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Works111 from public data
- Understanding adversarial attacks on deep learning based medical image analysis systems567
It is found that medical DNN models can be more vulnerable to adversarial attacks compared to models for natural images, according to two different viewpoints, and surprisingly, medical adversarialattacks can be easily detected.
- Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study351
The UG2+ challenge Track 2 competition in IEEE CVPR 2019 is launched, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios.
- Frequency-Aware Feature Fusion for Dense Image Prediction325
Comprehensive visualization and quantitative analysis demonstrate that FreqFusion effectively improves feature consistency and sharpens object boundaries, and extensive experiments across various dense prediction tasks confirm its effectiveness.
- Beyond Triplet Loss: Person Re-Identification With Fine-Grained Difference-Aware Pairwise Loss230
This work introduces a novel pairwise loss function that enables ReID models to learn the fine-grained features by adaptively enforcing an exponential penalization on the images of small differences and a bounded penalization of large differences.
- Multitask AET with Orthogonal Tangent Regularity for Dark Object Detection188
This work proposes a novel multitask auto encoding transformation (MAET) model which is able to explore the intrinsic pattern behind illumination translation and achieves the object detection task by decoding the bounding box coordinates and classes.
- Goal-Oriented Gaze Estimation for Zero-Shot Learning171
This work introduces a novel goal-oriented gaze estimation module (GEM) to improve the discriminative attribute localization based on the class-level attributes for ZSL and implies the promising benefits of collecting human gaze dataset and automatic gaze estimation algorithms on high-level computer vision tasks.
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- Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI101
A systematic review of the literature in automated multiple sclerosis lesion segmentation based on deep learning and gives a quantitative comparison of the methods reviewed through two metrics: Dice Similarity Coefficient and Positive Predictive Value.
- Fixed pattern noise reduction for infrared images based on cascade residual attention CNN97
An innovative cascade convolution neural network model with residual skip connections is designed to realize single frame blind FPNR operation without any parameter tuning, and a coarse-fine convolution (CF-Conv) unit is introduced to extract complementary features in various scales and fuse them to pick more spatial information.
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- Revisiting Domain Generalized Stereo Matching Networks from a Feature Consistency Perspective91
A simple pixel-wise contrastive learning across the viewpoints for stereo matching performance to unseen domains is proposed, which achieves superior performance over several state-of-the-art networks.
- Explainable Diabetic Retinopathy Detection and Retinal Image Generation86
By determining and isolating the neuron activation patterns on which diabetic retinopathy (DR) detector relies to make decisions, it is demonstrated the direct relation between the isolated neuron activation and lesions for a pathological explanation.
- Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question Answering79
A novel Chest-Xray Different Visual Question Answering (VQA) task that attempts to answer several questions on both diseases and, more importantly, the differences between them, and a novel expert knowledge-aware graph representation learning model to address this task.
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- Opportunities and challenges of using generative AI to personalize educational assessment52
The opportunities and challenges of integrating GenAI into personalized educational assessments regarding its potential risks to the assessment’s core values of validity, reliability, and fairness are elaborated.
- BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets52
This resource describes a collection of neurons from a variety of light microscopy-based datasets, which can serve as a gold standard for testing automated tracing algorithms, as shown by comparison of the performance of 35 algorithms.
- Semi-supervised Learning for Biomedical Image Segmentation via Forest Oriented Super Pixels(Voxels)48
This paper proposes to construct forest oriented super pixels to augment the standard random forest classifier, in which super pixels(voxels) are built upon the forest based code.
- Zhejiang University at ImageCLEF 2019 Visual Question Answering in the Medical Domain.47
A novel convolutional neural network based on VGG16 network and Global Average Pooling strategy to extract visual features to effectively capture the medical image features under small training set of ImageCLEF 2019.
- Hyperspectral anomaly detection by local joint subspace process and support vector machine46
The proposed hyperspectral anomaly detection algorithm based on local joint subspace process and support vector machine (SVM) has shown a superior performance on both synthetic and real-world datasets.
- Interpretable medical image Visual Question Answering via multi-modal relationship graph learning45
A novel VQA method is proposed by constructing three different relationship graphs: spatial relationships, semantic relationships, and implicit relationship graphs on the image regions, questions, and semantic labels, which leveraged graph attention to learn the logical reasoning paths for different questions.
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- Rethinking masked image modelling for medical image representation38
This work proposes and introduces two mutual comprehensive masking strategies, knowledge-driven masking (KDM), and sentence-driven masking (SDM), and substantially outperforms ImageNet pre-training, MIM-based pre-training, and medical image-report pre-training counterparts.
- EtinyNet: Extremely Tiny Network for TinyML35
There are many AI applications in high-income countries because their implementation depends on expensive GPU cards and reliable power supply, so it is important to deploy AI in resource-poor settings on cheaper and low-power devices.
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- Unsupervised Ensemble Strategy for Retinal Vessel Segmentation31
This work proposes a novel unsupervised ensemble strategy to automatically combine multiple segmentation results for an accurate result, and shows superior performance over the state-of-the-art on the DRIVE, STARE, CHASE_DB1 datasets.
- Exploring the Usage of Pre-trained Features for Stereo Matching29
This paper develops the deformable neck (DN) that decouples the network architectures of pre-trained backbones and stereo matching networks, and reveals the crucial factors that influence using pre-trained features for stereo matching.
- Target tracking from infrared imagery via an improved appearance model29
This work proposes an infrared target tracking method that combines the Sparsity-based Generative Model and Guided Filter-based Discriminative Classifier with the particle filter and utilizes the joint confidence values to measure the candidate templates for the target position estimation.
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- Fully automated leg tracking of Drosophila neurodegeneration models reveals distinct conserved movement signatures28
A machine-learning image-analysis program is developed, Feature Learning-based LImb segmentation and Tracking (FLLIT), that automatically tracks leg claw positions of freely moving flies recorded on high-speed video, producing a series of gait measurements that suggest the behavioral phenotype depends on the neurons affected rather than the specific nature of the mutation.
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- AttR2U-Net: A Fully Automated Model for MRI Nasopharyngeal Carcinoma Segmentation Based on Spatial Attention and Residual Recurrent Convolution25
A novel AttR2U-Net model is proposed which automatically and accurately segments nasopharyngeal carcinoma from MRI images and achieves the best performance compared with six other state-of-the-art image segmentation models.
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- RGB-IR Cross Input and Sub-Pixel Upsampling Network for Infrared Image Super-Resolution25
The RGB-IR cross input and sub-pixel upsampling network is proposed to increase the spatial resolution of an Infrared (IR) image by combining it with a color image of higher spatial resolution obtained with a different imaging modality.
- RealX3D: A Physically-Degraded 3D Benchmark for Multi-view Visual Restoration and Reconstruction23
This work introduces RealX3D, a real capture benchmark for multi-view restoration and reconstruction under real-world degradations, organized into four families spanning nine controlled settings that include motion and defocus blur, low-light, view-varying exposure, smoke, dynamic occlusion, and reflection.
- InsTaG: Learning Personalized 3D Talking Head from Few-Second Video23
InsTaG is introduced, a 3D talking head synthesis framework that allows a fast learning of realistic personalized 3D talking head from few training data and achieves high-quality and fast adaptation while preserving high-level personalization and efficiency.
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- Sex differences in the association of treatment-resistant schizophrenia and serum interleukin-6 levels22
Evidence is provided supporting the hypothesis that the inflammatory response system (IRS) may play a role in the pathogenesis of TRS in a sex-dependent manner and sex differences in the immune dysfunction of individuals with schizophrenia cannot be neglected.
- The Etiology of Auditory Hallucinations in Schizophrenia: From Multidimensional Levels21
Main findings include gene polymorphisms, glutamate level change, electroencephalographic alterations, and abnormalities of white matter fasciculi, cortical structure, and cerebral activities, especially in multiple regions, including auditory and language networks.
- City-Scale Distance Sensing via Bispectral Light Extinction in Bad Weather21
A novel city-scale distance sensing algorithm based on atmosphere optics is proposed by leveraging the difference of extinction coefficient between two specifically selected near infrared wavelengths by maximising the extinction coefficient difference while minimizing the influence of building’s reflectance variance.
- Semi-Supervised Learning in Medical Images Through Graph-Embedded Random Forest19
A novel semi-supervised learning algorithm to boost the performance of random forest under limited labeled data by exploiting the local structure of unlabeled data and replacing it with a graph-embedded entropy which is more reliable for insufficient labeled data scenario.
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- Exosome and exosomal contents in schizophrenia16
The current understanding of the relationship between exosomes and schizophrenia is discussed, focusing on the role of exosomal contents in this disease, and insights into the potential use ofExosomes as biomarkers for the diagnosis and treatment of schizophrenia are provided.
- Sketch-based semantic retrieval of medical images15
A sketch-based medical image retrieval (SBMIR) system that enables users to find images of interest without the need for example images, and provides on-demand, customizable medical image retrieval, thereby expanding the utility of medical image databases.
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- Information bottleneck and selective noise supervision for zero-shot learning14
This work proposes a novel bidirectional embedding based generative model with a tight visual-semantic coupling constraint that outperforms the state-of-the-art methods in different ZSL settings on most benchmark datasets.
- Multiple serum anti-glutamate receptor antibody levels in clozapine-treated/naïve patients with treatment-resistant schizophrenia12
The findings suggest that TRS may related to increased anti-glutamate receptor antibody levels and provide further evidence that glutamatergic dysfunction and immune processes may contribute to the pathogenesis of TRS.
- Deficits in Sense of Body Ownership, Sensory Processing, and Temporal Perception in Schizophrenia Patients With/Without Auditory Verbal Hallucinations12
An important finding was that patients with AVHs did not show a reduction in RHI under asynchronous conditions, which emphasize the disturbances of the sense of body ownership in schizophrenia patients with/without AVHS and the associations with AVhs.
- Hyperspectral Anomaly Detection with Harmonic Analysis and Low-Rank Decomposition11
A single-pixel-related HA was introduced to reduce dimension and remove redundant information in the original hyperspectral image (HSI) and a novel background dictionary construction method was proposed to acquire the initial smoothed images suppressing some isolated noise, while simultaneously constructing a discriminative background dictionary.
- People Taking Photos That Faces Never Share: Privacy Protection and Fairness Enhancement from Camera to User10
A novel lightweight Flow-based Face Encryption Method (FFEM) on the local embedded system privately connected to the camera, minimizing the risk of eavesdropping during data transmission and could protect privacy and enhance fairness with minimal effect on high-level downstream task.
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- Virtual Blood Vessels in Complex Background Using Stereo X-Ray Images10
A fully automatic system to reconstruct and visualize 3D blood vessels in Augmented Reality (AR) system from stereo X-ray images with bones and body fat, including initial matching using affine transform and dense matching using Hungarian algorithm guided by Gaussian regression is proposed.
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- Expert Uncertainty and Severity Aware Chest X-Ray Classification by Multi-Relationship Graph Learning7
A multi-relationship graph learning method with an expert uncertainty-aware loss function that can also interpret the disease classification results and shows that models considering disease severity and uncertainty outperform previous state-of-the-art methods.
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- Graph interaction for automated diagnosis of thoracic disease using x-ray images4
A novel framework is proposed: Feature map Graph Representational Probabilistic Class Activation Map (FGR-PCAM) to learn the graph structure of lesion-specific features and consider these relationships while also leveraging the localization ability of PCAM.
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- Name Your Colour For the Task: Artificially Discover Colour Naming via Colour Quantisation Transformer2
A novel colour quantisation transformer, CQFormer, that quantises colour space while maintaining the accuracy of machine recognition on the quantised images, and observes the consistent evolution pattern between the authors' artificial colour system and basic colour terms across human languages.
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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 2026-10-16. 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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