Replay Without Saving: Prototype Derivation and Distribution Rebalance for Class-Incremental Semantic Segmentation.

The research of class-incremental semantic segmentation (CISS) seeks to enhance semantic segmentation methods by enabling the progressive learning of new classes while preserving knowledge of previously learned ones. A significant yet often neglected challenge in this domain is class imbalance. In CISS, each task focuses on different foreground classes, with the training set for each task exclusively comprising images that contain these currently focused classes.

Evolutionary Multitask Optimization for Multiform Feature Selection in Classification.

Feature selection (FS) is a significant research topic in machine learning and artificial intelligence, but it becomes complicated in the high dimensional search space due to the vast number of features. Evolutionary computation (EC) has been widely used in solving FS by modeling it as an expensive wrapper-form optimization task, where a classifier is used to obtain classification accuracy for fitness evaluation (FE). In this article, we propose that the FS problem can be also modeled as a cheap filter-form optimization task, where the FE is based on the relevance and redundancy of the selected features.

HATNet: EEG-Based Hybrid Attention Transfer Learning Network for Train Driver State Detection.

Electroencephalography (EEG) is widely utilized for train driver state detection due to its high accuracy and low latency. However, existing methods for driver status detection rarely use the rich physiological information in EEG to improve detection performance. Moreover, there is currently a lack of EEG datasets for abnormal states of train drivers.

Shell-Guided Compression of Voxel Radiance Fields.

In this paper, we address the challenge of significant memory consumption and redundant components in large-scale voxel-based model, which are commonly encountered in real-world 3D reconstruction scenarios. We propose a novel method called Shell-guided compression of Voxel Radiance Fields (SVRF), aimed at optimizing voxel-based model into a shell-like structure to reduce storage costs while maintaining rendering accuracy. Specifically, we first introduce a Shell-like Constraint, operating in two main aspects: 1) enhancing the influence of voxels neighboring the surface in determining the rendering outcomes, and 2) expediting the elimination of redundant voxels both inside and outside the surface.

Image Quality Assessment: Exploring Joint Degradation Effect of Deep Network Features via Kernel Representation Similarity Analysis.

Typically, deep network-based full-reference image quality assessment (FR-IQA) models compare deep features from reference and distorted images pairwise, overlooking correlations among features from the same source. We propose a dual-branch framework to capture the joint degradation effect among deep network features. The first branch uses kernel representation similarity analysis (KRSA), which compares feature self-similarity matrices via the mean absolute error (MAE).

Breaking Boundaries: Unifying Imaging and Compression for HDR Image Compression.

High Dynamic Range (HDR) images present unique challenges for Learned Image Compression (LIC) due to their complex domain distribution compared to Low Dynamic Range (LDR) images. In coding practice, HDR-oriented LIC typically adopts preprocessing steps (e.g.

ID3RSNet: cross-subject driver drowsiness detection from raw single-channel EEG with an interpretable residual shrinkage network.

Accurate monitoring of drowsy driving through electroencephalography (EEG) can effectively reduce traffic accidents. Developing a calibration-free drowsiness detection system with single-channel EEG alone is very challenging due to the non-stationarity of EEG signals, the heterogeneity among different individuals, and the relatively parsimonious compared to multi-channel EEG. Although deep learning-based approaches can effectively decode EEG signals, most deep learning models lack interpretability due to their black-box nature.

Overview of High-Dynamic-Range Image Quality Assessment.

In recent years, the High-Dynamic-Range (HDR) image has gained widespread popularity across various domains, such as the security, multimedia, and biomedical fields, owing to its ability to deliver an authentic visual experience. However, the extensive dynamic range and rich detail in HDR images present challenges in assessing their quality. Therefore, current efforts involve constructing subjective databases and proposing objective quality assessment metrics to achieve an efficient HDR Image Quality Assessment (IQA).

GRESS: Grouping Belief-Based Deep Contrastive Subspace Clustering.

The self-expressive coefficient plays a crucial role in the self-expressiveness-based subspace clustering method. To enhance the precision of the self-expressive coefficient, we propose a novel deep subspace clustering method, named grouping belief-based deep contrastive subspace clustering (GRESS), which integrates the clustering information and higher-order relationship into the coefficient matrix. Specifically, we develop a deep contrastive subspace clustering module to enhance the learning of both self-expressive coefficients and cluster representations simultaneously.

Neural Network-Based Knowledge Transfer for Multitask Optimization.

Knowledge transfer (KT) is crucial for optimizing tasks in evolutionary multitask optimization (EMTO). However, most existing KT methods can only achieve superficial KT but lack the ability to deeply mine the similarities or relationships among different tasks. This limitation may result in negative transfer, thereby degrading the KT performance.

SRNSD: Structure-Regularized Night-Time Self-Supervised Monocular Depth Estimation for Outdoor Scenes.

Deep CNNs have achieved impressive improvements for night-time self-supervised depth estimation form a monocular image. However, the performance degrades considerably compared to day-time depth estimation due to significant domain gaps, low visibility, and varying illuminations between day and night images. To address these challenges, we propose a novel night-time self-supervised monocular depth estimation framework with structure regularization, i.

Decomposition-Estimation-Reconstruction: An Automatic and Accurate Neuron Extraction Paradigm.

The extraction of spatiotemporal neuron activity from calcium imaging videos plays a crucial role in unraveling the coding properties of neurons. While existing neuron extraction approaches have shown promising results, disturbing and scattering background and unused depth still impede their performance. To address these limitations, we develop an automatic and accurate neuron extraction paradigm, dubbed as decomposition-estimation-reconstruction (DER), consisting of D-procedure, E-procedure, and R-procedure.

Image Quality Assessment: Measuring Perceptual Degradation via Distribution Measures in Deep Feature Spaces.

This study aims to develop advanced and training-free full-reference image quality assessment (FR-IQA) models based on deep neural networks. Specifically, we investigate measures that allow us to perceptually compare deep network features and reveal their underlying factors. We find that distribution measures enjoy advanced perceptual awareness and test the Wasserstein distance (WSD), Jensen-Shannon divergence (JSD), and symmetric Kullback-Leibler divergence (SKLD) measures when comparing deep features acquired from various pretrained deep networks, including the Visual Geometry Group (VGG) network, SqueezeNet, MobileNet, and EfficientNet.

Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessment.

The statistical regularities of natural images, referred to as natural scene statistics, play an important role in no-reference image quality assessment. However, it has been widely acknowledged that screen content images (SCIs), which are typically computer generated, do not hold such statistics. Here we make the first attempt to learn the statistics of SCIs, based upon which the quality of SCIs can be effectively determined.

A Novel Multiobjective Genetic Programming Approach to High-Dimensional Data Classification.

The development of data sensing technology has generated a vast amount of high-dimensional data, posing great challenges for machine learning models. Over the past decades, despite demonstrating its effectiveness in data classification, genetic programming (GP) has still encountered three major challenges when dealing with high-dimensional data: 1) solution diversity; 2) multiclass imbalance; and 3) large feature space. In this article, we have developed a problem-specific multiobjective GP framework (PS-MOGP) for handling classification tasks with high-dimensional data.

KepSalinst: Using Peripheral Points to Delineate Salient Instances.

Salient instance segmentation (SIS) is an emerging field that evolves from salient object detection (SOD), aiming at identifying individual salient instances using segmentation maps. Inspired by the success of dynamic convolutions in segmentation tasks, this article introduces a keypoints-based SIS network (KepSalinst). It employs multiple keypoints, that is, the center and several peripheral points of an instance, as effective geometrical guidance for dynamic convolutions.

PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN With Dual-Discriminators.

Due to the light absorption and scattering induced by the water medium, underwater images usually suffer from some degradation problems, such as low contrast, color distortion, and blurring details, which aggravate the difficulty of downstream underwater understanding tasks. Therefore, how to obtain clear and visually pleasant images has become a common concern of people, and the task of underwater image enhancement (UIE) has also emerged as the times require. Among existing UIE methods, Generative Adversarial Networks (GANs) based methods perform well in visual aesthetics, while the physical model-based methods have better scene adaptability.

CSformer: Bridging Convolution and Transformer for Compressive Sensing.

Convolutional Neural Networks (CNNs) dominate image processing but suffer from local inductive bias, which is addressed by the transformer framework with its inherent ability to capture global context through self-attention mechanisms. However, how to inherit and integrate their advantages to improve compressed sensing is still an open issue. This paper proposes CSformer, a hybrid framework to explore the representation capacity of local and global features.

Impulse Noise Image Restoration Using Nonconvex Variational Model and Difference of Convex Functions Algorithm.

In this article, the problem of impulse noise image restoration is investigated. A typical way to eliminate impulse noise is to use an L norm data fitting term and a total variation (TV) regularization. However, a convex optimization method designed in this way always yields staircase artifacts.