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Deep convolutional neural networks for image classification: A comprehensive review
Convolutional neural networks (CNNs) have been applied to visual tasks since the late
1980s. However, despite a few scattered applications, they were dormant until the mid …
1980s. However, despite a few scattered applications, they were dormant until the mid …
Towards automatic threat detection: A survey of advances of deep learning within X-ray security imaging
X-ray security screening is widely used to maintain aviation/transport security, and its
significance poses a particular interest in automated screening systems. This paper aims to …
significance poses a particular interest in automated screening systems. This paper aims to …
Morphological prototy** for unsupervised slide representation learning in computational pathology
Abstract Representation learning of pathology whole-slide images (WSIs) has been has
primarily relied on weak supervision with Multiple Instance Learning (MIL). However the …
primarily relied on weak supervision with Multiple Instance Learning (MIL). However the …
Texture feature extraction methods: A survey
A Humeau-Heurtier - IEEE access, 2019 - ieeexplore.ieee.org
Texture analysis is used in a very broad range of fields and applications, from texture
classification (eg, for remote sensing) to segmentation (eg, in biomedical imaging), passing …
classification (eg, for remote sensing) to segmentation (eg, in biomedical imaging), passing …
Coco-stuff: Thing and stuff classes in context
Semantic classes can be either things (objects with a well-defined shape, eg car, person) or
stuff (amorphous background regions, eg grass, sky). While lots of classification and …
stuff (amorphous background regions, eg grass, sky). While lots of classification and …
Deep unsupervised learning using nonequilibrium thermodynamics
A central problem in machine learning involves modeling complex data-sets using highly
flexible families of probability distributions in which learning, sampling, inference, and …
flexible families of probability distributions in which learning, sampling, inference, and …
A background-agnostic framework with adversarial training for abnormal event detection in video
Abnormal event detection in video is a complex computer vision problem that has attracted
significant attention in recent years. The complexity of the task arises from the commonly …
significant attention in recent years. The complexity of the task arises from the commonly …
Salient objects in clutter: Bringing salient object detection to the foreground
We provide a comprehensive evaluation of salient object detection (SOD) models. Our
analysis identifies a serious design bias of existing SOD datasets which assumes that each …
analysis identifies a serious design bias of existing SOD datasets which assumes that each …
SSMTL++: Revisiting self-supervised multi-task learning for video anomaly detection
A self-supervised multi-task learning (SSMTL) framework for video anomaly detection was
recently introduced in literature. Due to its highly accurate results, the method attracted the …
recently introduced in literature. Due to its highly accurate results, the method attracted the …