Active Learning for Object Detection

In this thesis, we explore the interesting paradigm of Active Learning which is garnering a lot of attention in recent years within the Machine Learning community, and introduce approaches to how we can leverage Active Learning strategies to acquire significant performance gain on a niche task like Object Detection. From an application perspective, we broadly focus on two aspects -

1. Apply Active Learning strategies on the standard Object Detection task
2. Introduce targeted Active Learning for Object detection that improves performance on rare class/rare slices of data We will begin with a brief introduction to standard object detection mechanisms and various Active Learning strategies available and then we will dive into the experimental setup for implementing standard Active Learning and targeted Active Learning for Object Detection, discuss the empirical results obtained from different experimental settings and finally conclude with key observations from the experiments.