigidity-Aware Detection for 6D Object Pose Estimation
To address this, we propose a rigidity-aware detection method exploiting the fact that, in 6D pose estimation, the target objects are rigid.
DGSSC: A Deep Generative Spectral-Spatial Classifier for Imbalanced Hyperspectral Imagery
Specifically, the DGSSC comprises three components, a two-stage encoder, a decoder, and a classifier, which are trained in an end-to-end manner.
Few-shot Learning with Class-Covariance Metric for Hyperspectral Image Classification
Recently, embedding and metric-based few-shot learning (FSL) has been introduced into hyperspectral image classification (HSIC) and achieved impressive progress.
Semisupervised Cross-scale Graph Prototypical Network for Hyperspectral Image Classification
In practice, the acquirement of labeled samples for hyperspectral image (HSI) is time-consuming and labor-intensive.
HPRN: Holistic Prior-embedded Relation Network for Spectral Super-Resolution
Spectral super-resolution (SSR) refers to the hyperspectral image (HSI) recovery from an RGB counterpart.
SGML: A Symmetric Graph Metric Learning Framework for Efficient Hyperspectral Image Classification
To conquer these issues, we propose an efficient symmetric graph metric learning (SGML) framework by incorporating metric learning into the SSGCN paradigm.
Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification
In this paper, we propose a novel semi-supervised graph prototypical network (SSGPN) for high-precise HSIC.
Multi-direction Networks with Attentional Spectral Prior for Hyperspectral Image Classification
Convolutional neural networks (CNNs) have achieved prominent progress in recent years and demonstrated remarkable properties in spectral-spatial hyperspectral image (HSI) classification.
General Classification
Hyperspectral Image Classification
+1
Multiscale Context-Aware Ensemble Deep KELM for Efficient Hyperspectral Image Classification
Recently, multiscale spatial features have been widely utilized to improve the hyperspectral image (HSI) classification performance.
Deep Prototypical Networks with Hybrid Residual Attention for Hyperspectral Image Classification
Recently, convolutional neural networks (CNNs) have attracted enormous attention in pattern recognition and demonstrated excellent performance in hyperspectral image (HSI) classification.
AdaptiveWeighted Attention Network with Camera Spectral Sensitivity Prior for Spectral Reconstruction from RGB Images
Recent promising effort for spectral reconstruction (SR) focuses on learning a complicated mapping through using a deeper and wider convolutional neural networks (CNNs).
