Wang et al (2021) Real-ESRGAN

This is to extend the SR-GAN and ESR-GAN to do blind super-resolution. The problem statement is to reconstruct the high-resolution image from low resolution (a.k.a. super-resolution), but without knowing how the low resolution is derived from the original high-resolution image, i.e., blind super-resolution. [more]

Explaining Attention Mechanism

Attention mechanism was first mentioned in Bahdanau et al (2015) paper titled “Neural Machine Translation by Jointly Learning to Align and Translate”, and Luong et al (2015) improved it with the paper “Effective Approaches to Attention-based Neural Machine Translation”. The key is to find the attention score $a_{ij}$ between two... [more]

Carion et al (2020) End-to-End Object Detection with Transformers

Object detection is to predict the bounding boxes and category labels for each object of interest. This paper proposed DETR (Detection Transformer) to predict all objects at once, trained end-to-end with a set loss function to perform bipartite matching between the predicted and groundtruth. It is found to perform better... [more]