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Found 12 papers, 3 papers with code
Illumination Controllable Dehazing Network based on Unsupervised Retinex Embedding
On the one hand, the dehazing task is an illposedness problem, which means that no unique solution exists.
MASK-CNN-Transformer For Real-Time Multi-Label Weather Recognition
The proposed model called MASK-Convolutional Neural Network-Transformer (MASK-CT) is based on the Transformer, the convolutional process, and the MASK mechanism.
Fooling the Image Dehazing Models by First Order Gradient
In this paper, we focus on designing a group of attack methods based on first order gradient to verify the robustness of the existing dehazing algorithms.
AlignVE: Visual Entailment Recognition Based on Alignment Relations
Such approaches, however, ignore the VE's unique nature of relation inference between the premise and hypothesis.
PFENet++: Boosting Few-shot Semantic Segmentation with the Noise-filtered Context-aware Prior Mask
In this work, we revisit the prior mask guidance proposed in ``Prior Guided Feature Enrichment Network for Few-Shot Segmentation''.
Low-Rank Matrix Recovery from Noise via an MDL Framework-based Atomic Norm
The recovery of the underlying low-rank structure of clean data corrupted with sparse noise/outliers is attracting increasing interest.
Learning a Deep Part-based Representation by Preserving Data Distribution
Then one hopes to learn a new low dimensional representation which can preserve the intrinsic structure embedded in the original high dimensional data space perfectly.
Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images
In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier.
Bayes Imbalance Impact Index: A Measure of Class Imbalanced Dataset for Classification Problem
To the best of our knowledge, there is no any measurement about the extent of influence of class imbalance on the classification performance of imbalanced data.
Constrained Manifold Learning for Hyperspectral Imagery Visualization
A composite kernel is applied in manifold learning to incorporate both the spatial and spectral information of HSI in the embedded space.
Modal Regression based Atomic Representation for Robust Face Recognition
In this paper, we propose a modal regression based atomic representation and classification (MRARC) framework to alleviate such limitation.
Unsupervised Cross-Domain Recognition by Identifying Compact Joint Subspaces
By adopting a natural and widely used assumption -- "the data samples from the same class should lay on a low-dimensional subspace, even if they come from different domains", the proposed method circumvents the limitation of the global domain shift, and solves the cross-domain recognition by finding the compact joint subspaces of source and target domain.
