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- Antonio Robles‐KellySuggested from co-authorship
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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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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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