Morphological Landmark Detection on Lobsters Using Attention Networks
Parmeet Singh, Mae Seto · 2019
The aim of this paper is map landmarks on lobsters using convolution neural networks. An attention mechanism for improving the performance of the VGG network to that effect is used. Regular CNN architectures do not consciously extract detailed features from images. The attention mechanism learns to focus on regions around the lobster landmarks amongst the whole image. Existing approaches use a cascade of regression models for landmark prediction. Proposed, is a single iteration model augmenting an attention mechanism to produce similar results. The adaptability of the attention mechanism over any network, such as VGG16 or Resnet, avoids the need to learn the network from scratch.