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Found 15 papers, 5 papers with code
HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature Embedding
To address this limitation, this study rethinks hyperspectral intrinsic image decomposition for classification tasks by introducing deep feature embedding.
Beyond Sharing Weights in Decoupling Feature Learning Network for UAV RGB-Infrared Vehicle Re-Identification
Moreover, to meet cross-modality discrepancy and orientation discrepancy challenges, we present a hybrid weights decoupling network (HWDNet) to learn the shared discriminative orientation-invariant features.
Masked Spatial-Spectral Autoencoders Are Excellent Hyperspectral Defenders
Deep learning methodology contributes a lot to the development of hyperspectral image (HSI) analysis community.
A CNN with Noise Inclined Module and Denoise Framework for Hyperspectral Image Classification
Finally, the CNN with noise inclined module and the denoise framework is developed to obtain discriminative features and provides good classification performance of hyperspectral image.
Transferable Physical Attack against Object Detection with Separable Attention
Transferable adversarial attack is always in the spotlight since deep learning models have been demonstrated to be vulnerable to adversarial samples.
Self-aligned Spatial Feature Extraction Network for UAV Vehicle Re-identification
Extensive experiments are conducted on UAV-VeID dataset, and our method achieves the best performance compared with recent ReID works.
Few-shot Object Detection with Self-adaptive Attention Network for Remote Sensing Images
Particularly, in order to fit the object detection settings, our proposed few-shot detector concentrates on the relations that lie in the level of objects instead of the full image with the assistance of Self-Adaptive Attention Network (SAAN).
Few-shot Object Detection with Feature Attention Highlight Module in Remote Sensing Images
Then the object-specific features are delivered to the two-stage detection backend for the detection results.
Statistical Loss and Analysis for Deep Learning in Hyperspectral Image Classification
To overcome this problem, this work characterizes each class from the hyperspectral image as a statistical distribution and further develops a novel statistical loss with the distributions, not directly with samples for deep learning.
Deep Manifold Embedding for Hyperspectral Image Classification
Deep learning methods have played a more and more important role in hyperspectral image classification.
Hyperspectral Image Classification With Context-Aware Dynamic Graph Convolutional Network
In hyperspectral image (HSI) classification, spatial context has demonstrated its significance in achieving promising performance.
Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification
To alleviate this shortcoming, we consider employing the recently proposed Graph Convolutional Network (GCN) for hyperspectral image classification, as it can conduct the convolution on arbitrarily structured non-Euclidean data and is applicable to the irregular image regions represented by graph topological information.
A novel statistical metric learning for hyperspectral image classification
In this paper, a novel statistical metric learning is developed for spectral-spatial classification of the hyperspectral image.
An End-to-End Joint Unsupervised Learning of Deep Model and Pseudo-Classes for Remote Sensing Scene Representation
Finally, joint learning of the pseudo-center loss and the pseudo softmax loss which is formulated with the samples and the pseudo labels is developed for unsupervised remote sensing scene representation to obtain discriminative representations from the scenes.
Diversity in Machine Learning
Even though the diversity plays an important role in machine learning process, there is no systematical analysis of the diversification in machine learning system.
