AI Medical Compendium Journal:
IEEE transactions on pattern analysis and machine intelligence

Showing 121 to 130 of 300 articles

Robust Face Alignment via Deep Progressive Reinitialization and Adaptive Error-Driven Learning.

IEEE transactions on pattern analysis and machine intelligence
Regression-based face alignment involves learning a series of mapping functions to predict the true landmarks from an initial estimation of the alignment. Most existing approaches focus on learning efficacious mapping functions from some feature repr...

Active Fine-Tuning From gMAD Examples Improves Blind Image Quality Assessment.

IEEE transactions on pattern analysis and machine intelligence
The research in image quality assessment (IQA) has a long history, and significant progress has been made by leveraging recent advances in deep neural networks (DNNs). Despite high correlation numbers on existing IQA datasets, DNN-based models may be...

Sample-Efficient Neural Architecture Search by Learning Actions for Monte Carlo Tree Search.

IEEE transactions on pattern analysis and machine intelligence
Neural Architecture Search (NAS) has emerged as a promising technique for automatic neural network design. However, existing MCTS based NAS approaches often utilize manually designed action space, which is not directly related to the performance metr...

On the Synergies Between Machine Learning and Binocular Stereo for Depth Estimation From Images: A Survey.

IEEE transactions on pattern analysis and machine intelligence
Stereo matching is one of the longest-standing problems in computer vision with close to 40 years of studies and research. Throughout the years the paradigm has shifted from local, pixel-level decision to various forms of discrete and continuous opti...

TRACK: A New Method From a Re-Examination of Deep Architectures for Head Motion Prediction in 360 Videos.

IEEE transactions on pattern analysis and machine intelligence
We consider predicting the user's head motion in 360 videos, with 2 modalities only: the past user's positions and the video content (not knowing other users' traces). We make two main contributions. First, we re-examine existing deep-learning appro...

Visual Camera Re-Localization From RGB and RGB-D Images Using DSAC.

IEEE transactions on pattern analysis and machine intelligence
We describe a learning-based system that estimates the camera position and orientation from a single input image relative to a known environment. The system is flexible w.r.t. the amount of information available at test and at training time, catering...

3D Human Pose, Shape and Texture From Low-Resolution Images and Videos.

IEEE transactions on pattern analysis and machine intelligence
3D human pose and shape estimation from monocular images has been an active research area in computer vision. Existing deep learning methods for this task rely on high-resolution input, which however, is not always available in many scenarios such as...

Deep Cognitive Gate: Resembling Human Cognition for Saliency Detection.

IEEE transactions on pattern analysis and machine intelligence
Saliency detection by human refers to the ability to identify pertinent information using our perceptive and cognitive capabilities. While human perception is attracted by visual stimuli, our cognitive capability is derived from the inspiration of co...

Hierarchical and Self-Attended Sequence Autoencoder.

IEEE transactions on pattern analysis and machine intelligence
It is important and challenging to infer stochastic latent semantics for natural language applications. The difficulty in stochastic sequential learning is caused by the posterior collapse in variational inference. The input sequence is disregarded i...

Deep Learning Adapted to Differential Neural Networks Used as Pattern Classification of Electrophysiological Signals.

IEEE transactions on pattern analysis and machine intelligence
This manuscript presents the design of a deep differential neural network (DDNN) for pattern classification. First, we proposed a DDNN topology with three layers, whose learning laws are derived from a Lyapunov analysis, justifying local asymptotic c...