Localizing Little Landmarks with Transfer Learning

• Main Author: Kumar, Sharad
• Format: Others
• Published: PDXScholar 2019
• Subjects: Computer Sciences
• Online Access: https://pdxscholar.library.pdx.edu/open_access_etds/4827; https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=5899&context=open_access_etds

Locating a small object in an image -- like a mouse on a computer desk or the door handle of a car -- is an important computer vision problem to solve because in many real life situations a small object may be the first thing that gets operated upon in the image scene. While a significant amount of artificial intelligence and machine learning research has focused on localizing prominent objects in an image, the area of small object detection has remained less explored. In my research I explore the possibility of using context information to localize small objects in an image. Using a Convolutional Neural Network (CNN), I create a regression model to detect a small object in an image where model training is supervised by coordinates of the small object in the image. Since small objects do not have strong visual characteristics in an image, it's difficult for a neural network to discern their pattern because their feature map exhibits low resolution rendering a much weaker signal for the network to recognize. Use of context for object detection and localization has been studied for a long time. This idea is explored by Singh et al. for small object localization by using a multi-step regression process where spatial context is used effectively to localize small objects in several datasets. I extend the idea in this research and demonstrate that the technique of localizing in steps using contextual information when used with transfer learning can significantly reduce model training time.