What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …

Despite enormous progress in machine learning, artificial neural networks still lag behind
brains in their ability to generalize to new situations. Given identical training data …

Abstract Visual Question Answering (VQA) requires a fine-grained and simultaneous
understanding of both the visual content of images and the textual content of questions …

Convolutional neural networks have been widely deployed in various application scenarios.
In order to extend the applications' boundaries to some accuracy-crucial domains …

Abstract In standard Convolutional Neural Networks (CNNs), the receptive fields of artificial
neurons in each layer are designed to share the same size. It is well-known in the …

In recent years, machine learning (ML) methods have become increasingly popular in
computational chemistry. After being trained on appropriate ab initio reference data, these …

Saliency detection is one of the basic challenges in computer vision. Recently, CNNs are the
most widely used and powerful techniques for saliency detection, in which feature maps …

This paper introduces an extremely efficient CNN architecture named DFANet for semantic
segmentation under resource constraints. Our proposed network starts from a single …

We propose a novel attention gate (AG) model for medical image analysis that automatically
learns to focus on target structures of varying shapes and sizes. Models trained with AGs …

Reinforcement learning in multi-agent scenarios is important for real-world applications but
presents challenges beyond those seen in single-agent settings. We present an actor-critic …