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When CNN Meet with ViT: Towards Semi-supervised Learning for Multi-class Medical Image Semantic Segmentation
Due to the lack of quality annotation in medical imaging community, semi-supervised learning methods are highly valued in image semantic segmentation...
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A Consistency Regularization for Certified Robust Neural Networks
A range of provable defense methods have been proposed to train neural networks that are certifiably robust to the adversarial examples. Among which,...
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Deep Mutual Distillation for Semi-supervised Medical Image Segmentation
In this paper, we focus on semi-supervised medical image segmentation. Consistency regularization methods such as initialization perturbation on two...
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An Approach to Mongolian Neural Machine Translation Based on RWKV Language Model and Contrastive Learning
Low-resource machine translation (LMT) is a challenging task, especially for languages with limited resources like Mongolian. In this paper, we...
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Meta semi-supervised medical image segmentation with label hierarchy
Semi-supervised learning (SSL) has attracted increasing attention in medical image segmentation, where the mainstream usually explores...
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Efficient Semi-supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency
Gross Target Volume (GTV) segmentation plays an irreplaceable role in radiotherapy planning for Nasopharyngeal Carcinoma (NPC). Despite that...
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A Novel DNN Object Contour Attack on Image Recognition
Deep neural networks (DNNs) have diverse applications due to their ability to learn features. However, recent studies have revealed that DNNs are...
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Learning Spatiotemporal Inconsistency via Thumbnail Layout for Face Deepfake Detection
The deepfake threats to society and cybersecurity have provoked significant public apprehension, driving intensified efforts within the realm of...
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PPS: Semi-supervised 3D Biomedical Image Segmentation via Pyramid Pseudo-Labeling Supervision
Although deep learning models have demonstrated impressive performance in various biomedical image segmentation tasks, their effectiveness heavily...
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A Textual Adversarial Attack Scheme for Domain-Specific Models
Most of the textual adversarial attack methods generate adversarial examples by searching solutions from a perturbation space, which is constructed...
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Semi-supervised Semantic Segmentation Algorithm for Video Frame Corruption
To address the problems of lack of labeled data and inaccurate segmentation in semantic segmentation of corrupted frame in surveillance video, a...
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Voice Privacy Using Time-Scale and Pitch Modification
There is a growing demand toward digitization of various day-to-day work and hence, there is a surge in use of Intelligent Personal Assistants. The...
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Pull and concentrate: improving unsupervised semantic segmentation adaptation with cross- and intra-domain consistencies
Unsupervised domain adaptation (UDA) is an important solution for the cross-domain problem in semantic segmentation. Existing segmentation UDA...
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An Efficient Computational Method to Predict Drug-Target Interactions Utilizing Structural Perturbation Method
Accurately and quickly identifying potential drug candidates for therapeutic targets (i.e., drug-target interactions, DTIs) is a basic step in the...
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TCL: Triplet Consistent Learning for Odometry Estimation of Monocular Endoscope
The depth and pose estimations from monocular images are essential for computer-aided navigation. Since the ground truth of depth and pose are...
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Explaining Siamese networks in few-shot learning
Machine learning models often struggle to generalize accurately when tested on new class distributions that were not present in their training data....
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Extreme Consistency: Overcoming Annotation Scarcity and Domain Shifts
Supervised learning has proved effective for medical image analysis. However, it can utilize only the small labeled portion of data; it fails to...
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Robust gradient aware and reliable entropy minimization for stable test-time adaptation in dynamic scenarios
Test-time adaptation (TTA) aims to provide neural networks capable of adapting to the target domain distribution using only unlabeled test data. Most...
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Consistent Semantic Attacks on Optical Flow
We present a novel approach for semantically targeted adversarial attacks on Optical Flow. In such attacks the goal is to corrupt the flow...
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Evaluating Input Perturbation Methods for Interpreting CNNs and Saliency Map Comparison
Input perturbation methods occlude parts of an input to a function and measure the change in the function’s output. Recently, input perturbation...