Weakly Supervised Learning of Objects and Attributes.
Zhiyuan Shi · Queen Mary Research Online (Queen Mary University of London) · 2016
This thesis presents weakly supervised learning approaches to directly exploit image-level tags (e.g.objects, attributes) for comprehensive image understanding, including tasks such as object localisation, image description, image retrieval, semantic segmentation, person re-identification and person search, etc.Unlike the conventional approaches which tackle weakly supervised problem by learning a discriminative model, a generative Bayesian framework is proposed which provides better mechanisms to resolve the ambiguity problem.The proposed model significantly differentiates from the existing approaches in that: (1) All foreground object classes are modelled jointly in a single generative model that encodes multiple objects co-existence so that "explaining away" inference can resolve ambiguity and lead to better learning.(2) Image backgrounds are shared across classes to better learn varying surroundings and "push out" objects of interest.(3) the Bayesian formulation enables the exploitation of various types of prior knowledge to compensate for the limited supervision offered by weakly labelled data, as well as Bayesian domain adaptation for transfer learning.Detecting objects is the first and critical component in image understanding paradigm.Unlike conventional fully supervised object detection approaches, the proposed model aims to train an object detector from weakly labelled data.A novel framework based on Bayesian latent topic model is proposed to address the problem of localisation of objects as bounding boxes in images and videos with image level object labels.The inferred object location can be then used as the annotation to train a classic object detector with conventional approaches.However, objects cannot tell the whole story in an image.Beyond detecting objects, a general visual model should be able to describe objects I consider myself extremely lucky to have had the opportunity to work with my supervisor, Dr. Tao Xiang.I would like to thank my supervisor for his perpetual patience, encouragement and guidance.I will never forget his kind help in every bit of the PhD process.Besides, I am deeply