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Found 18 papers, 5 papers with code
Underwater Organism Color Enhancement via Color Code Decomposition, Adaptation and Interpolation
ColorCode offers three key features: 1) color enhancement, producing an enhanced image with a fixed color; 2) color adaptation, enabling controllable adjustments of long-wavelength color components using guidance images; and 3) color interpolation, allowing for the smooth generation of multiple colors through continuous sampling of the color code.
Improving Fast Adversarial Training via Self-Knowledge Guidance
Based on the analysis, we mainly attribute the observed misalignment and disparity to the imbalanced optimization in FAT, which motivates us to optimize different training data adaptively to enhance robustness.
CFVNet: An End-to-End Cancelable Finger Vein Network for Recognition
BWR-ROIAlign can directly plug into the model to introduce the above features for DCNN-based finger vein recognition systems.
Unrevealed Threats: A Comprehensive Study of the Adversarial Robustness of Underwater Image Enhancement Models
In this paper, we propose a general adversarial attack protocol.
Improving Fast Adversarial Training Paradigm: An Example Taxonomy Perspective
However, FAT suffers from catastrophic overfitting, which leads to a performance drop compared with multi-step adversarial training.
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.
Exploring the Coordination of Frequency and Attention in Masked Image Modeling
To tackle this issue, we propose the Frequency \& Attention-driven Masking and Throwing Strategy (FAMT), which can extract semantic patches and reduce the number of training patches to boost model performance and training efficiency simultaneously.
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.
