Boosting cross‐task adversarial attack with random blur
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22932
Abstract: Deep neural networks are highly vulnerable to adversarial examples, and these adversarial examples stay malicious when transferred to other neural networks. Many works exploit this transferability of adversarial examples to execute black‐box attacks. However, most… read more here.
Keywords: random; cross task; adversarial examples; blur ... See more keywords
Toward Robust Visual Object Tracking With Independent Target-Agnostic Detection and Effective Siamese Cross-Task Interaction
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2023.3246800
Abstract: Advanced Siamese visual object tracking architectures are jointly trained using pair-wise input images to perform target classification and bounding box regression. They have achieved promising results in recent benchmarks and competitions. However, the existing methods… read more here.
Keywords: cross task; interaction; target agnostic; task ... See more keywords
Dynamic Cross-Task Representation Adaptation for Clinical Targets Co-Segmentation in CT Image-Guided Post-Prostatectomy Radiotherapy
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Medical Imaging"
DOI: 10.1109/tmi.2022.3223405
Abstract: Adjuvant and salvage radiotherapy after radical prostatectomy requires precise delineations of prostate bed (PB), i.e., the clinical target volume, and surrounding organs at risk (OARs) to optimize radiotherapy planning. Segmenting PB is particularly challenging even… read more here.
Keywords: clinical targets; cross task; task representation; radiotherapy ... See more keywords
Semi-Supervised Image Classification With Self-Paced Cross-Task Networks
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DOI: 10.1109/tmm.2017.2758522
Abstract: In a semi-supervised setting, direct training of a deep discriminative model on partially labeled images often suffers from overfitting and poor performance, because only a small number of labeled images are available, and errors in… read more here.
Keywords: classification; image; semi supervised; model ... See more keywords
Cross-Task Cognitive Workload Recognition Based on EEG and Domain Adaptation
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Neural Systems and Rehabilitation Engineering"
DOI: 10.1109/tnsre.2022.3140456
Abstract: Cognitive workload recognition is pivotal to maintain the operator’s health and prevent accidents in the human-robot interaction condition. So far, the focus of workload research is mostly restricted to a single task, yet cross-task cognitive… read more here.
Keywords: cross task; task; workload; workload recognition ... See more keywords
CTG-Net: Cross-task guided network for breast ultrasound diagnosis
Sign Up to like & getrecommendations! Published in 2022 at "PLoS ONE"
DOI: 10.1371/journal.pone.0271106
Abstract: Deep learning techniques have achieved remarkable success in lesion segmentation and classification between benign and malignant tumors in breast ultrasound images. However, existing studies are predominantly focused on devising efficient neural network-based learning structures to… read more here.
Keywords: cross task; classification; task; segmentation ... See more keywords
Cross Task Modality Alignment Network for Sketch Face Recognition
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Neurorobotics"
DOI: 10.3389/fnbot.2022.823484
Abstract: The task of sketch face recognition refers to matching cross-modality facial images from sketch to photo, which is widely applied in the criminal investigation area. Existing works aim to bridge the cross-modality gap by inter-modality… read more here.
Keywords: modality alignment; cross task; modality; task ... See more keywords