Learning Multimodal Latent Attributes

Abstract—The rapid development of social media sharing has created a huge demand for automatic media classification and annotation techniques. Attribute learning has emerged as a promising paradigm for bridging the semantic gap and addressing data sparsity via transferring attribute knowledge in object recognition and relatively simple action classification. In this paper, we address the task of attribute learning for understanding multimedia data with sparse and incomplete labels. In particular we focus on videos of social group activities, which are particularly challenging and topical examples of this task because of their multi-modal content and complex and unstructured nature relative to the density of annotations. To solve this problem, we (1) introduce a concept of semi-latent attribute space, expressing user-defined and latent attributes in a unified framework, and (2) propose a novel scalable probabilistic topic model for learning multi-modal semi-latent attributes, which dramatically reduces requirements for an exhaustive accurate attribute ontology and expensive annotation effort. We show that our framework is able to exploit latent attributes to outperform contemporary approaches for addressing a variety of realistic multimedia sparse data learning tasks including: multi-task learning, learning with label noise, N-shot transfer learning and importantly zero-shot learning

Multi-task CNN Model for Attribute Prediction

This paper proposes a joint multi-task learning algorithm to better predict attributes in images using deep convolutional neural networks (CNN). We consider learning binary semantic attributes through a multi-task CNN model, where each CNN will predict one binary attribute. The multi-task learning allows CNN models to simultaneously share visual knowledge among different attribute categories. Each CNN will generate attribute-specific feature representations, and then we apply multi-task learning on the features to predict their attributes. In our multi-task framework, we propose a method to decompose the overall model's parameters into a latent task matrix and combination matrix. Furthermore, under-sampled classifiers can leverage shared statistics from other classifiers to improve their performance. Natural grouping of attributes is applied such that attributes in the same group are encouraged to share more knowledge. Meanwhile, attributes in different groups will generally compete with each other, and consequently share less knowledge. We show the effectiveness of our method on two popular attribute datasets.Comment: 11 pages, 3 figures, ieee transaction pape

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

Deep Multi-View Learning for Visual Understanding

PhD ThesisMulti-view data is the result of an entity being perceived or represented from multiple perspectives. Plenty of applications in visual understanding contain multi-view data. For example, the face images for training a recognition system are usually captured by different devices from multiple angles. This thesis focuses on the cross-view visual recognition problems, e.g., identifying the face images of the same person across different cameras. Several representative multi-view settings, from the supervised multi-view learning to the more challenging unsupervised domain adaptive (UDA) multi-view learning, are investigated. Novel multi-view learning algorithms are proposed correspondingly. To be more specific, the proposed methods are based on the advanced deep neural network (DNN) architectures for better handling visual data. However, directly combining the multi-view learning objectives with DNN can result in different issues, e.g., on scalability, and limit the application scenarios and model performance. Corresponding novelties in DNN methods are thus required to solve them. This thesis is organised into three parts. Each chapter focuses on a multi-view learning setting with novel solutions and is detailed as follows: Chapter 3 A supervised multi-view learning setting with two different views are studied. To recognise the data samples across views, one strategy is aligning them in a common feature space via correlation maximisation. It is also known as canonical correlation analysis (CCA). Deep CCA has been proposed for better performance with the non-linear projection via deep neural networks. Existing deep CCA models typically decorrelate the deep feature dimensions of each view before their Euclidean distances are minimised in the common space. This feature decorrelation is achieved by enforcing an exact decorrelation constraint which is computationally expensive due to the matrix inversion or SVD operations. Therefore, existing deep CCA models are inefficient and have scalability issues. Furthermore, the exact decorrelation is incompatible with the gradient based deep model training and results in sub-optimal solution. To overcome these aforementioned issues, a novel deep CCA model Soft CCA is introduced in this thesis. Specifically, the exact decorrelation is replaced by soft decorrelation via a mini-batch based Stochastic Decorrelation Loss (SDL). It can be jointly optimised with the other training objectives. In addition, our SDL loss can be applied to other deep models beyond multi-view learning. Chapter 4 The supervised multi-view learning setting, whereby more than two views exist, are studied in this chapter. Recently developed deep multi-view learning algorithms either learn a latent visual representation based on a single semantic level and/or require laborious human annotation of these factors as attributes. A novel deep neural network architecture, called Multi- Level Factorisation Net (MLFN), is proposed to automatically factorise the visual appearance into latent discriminative factors at multiple semantic levels without manual annotation. The main purpose is forcing different views share the same latent factors so that they are can be aligned at all layers. Specifically, MLFN is composed of multiple stacked blocks. Each block contains multiple factor modules to model latent factors at a specific level, and factor selection modules that dynamically select the factor modules to interpret the content of each input image. The outputs of the factor selection modules also provide a compact latent factor descriptor that is complementary to the conventional deeply learned feature, and they can be fused efficiently. The effectiveness of the proposed MLFN is demonstrated by not only the large-scale cross-view recognition problems but also the general object categorisation tasks. Chapter 5 The last problem is a special unsupervised domain adaptation setting called unsupervised domain adaptive (UDA) multi-view learning. It contains a fully annotated dataset as the source domain and another unsupervised dataset with relevant tasks as the target domain. The main purpose is to improve the performance of the unlabelled dataset with the annotated data from the other dataset. More importantly, this setting further requires both the source and target domains are multi-view datasets with relevant tasks. Therefore, the assumption of the aligned label space across domains is inappropriate in the UDA multi-view learning. For example, the person re-identification (Re-ID) datasets built on different surveillance scenarios are with images of different people captured and should be given disjoint person identity labels. Existing methods for UDA multi-view learning problems are aligning different domains either in the raw image space or a feature embedding space for domain alignment. In this thesis, a different framework, multi-task learning, is adopted with the domain specific objectives for a common space learning. Specifically, such common space is proposed to enable the knowledge transfer. The conventional supervised losses can be used for the labelled source data while the unsupervised objectives for the target domain play the key roles in domain adaptation. Two novel unsupervised objectives are introduced for UDA multi-view learning and result in two models as below. The first model, termed common factorised space model (CFSM), is built on the assumptions that the semantic latent attributes are shared between the source and target domains since they are relevant multi-view learning tasks. Different from the existing methods that based on domain alignment, CFSM emphasizes on transferring the information across domains via discovering discriminative latent factors in the proposed common space. However, the multi-view data from target domain is without labels. Therefore, an unsupervised factorisation loss is derived and applied on the common space for latent factors discovery across domains. The second model still learns a shared embedding space with multi-view data from both domains but with a different assumption. It attempts to discover the latent correspondence of multi-view data in the unsupervised target data. The target data’s contribution comes from a clustering process. Each cluster thus reveals the underlying cross-view correspondences across multiple views in target domain. To this end, a novel Stochastic Inference for Deep Clustering (SIDC) method is proposed. It reduces self-reinforcing errors that lead to premature convergence to a sub-optimal solution by changing the conventional deterministic cluster assignment to a stochastic one