504 research outputs found

    Deep Multi-View Learning for Visual Understanding

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    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

    Multi-Task Zero-Shot Action Recognition with Prioritised Data Augmentation

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    Zero-Shot Learning (ZSL) promises to scale visual recognition by bypassing the conventional model training requirement of annotated examples for every category. This is achieved by establishing a mapping connecting low-level features and a semantic description of the label space, referred as visual-semantic mapping, on auxiliary data. Reusing the learned mapping to project target videos into an embedding space thus allows novel-classes to be recognised by nearest neighbour inference. However, existing ZSL methods suffer from auxiliary-target domain shift intrinsically induced by assuming the same mapping for the disjoint auxiliary and target classes. This compromises the generalisation accuracy of ZSL recognition on the target data. In this work, we improve the ability of ZSL to generalise across this domain shift in both model- and data-centric ways by formulating a visual-semantic mapping with better generalisation properties and a dynamic data re-weighting method to prioritise auxiliary data that are relevant to the target classes. Specifically: (1) We introduce a multi-task visual-semantic mapping to improve generalisation by constraining the semantic mapping parameters to lie on a low-dimensional manifold, (2) We explore prioritised data augmentation by expanding the pool of auxiliary data with additional instances weighted by relevance to the target domain. The proposed new model is applied to the challenging zero-shot action recognition problem to demonstrate its advantages over existing ZSL models.Comment: Published in ECCV 201

    The role of time in video understanding

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    Knowledge sharing: From atomic to parametrised context and shallow to deep models

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    PhDKey to achieving more effective machine intelligence is the capability to generalise knowledge across different contexts. In this thesis, we develop a new and very general perspective on knowledge sharing that unifi es and generalises many existing methodologies, while being practically effective, simple to implement, and opening up new problem settings. Knowledge sharing across tasks and domains has conventionally been studied disparately. We fi rst introduce the concept of a semantic descriptor and a flexible neural network approach to knowledge sharing that together unify multi-task/multi-domain learning, and encompass various classic and recent multi-domain learning (MDL) and multi-task learning (MTL) algorithms as special cases. We next generalise this framework from single-output to multi-output problems and from shallow to deep models. To achieve this, we establish the equivalence between classic tensor decomposition methods, and specifi c neural network architectures. This makes it possible to implement our framework within modern deep learning stacks. We present both explicit low-rank, and trace norm regularisation solutions. From a practical perspective, we also explore a new problem setting of zero-shot domain adaptation (ZSDA) where a model can be calibrated solely based on some abstract information of a new domain, e.g., some metadata like the capture device of photos, without collecting or labelling the data

    Deeper, Broader and Artier Domain Generalization

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    The problem of domain generalization is to learn from multiple training domains, and extract a domain-agnostic model that can then be applied to an unseen domain. Domain generalization (DG) has a clear motivation in contexts where there are target domains with distinct characteristics, yet sparse data for training. For example recognition in sketch images, which are distinctly more abstract and rarer than photos. Nevertheless, DG methods have primarily been evaluated on photo-only benchmarks focusing on alleviating the dataset bias where both problems of domain distinctiveness and data sparsity can be minimal. We argue that these benchmarks are overly straightforward, and show that simple deep learning baselines perform surprisingly well on them. In this paper, we make two main contributions: Firstly, we build upon the favorable domain shift-robust properties of deep learning methods, and develop a low-rank parameterized CNN model for end-to-end DG learning. Secondly, we develop a DG benchmark dataset covering photo, sketch, cartoon and painting domains. This is both more practically relevant, and harder (bigger domain shift) than existing benchmarks. The results show that our method outperforms existing DG alternatives, and our dataset provides a more significant DG challenge to drive future research.Comment: 9 pages, 4 figures, ICCV 201
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