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Learning Feature Restoration Transformer for Robust Dehazing Visual Object Tracking
In recent years, deep-learning-based visual object tracking has obtained promising results. However, a drastic performance drop is observed when...
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CD-iNet: Deep Invertible Network for Perceptual Image Color Difference Measurement
Image color difference (CD) measurement, a crucial concept in color science and imaging technology, aims to quantify the perceived difference between...
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HyperMatch: long-form text matching via hypergraph convolutional networks
Semantic text matching plays a vital role in diverse domains, such as information retrieval, question answering, and recommendation. However, longer...
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Hierarchical adaptive evolution framework for privacy-preserving data publishing
The growing need for data publication and the escalating concerns regarding data privacy have led to a surge in interest in Privacy-Preserving Data...
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Adaptive shift graph convolutional neural network for hand gesture recognition based on 3D skeletal similarity
Graph convolutional neural networks (GCNs) have shown promising results in the field of hand gesture recognition based on 3D skeletal data. However,...
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Intelligent Personality Assessment and Verification from Handwriting using Machine Learning
It is possible to tell a lot about a person just by looking at their handwriting. The way someone writes might tell you a lot about who, they are as...
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M2AST:MLP-mixer-based adaptive spatial-temporal graph learning for human motion prediction
Human motion prediction is a challenging task in human-centric computer vision, involving forecasting future poses based on historical sequences....
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Lightweight model for small target detection of SAR images of ships based on NWD loss
Synthetic Aperture Radar (SAR) has the advantages of all-weather and high resolution, and is an effective tool for ship monitoring. SAR image ship...
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DiffCAS: diffusion based multi-attention network for segmentation of 3D coronary artery from CT angiography
Automatic segmentation of 3D coronary arteries from computed tomography angiography (CTA) is an indispensable part of accurate and efficient coronary...
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Image inpainting based on tensor ring decomposition with generative adversarial network
Image inpainting is a fundamental task in the field of computer vision. However, there are three major challenges associated with this technique: (1)...
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Anomaly analytics in data-driven machine learning applications
Machine learning is used widely to create a range of prediction or classification models. The quality of the machine learning (ML) models depends not...
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Temporal analysis of computational economics: a topic modeling approach
This study offers a comprehensive investigation into the thematic evolution within computational economics over the past two decades, leveraging...
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A combined non-convex TVp and wavelet \(\ell _1\)-norm approach for image deblurring via split Bregman method
Image deblurring is one of the most fundamental problems in the image processing and computer vision fields. The methods based on total variation are...
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Multi-source-free Domain Adaptive Object Detection
To enhance the transferability of object detection models in real-world scenarios where data is sampled from disparate distributions, considerable...
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De-confounding representation learning for counterfactual inference on continuous treatment via generative adversarial network
Counterfactual inference for continuous rather than binary treatment variables is more common in real-world causal inference tasks. While there are...
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Exploiting Diffusion Prior for Real-World Image Super-Resolution
We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution....
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Certifying Accuracy, Privacy, and Robustness of ML-Based Malware Detection
Recent advances in artificial intelligence (AI) are radically changing how systems and applications are designed and developed. In this context, new...
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Gradient-based explanation for non-linear non-parametric dimensionality reduction
Dimensionality reduction (DR) is a popular technique that shows great results to analyze high-dimensional data. Generally, DR is used to produce...
