Lightweight Image Super-Resolution With Expectation-Maximization Attention Mechanism

An Expectation Maximization Approach to Pronoun Resolution

We propose an unsupervised Expectation Maximization approach to pronoun resolution. The system learns from a fixed list of potential antecedents for each pronoun. We show that unsupervised learning is possible in this context, as the performance of our system is comparable to supervised methods. Our results indicate that a probabilistic gender/number model, determined automatically from unlabel...

Expectation-Maximization Algorithm and Image Segmentation

In computer vision, image segmentation problem is to partition a digital image into multiple parts. The goal is to change the representation of the image and make it more meaningful and easier to analyze [11]. In this assignment, we will show how an image segmentation algorithm works in a real application. In the Electronic Field Guide (EFG) project, researchers want to segment the leaf region ...

Image Super - Resolution Members :

Super-resolution is the task of obtaining a high-resolution image of a scene given low resolution image(s) of the scene. Applications of super-resolution include forensic, satellite, medical imaging, surveillance, displaying video on large screens, etc [1]. Obtaining high-resolution images directly via better hardware (better image sensors, larger chip size) is quite costly. Furthermore, most o...

Fast, Accurate, and, Lightweight Super-Resolution with Cascading Residual Network

In recent years, deep learning methods have been successfully applied to single-image super-resolution tasks. Despite their great performances, deep learning methods cannot be easily applied to real-world applications due to the requirement of heavy computation. In this paper, we address this issue by proposing an accurate and lightweight deep learning model for image super-resolution. In detai...

Expectation Maximization