AIMC Topic: Attention

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End-to-End Sentence-Level Multi-View Lipreading Architecture with Spatial Attention Module Integrated Multiple CNNs and Cascaded Local Self-Attention-CTC.

Sensors (Basel, Switzerland)
Concomitant with the recent advances in deep learning, automatic speech recognition and visual speech recognition (VSR) have received considerable attention. However, although VSR systems must identify speech from both frontal and profile faces in re...

Salient Object Detection in the Deep Learning Era: An In-Depth Survey.

IEEE transactions on pattern analysis and machine intelligence
As an essential problem in computer vision, salient object detection (SOD) has attracted an increasing amount of research attention over the years. Recent advances in SOD are predominantly led by deep learning-based solutions (named deep SOD). To ena...

Spatiotemporal Co-Attention Recurrent Neural Networks for Human-Skeleton Motion Prediction.

IEEE transactions on pattern analysis and machine intelligence
Human motion prediction aims to generate future motions based on the observed human motions. Witnessing the success of Recurrent Neural Networks (RNN) in modeling sequential data, recent works utilize RNNs to model human-skeleton motions on the obser...

Reporting details of neuroimaging studies on individual traits prediction: A literature survey.

NeuroImage
Using machine-learning tools to predict individual phenotypes from neuroimaging data is one of the most promising and hence dynamic fields in systems neuroscience. Here, we perform a literature survey of the rapidly work on phenotype prediction in he...

MHA-CoroCapsule: Multi-Head Attention Routing-Based Capsule Network for COVID-19 Chest X-Ray Image Classification.

IEEE transactions on medical imaging
The outbreak of COVID-19 threatens the lives and property safety of countless people and brings a tremendous pressure to health care systems worldwide. The principal challenge in the fight against this disease is the lack of efficient detection metho...

Deep parameter-free attention hashing for image retrieval.

Scientific reports
Deep hashing method is widely applied in the field of image retrieval because of its advantages of low storage consumption and fast retrieval speed. There is a defect of insufficiency feature extraction when existing deep hashing method uses the conv...

MR-FPN: Multi-Level Residual Feature Pyramid Text Detection Network Based on Self-Attention Environment.

Sensors (Basel, Switzerland)
With humanity entering the age of intelligence, text detection technology has been gradually applied in the industry. However, text detection in a complex background is still a challenging problem for researchers to overcome. Most of the current algo...

Attention-modulated multi-branch convolutional neural networks for neonatal brain tissue segmentation.

Computers in biology and medicine
Accurate measurement of brain structures is essential for the evaluation of neonatal brain growth and development. The conventional methods use manual segmentation to measure brain tissues, which is very time-consuming and inefficient. Recent deep le...

Clinical target segmentation using a novel deep neural network: double attention Res-U-Net.

Scientific reports
We introduced Double Attention Res-U-Net architecture to address medical image segmentation problem in different medical imaging system. Accurate medical image segmentation suffers from some challenges including, difficulty of different interest obje...

Sparse factorization of square matrices with application to neural attention modeling.

Neural networks : the official journal of the International Neural Network Society
Square matrices appear in many machine learning problems and models. Optimization over a large square matrix is expensive in memory and in time. Therefore an economic approximation is needed. Conventional approximation approaches factorize the square...