Diffusion Model-Based Visual Compensation Guidance and Visual Difference Analysis for No-Reference Image Quality Assessment.

Existing free-energy guided No-Reference Image Quality Assessment (NR-IQA) methods continue to face challenges in effectively restoring complexly distorted images. The features guiding the main network for quality assessment lack interpretability, and efficiently leveraging high-level feature information remains a significant challenge. As a novel class of state-of-the-art (SOTA) generative model, the diffusion model exhibits the capability to model intricate relationships, enhancing image restoration effectiveness.

Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation.

We introduce Hyper-YOLO, a new object detection method that integrates hypergraph computations to capture the complex high-order correlations among visual features. Traditional YOLO models, while powerful, have limitations in their neck designs that restrict the integration of cross-level features and the exploitation of high-order feature interrelationships. To address these challenges, we propose the Hypergraph Computation Empowered Semantic Collecting and Scattering (HGC-SCS) framework, which transposes visual feature maps into a semantic space and constructs a hypergraph for high-order message propagation.

Cross-Modal 3D Shape Retrieval via Heterogeneous Dynamic Graph Representation.

Cross-modal 3D shape retrieval is a crucial and widely applied task in the field of 3D vision. Its goal is to construct retrieval representations capable of measuring the similarity between instances of different 3D modalities. However, existing methods face challenges due to the performance bottlenecks of single-modal representation extractors and the modality gap across 3D modalities.

Filter Pruning by High-Order Spectral Clustering.

Large amount of redundancy is widely present in convolutional neural networks (CNNs). Identifying the redundancy in the network and removing the redundant filters is an effective way to compress the CNN model size with a minimal reduction in performance. However, most of the existing redundancy-based pruning methods only consider the distance information between two filters, which can only model simple correlations between filters.

BiTS-SleepNet: An Attention-Based Two Stage Temporal-Spectral Fusion Model for Sleep Staging With Single-Channel EEG.

Automated sleep staging is crucial for assessing sleep quality and diagnosing sleep-related diseases. Single-channel EEG has attracted significant attention due to its portability and accessibility. Most existing automated sleep staging methods often emphasize temporal information and neglect spectral information, the relationship between sleep stage contextual features, and transition rules between sleep stages.

DARCS: Memory-Efficient Deep Compressed Sensing Reconstruction for Acceleration of 3D Whole-Heart Coronary MR Angiography.

Three-dimensional coronary magnetic resonance angiography (CMRA) requires reconstruction algorithms that can significantly suppress the artifacts encountered in heavily undersampled acquisitions. While unrolling-based deep reconstruction methods have achieved state-of-the-art performance on 2D image reconstruction, their application in 3D reconstruction is hindered by the large amount of memory required to train an unrolled network. In this study, we propose a memoryefficient deep compressed sensing method that employs a sparsifying transform based on a pre-trained artifact estimation network.

GBCT: Efficient and Adaptive Clustering via Granular-Ball Computing for Complex Data.

Traditional clustering algorithms often focus on the most fine-grained information and achieve clustering by calculating the distance between each pair of data points or implementing other calculations based on points. This way is not inconsistent with the cognitive mechanism of "global precedence" in the human brain, resulting in those methodsbad performance in efficiency, generalization ability, and robustness. To address this problem, we propose a new clustering algorithm called granular-ball clustering via granular-ball computing.

RBF NN-Enabled Adaptive Filter for Any Type of Noise.

The brief proposes a radial basis function (RBF) neural network (NN)-enabled adaptive filter (AF) algorithm, which consists of two stages. The first stage is a data-driven (DD) preprocessing part, and the RBF NN is to fit the probability density function (pdf) of the noise. The second stage is a model-driven filtering part, the RBF NN works as the cost function of the adaptive filtering, and an adaptive gradient ascent algorithm is obtained by maximizing the RBF NN.

Exploring Attention and Self-Supervised Learning Mechanism for Graph Similarity Learning.

Graph similarity estimation is a challenging task due to the complex graph structures. Though important and well-studied, three critical aspects are yet to be fully handled in a unified framework: 1) how to learn richer cross-graph interactions from a pairwise node perspective; 2) how to map the similarity matrix into a similarity score by exploiting the inherent structure in the similarity matrix; and 3) how to establish a self-supervised learning mechanism for graph similarity learning. To solve these issues, we explore multiple attention and self-supervised mechanisms for graph similarity learning in this work.

Manifold Based Multi-View K-Means.

Although numerous clustering algorithms have been developed, many existing methods still rely on the K-means technique to identify clusters of data points. However, the performance of K-means is highly dependent on the accurate estimation of cluster centers, which is challenging to achieve optimally. Furthermore, it struggles to handle linearly non-separable data.

Enhancing SMT Quality and Efficiency With Self-Adaptive Collaborative Optimization.

In the field of smart surface mount technology (SMT) production, integrating machines through a cyber-physical system (CPS) architecture holds significant potential for improving assembly quality and efficiency. However, fully unifying inspection and production systems to effectively address assembly-related quality issues remains a challenge. This study seeks to close these gaps by introducing collaborative optimization methods to ensure seamless operations.

Multilayer Brain Networks for Enhanced Decoding of Natural Hand Movements and Kinematic Parameters.

Decoding natural hand movements using Movement-Related Cortical Potentials (MRCPs) features is crucial for the natural control of neuroprosthetics. However, current research has primarily focused on the characteristics of individual channels or on brain networks within a single frequency or time segment, overlooking the potential of brain networks across multiple time-frequency domains. To address this problem, our study investigates the application of multilayer brain networks (MBNs) in decoding natural hand movements and kinematic parameters, using a combination of MRCPs features and MBNs metrics.

Training-free image style alignment for domain shift on handheld ultrasound devices.

Handheld ultrasound devices face usage limitations due to user inexperience and cannot benefit from supervised deep learning without extensive expert annotations. Moreover, the models trained on standard ultrasound device data are constrained by training data distribution and perform poorly when directly applied to handheld device data. In this study, we propose the Training-free Image Style Alignment (TISA) to align the style of handheld device data to those of standard devices.

SinKD: Sinkhorn Distance Minimization for Knowledge Distillation.

Knowledge distillation (KD) has been widely adopted to compress large language models (LLMs). Existing KD methods investigate various divergence measures including the Kullback-Leibler (KL), reverse KL (RKL), and Jensen-Shannon (JS) divergences. However, due to limitations inherent in their assumptions and definitions, these measures fail to deliver effective supervision when a distribution overlap exists between the teacher and the student.

A Forward and Backward Compatible Framework for Few-Shot Class-Incremental Pill Recognition.

Automatic pill recognition (APR) systems are crucial for enhancing hospital efficiency, assisting visually impaired individuals, and preventing cross-infection. However, most existing deep learning-based pill recognition systems can only perform classification on classes with sufficient training data. In practice, the high cost of data annotation and the continuous increase in new pill classes necessitate the development of a few-shot class-incremental pill recognition (FSCIPR) system.

EvaSurf: Efficient View-Aware Implicit Textured Surface Reconstruction.

Reconstructing real-world 3D objects has numerous applications in computer vision, such as virtual reality, video games, and animations. Ideally, 3D reconstruction methods should generate high-fidelity results with 3D consistency in real-time. Traditional methods match pixels between images using photo-consistency constraints or learned features, while differentiable rendering methods like Neural Radiance Fields (NeRF) use differentiable volume rendering or surface-based representation to generate high-fidelity scenes.

Resting-State Electroencephalographic Signatures Predict Treatment Efficacy of tACS for Refractory Auditory Hallucinations in Schizophrenic Patients.

Transcranial alternating current stimulation (tACS) has been reported to treat refractory auditory hallucinations in schizophrenia. Despite diligent efforts, it is imperative to underscore that tACS does not uniformly demonstrate efficacy across all patients as with all treatments currently employed in clinical practice. The study aims to find biomarkers predicting individual responses to tACS, guiding treatment decisions, and preventing healthcare resource wastage.

Clustering Interval and Triangular Granular Data: Modeling, Execution, and Assessment.

In current granular clustering algorithms, numeric representatives were selected by users or an ordinary strategy, which seemed simple; meanwhile, weight settings for granular data could not adequately express their structural characteristics. Aiming at these problems, in this study, a new scheme called a granular weighted kernel fuzzy clustering (GWKFC) algorithm is put forward. We propose the representative selection and granularity generation (RSGG) algorithm enlightened by the density peak clustering (DPC) algorithm.

Synergizing Anti-Cancer Drug Combinations With Dual-View Hypergraph Representation Fusion.

Drug combination therapy plays a vital role in disease treatment, including cancer, as it contributes to treatment efficacy and can alleviate the effect of drug resistance. Although clinical trials and screening may provide valuable information about synergistic drug combinations, they suffer from challenging combinatorial space. Multiple methods are proposed to address those issues.

Iteratively Capped Reweighting Norm Minimization with Global Convergence Guarantee for Low-Rank Matrix Learning.

In recent years, a large number of studies have shown that low rank matrix learning (LRML) has become a popular approach in machine learning and computer vision with many important applications, such as image inpainting, subspace clustering, and recommendation system. The latest LRML methods resort to using some surrogate functions as convex or nonconvex relaxation of the rank function. However, most of these methods ignore the difference between different rank components and can only yield suboptimal solutions.

Automatic Segmentation of Bone Graft in Maxillary Sinus via Distance Constrained Network Guided by Prior Anatomical Knowledge.

Maxillary Sinus Lifting is a crucial surgical procedure for addressing insufficient alveolar bone mass andsevere resorption in dental implant therapy. To accurately analyze the geometry changesof the bone graft (BG) in the maxillary sinus (MS), it is essential to perform quantitative analysis. However, automated BG segmentation remains a major challenge due to the complex local appearance, including blurred boundaries, lesion interference, implant and artifact interference, and BG exceeding the MS.

Knowledge-Reinforced Cross-Domain Recommendation.

Over the past few years, cross-domain recommendation has gained great attention to resolve the cold-start issue. Many existing cross-domain recommendation methods model a preference bridge between the source and target domains to transfer preferences by the overlapping users. However, when there are insufficient cross-domain users available to bridge the two domains, it will negatively impact the recommender system's accuracy (ACC) and performance.

Motion Editing for Quadruped Characters via Latent Frequency Embedding.

The accurate and diversified generation of motion sequences for virtual characters poses both an enticing and challenging task within the domain of 3D animation and game content production. To achieve a natural and realistic full-body motion, the movements of virtual characters must adhere to a set of constraints, promoting reliable and seamless pose-changing. This study presents a two-stage model specifically designed to learn Inverse Kinematics (IK) constraints from the representative quadruped character poses.

Automated Screening Network for Fetal Closed Spina Bifida With Semantic Enhancement and Projected Attention.

Closed spina bifida is a high-incidence developmental disorder among rare fetal diseases. Its signs in ultrasound imaging are subtle, making it prone to misdiagnosis and heavily reliant on sonographers' experience. Therefore, we propose a novel semantic enhancement framework incorporating projected attention for the automated screening of closed spina bifida through precise landmark detection.

Saliency Segmentation Oriented Deep Image Compression with Novel Bit Allocation.

Image compression distortion can cause performance degradation of machine analysis tasks, therefore recent years have witnessed fast progress in developing deep image compression methods optimized for machine perception. However, the investigation still lacks for saliency segmentation. First, in this paper we propose a deep compression network increasing local signal fidelity of important image pixels for saliency segmentation, which is different from existing methods utilizing the analysis network loss for backward propagation.