The visual tasks could range from instance recognition to human action recognition. In instance recognition, we would be answering specific visual identification questions such as: is this an Airbus A380? Another relevant question is object classification, where we aim to answer questions such as: does this image contain a bike or not? Another relevant task is that of object detection in images and videos: where is the bike in the image? In action recognition we aim at more general tasks such as: what is going on in the video? In the course we will undertake a study of different tasks.
In terms of techniques, there have been a wide range of machine learning techniques ranging from Adaboost and support vector machines to state of the art deep learning techniques. Many of the machine learning techniques have attained popularity based on their success in visual recognition tasks. Indeed, the success of adaboost for face detection has made boosting popular while deep learning techniques became widely popular once they succeeded in large scale object classification. In this course we aim to understand a few of the machine learning techniques involved as applied to visual recognition.
There have been certain assumptions in visual recognition such as the need for large number of manually supervised training samples. While this has been dominant there are a number of techniques that aim to relax this assumption by minimising the need for supervision. These include learning with latent variables, active learning techniques, unsupervised machine learning techniques. In the final part of the course we aim to study these advanced techniques.
A brief outline of the topics to be covered in the course are as follows: