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- Differential Privacy for Industrial Internet of Things: Opportunities, Applications, and Challenges201
This article conducts a comprehensive survey on the opportunities, applications, and challenges of differential privacy in IIoT, and focuses on the metrics of industrial data privacy, and analyze the contradiction between data utilization for deep models and individual privacy protection.
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- Evaluating Quality of Screen Content Images Via Structural Variation Analysis108
This paper separates structures into global and local structures, which correspond to basic and detailed perceptions of humans, respectively, and systematically combines the measurements of variations in the above-stated two types of structures to yield the final quality estimation of screen content images.
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- Boundary uncertainty aware network for automated polyp segmentation87
A novel boundary uncertainty aware network (BUNet) is proposed for precise and robust colorectal polyp segmentation and is superior to thirteen competing methods in terms of both effectiveness and generalization ability.
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- Reduced-Reference Quality Assessment of Point Clouds via Content-Oriented Saliency Projection61
This letter proposes a novel and efficient Reduced-Reference quality metric for point clouds, which is based on Content-oriented sAliency Projection (RR-CAP), and makes the first attempt to simplify reference and distorted point clouds into projected saliency maps with a downsampling operation.
- Multi-scale Enhanced Graph Convolutional Network for Early Mild Cognitive Impairment Detection61
A novel framework based on multi-scale enhanced GCN (MSE-GCN) is designed in this paper, which fuses the functional and structural information from the resting-state functional magnetic resonance imaging and diffusion tensor imaging, respectively to detect EMCI.
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- Morphology-based deep learning enables accurate detection of senescence in mesenchymal stem cell cultures52
A successful morphology-based Cascade region-based convolution neural network system for detecting senescent MSCs, which can automatically locate single cells of different sizes and shapes in multicellular images and assess their senescence state, showing promising performance in both chronic and acute MSC senescence.
- PSNet: Parallel Symmetric Network for Video Salient Object Detection52
A VSOD network with up and down parallel symmetry, named PSNet is proposed and the Importance Perception Fusion (IPF) module is used to fuse the features from two parallel branches according to their different importance in different scenarios.
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- Adaptive Mixed-Scale Feature Fusion Network for Blind AI-Generated Image Quality Assessment50
A novel blind image quality assessment (IQA) network, named AMFF-Net, for AGIs, inspired by the characteristics of the human visual system and motivated by the observation that “visual quality” and “authenticity” are characterized by both local and global aspects.
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- Bipartite Graph-based Discriminative Feature Learning for Multi-View Clustering44
Bipartite graph-based discriminative feature learning for multi-view clustering is proposed, which combines bipartitegraph learning and discriminatives feature learning to a unified framework and achieves better or comparable clustering performance than the results of state-of-the-art methods.
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- Blind stereoscopic 3D image quality assessment via analysis of naturalness, structure, and binocular asymmetry39
Experimental results on four publicly available databases demonstrate that the proposed method of S3D quality assessment is superior to several mainstream image quality assessment (IQA) metrics.
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- AMD-GAN: Attention encoder and multi-branch structure based generative adversarial networks for fundus disease detection from scanning laser ophthalmoscopy images34
Experimental results demonstrate that the proposed generative adversarial network called AMD-GAN based on the attention encoder (AE) and multi-branch (MB) structure for fundus disease detection from SLO images is promising in detecting the diseased and normalSLO images with the experts labeling.
- A two-channel convolutional neural network for image super-resolution34
Experimental results demonstrate that the proposed method outperforms the existing methods in accuracy and visual impression and reduces the computational complexity of the overall SR operation.
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- Perceptual objective quality assessment of stereoscopic stitched images19
A new perceptual objective stereoscopic stitched image quality assessment (S-SIQA) method by considering different distortion types in the existing stitching methods, including color distortion, ghost distortion, structure distortion(shape distortion, information loss), and disparity distortion.
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- Efficient Multiple Kernel Clustering via Spectral Perturbation12
The proposed MKC method recovers the optimal clustering partition from base partitions by maximizing the eigen gaps to approximate the perturbation errors, and an equivalent optimization objective function is introduced to obtain base partitions.
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- Shape-optimizing mesh warping method for stereoscopic panorama stitching7
A novel shape-optimizing mesh warping method for stereoscopic panorama stitching, which aims to resolve shape distortion and unnatural rotation of traditional stitching methods, simultaneously coping with the challenges, misalignment, and stereoscopic inconsistency is proposed.
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- Enhancing Incomplete Multi-modal Brain Tumor Segmentation with Intra-modal Asymmetry and Inter-modal Dependency2
A novel approach that enhances the BTS model from two perspectives is proposed, introducing a pre-training stage that generates a diverse pre-training dataset covering a wide range of different combinations of tumor shapes and brain anatomy and a post-training stage that enables the model to reconstruct missing modalities in the prediction results when only partial modalities are available.
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- A hybrid Mamba-Transformer model with progressive feature enhancement for medical image segmentation–
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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