Developing object perception in the low data regime - Kaul, P (2024)
Objects are central to human perception and understanding of the world. There is an abundance of images available on the internet covering the vast number of objects in the world, however, labelling these images exhaustively to cover all objects is infeasible—limiting the utility of systems requiring strong supervision through large labelled datasets. To address this issue, this thesis develops methods to enable novel objects to be learnt with limited use of manually labelled data.