Semantic Autoencoder for Zero-Shot Learning

Existing zero-shot learning (ZSL) models typically learn a projection function from a feature space to a semantic embedding space (e.g.~attribute space). However, such a projection function is only concerned with predicting the training seen class semantic representation (e.g.~attribute prediction) or classification. When applied to test data, which in the context of ZSL contains different (unseen) classes without training data, a ZSL model typically suffers from the project domain shift problem. In this work, we present a novel solution to ZSL based on learning a Semantic AutoEncoder (SAE). Taking the encoder-decoder paradigm, an encoder aims to project a visual feature vector into the semantic space as in the existing ZSL models. However, the decoder exerts an additional constraint, that is, the projection/code must be able to reconstruct the original visual feature. We show that with this additional reconstruction constraint, the learned projection function from the seen classes is able to generalise better to the new unseen classes. Importantly, the encoder and decoder are linear and symmetric which enable us to develop an extremely efficient learning algorithm. Extensive experiments on six benchmark datasets demonstrate that the proposed SAE outperforms significantly the existing ZSL models with the additional benefit of lower computational cost. Furthermore, when the SAE is applied to supervised clustering problem, it also beats the state-of-the-art.Comment: accepted to CVPR201

Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective

This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the problems (e.g. eight of the seventeen problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look up for a possible solution accordingly

Enhancing deep transfer learning for image classification

Though deep learning models require a large amount of labelled training data for yielding high performance, they are applied to accomplish many computer vision tasks such as image classification. Current models also do not perform well across different domain settings such as illumination, camera angle and real-to-synthetic. Thus the models are more likely to misclassify unknown classes as known classes. These issues challenge the supervised learning paradigm of the models and encourage the study of transfer learning approaches. Transfer learning allows us to utilise the knowledge acquired from related domains to improve performance on a target domain. Existing transfer learning approaches lack proper high-level source domain feature analyses and are prone to negative transfers for not exploring proper discriminative information across domains. Current approaches also lack at discovering necessary visual-semantic linkage and has a bias towards the source domain. In this thesis, to address these issues and improve image classification performance, we make several contributions to three different deep transfer learning scenarios, i.e., the target domain has i) labelled data; no labelled data; and no visual data. Firstly, for improving inductive transfer learning for the first scenario, we analyse the importance of high-level deep features and propose utilising them in sequential transfer learning approaches and investigating the suitable conditions for optimal performance. Secondly, to improve image classification across different domains in an open set setting by reducing negative transfers (second scenario), we propose two novel architectures. The first model has an adaptive weighting module based on underlying domain distinctive information, and the second model has an information-theoretic weighting module to reduce negative transfers. Thirdly, to learn visual classifiers when no visual data is available (third scenario) and reduce source domain bias, we propose two novel models. One model has a new two-step dense attention mechanism to discover semantic attribute-guided local visual features and mutual learning loss. The other model utilises bidirectional mapping and adversarial supervision to learn the joint distribution of source-target domains simultaneously. We propose a new pointwise mutual information dependant loss in the first model and a distance-based loss in the second one for handling source domain bias. We perform extensive evaluations on benchmark datasets and demonstrate the proposed models outperform contemporary works.Doctor of Philosoph

Multimodal One-Shot Learning of Speech and Images

Imagine a robot is shown new concepts visually together with spoken tags, e.g. "milk", "eggs", "butter". After seeing one paired audio-visual example per class, it is shown a new set of unseen instances of these objects, and asked to pick the "milk". Without receiving any hard labels, could it learn to match the new continuous speech input to the correct visual instance? Although unimodal one-shot learning has been studied, where one labelled example in a single modality is given per class, this example motivates multimodal one-shot learning. Our main contribution is to formally define this task, and to propose several baseline and advanced models. We use a dataset of paired spoken and visual digits to specifically investigate recent advances in Siamese convolutional neural networks. Our best Siamese model achieves twice the accuracy of a nearest neighbour model using pixel-distance over images and dynamic time warping over speech in 11-way cross-modal matching.Comment: 5 pages, 1 figure, 3 tables; accepted to ICASSP 201