AIMC Topic: Pattern Recognition, Automated

Clear Filters Showing 301 to 310 of 1689 articles

Artificial intelligence for a personalized diagnosis and treatment of atrial fibrillation.

American journal of physiology. Heart and circulatory physiology
Although atrial fibrillation (AF) is the most common cardiac arrhythmia, its early identification, diagnosis, and treatment is still challenging. Due to its heterogeneous mechanisms and risk factors, targeting an individualized treatment of AF demand...

Controllable stroke-based sketch synthesis from a self-organized latent space.

Neural networks : the official journal of the International Neural Network Society
Learning to synthesize free-hand sketches controllably according to specified categories and sketching styles is a challenging task, due to the lack of training data with category labels and style labels. One choice to control the synthesis is by sel...

Extracting and Learning Fine-Grained Labels from Chest Radiographs.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Chest radiographs are the most common diagnostic exam in emergency rooms and intensive care units today. Recently, a number of researchers have begun working on large chest X-ray datasets to develop deep learning models for recognition of a handful o...

Adaptively Learning Facial Expression Representation via C-F Labels and Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Facial expression recognition is of significant importance in criminal investigation and digital entertainment. Under unconstrained conditions, existing expression datasets are highly class-imbalanced, and the similarity between expressions is high. ...

A conditional Triplet loss for few-shot learning and its application to image co-segmentation.

Neural networks : the official journal of the International Neural Network Society
Few-shot learning tries to solve the problems that suffer the limited number of samples. In this paper we present a novel conditional Triplet loss for solving few-shot problems using deep metric learning. While the conventional Triplet loss suffers t...

Shallow Graph Convolutional Network for Skeleton-Based Action Recognition.

Sensors (Basel, Switzerland)
Graph convolutional networks (GCNs) have brought considerable improvement to the skeleton-based action recognition task. Existing GCN-based methods usually use the fixed spatial graph size among all the layers. It severely affects the model's abiliti...

Visual behavior modelling for robotic theory of mind.

Scientific reports
Behavior modeling is an essential cognitive ability that underlies many aspects of human and animal social behavior (Watson in Psychol Rev 20:158, 1913), and an ability we would like to endow robots. Most studies of machine behavior modelling, howeve...

Few-Shot Human-Object Interaction Recognition With Semantic-Guided Attentive Prototypes Network.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Extreme instance imbalance among categories and combinatorial explosion make the recognition of Human-Object Interaction (HOI) a challenging task. Few studies have addressed both challenges directly. Motivated by the success of few-shot learning that...

Global and Local Knowledge-Aware Attention Network for Action Recognition.

IEEE transactions on neural networks and learning systems
Convolutional neural networks (CNNs) have shown an effective way to learn spatiotemporal representation for action recognition in videos. However, most traditional action recognition algorithms do not employ the attention mechanism to focus on essent...

Gradients Cannot Be Tamed: Behind the Impossible Paradox of Blocking Targeted Adversarial Attacks.

IEEE transactions on neural networks and learning systems
Despite their accuracy, neural network-based classifiers are still prone to manipulation through adversarial perturbations. These perturbations are designed to be misclassified by the neural network while being perceptually identical to some valid in...