Automatic Static Vulnerability Detection for Machine Learning Libraries: Are We There Yet?
Automatic detection of software security vulnerabilities is critical in software quality
assurance. Many static analysis tools that can help detect security vulnerabilities have been …
assurance. Many static analysis tools that can help detect security vulnerabilities have been …
Encoder deep interleaved network with multi-scale aggregation for RGB-D salient object detection
Recently, RGB-D salient object detection (SOD) has aroused widespread research interest.
Existing RGB-D SOD approaches mainly consider the cross-modal information fusion in the …
Existing RGB-D SOD approaches mainly consider the cross-modal information fusion in the …
H-ProMed: Ultrasound image segmentation based on the evolutionary neural network and an improved principal curve
The purpose of this work is to develop a method for accurate and robust prostate
segmentation in transrectal ultrasound (TRUS) images. These images are difficult to …
segmentation in transrectal ultrasound (TRUS) images. These images are difficult to …
Answering knowledge-based visual questions via the exploration of question purpose
Visual question answering has been greatly advanced by deep learning technologies, but
still remains an open problem subjected to two aspects of factors. First, previous works …
still remains an open problem subjected to two aspects of factors. First, previous works …
Automatic static bug detection for machine learning libraries: Are we there yet?
J Shin, J Wang, S Wang, N Nagappan - arxiv preprint arxiv:2307.04080, 2023 - arxiv.org
Automatic detection of software bugs is a critical task in software security. Many static tools
that can help detect bugs have been proposed. While these static bug detectors are mainly …
that can help detect bugs have been proposed. While these static bug detectors are mainly …
Progressive expansion for semi-supervised bi-modal salient object detection
Existing bi-modal salient object detection (SOD) methods primarily rely on fully supervised
training strategies that require extensive manual annotation. Undoubtedly, extensive manual …
training strategies that require extensive manual annotation. Undoubtedly, extensive manual …
Test time adaptation with regularized loss for weakly supervised salient object detection
O Veksler - Proceedings of the IEEE/CVF Conference on …, 2023 - openaccess.thecvf.com
It is well known that CNNs tend to overfit to the training data. Test-time adaptation is an
extreme approach to deal with overfitting: given a test image, the aim is to adapt the trained …
extreme approach to deal with overfitting: given a test image, the aim is to adapt the trained …
Characterizing and understanding software security vulnerabilities in machine learning libraries
The application of machine learning (ML) libraries has tremendously increased in many
domains, including autonomous driving systems, medical, and critical industries …
domains, including autonomous driving systems, medical, and critical industries …
BALQUE: Batch active learning by querying unstable examples with calibrated confidence
Active learning alleviates labeling costs by selecting and labeling the most informative
examples from an unlabeled pool. However, most existing active learning approaches …
examples from an unlabeled pool. However, most existing active learning approaches …
Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT
Supervised deep learning has become a standard approach to solving medical image
segmentation tasks. However, serious difficulties in attaining pixel-level annotations for …
segmentation tasks. However, serious difficulties in attaining pixel-level annotations for …