22 research outputs found

    Open-world Person Re-Identification by Multi-Label Assignment Inference.

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    (c) 2014. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms

    Identifying Rare and Subtle Behaviors: A Weakly Supervised Joint Topic Model

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    Transductive Multi-View Zero-Shot Learning

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    (c) 2012. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms

    Weakly Supervised Learning of Objects, Attributes and Their Associations

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    The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-10605-2_31]”

    Learning Multimodal Latent Attributes

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    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

    Multivariate Regression on the Grassmannian for Predicting Novel Domains

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    This work was supported by EPSRC (EP/L023385/1), and the European Union’s Horizon 2020 research and innovation program under grant agreement No 640891

    Finding Rare Classes: Active Learning with Generative and Discriminative Models

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    Bayesian Joint Modelling for Object Localisation in Weakly Labelled Images

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    Abstract—We address the problem of localisation of objects as bounding boxes in images and videos with weak labels. This weakly supervised object localisation problem has been tackled in the past using discriminative models where each object class is localised independently from other classes. In this paper, a novel framework based on Bayesian joint topic modelling is proposed, which differs significantly from the existing ones in that: (1) All foreground object classes are modelled jointly in a single generative model that encodes multiple object co-existence so that “explaining away ” inference can resolve ambiguity and lead to better learning and localisation. (2) Image backgrounds are shared across classes to better learn varying surroundings and “push out ” objects of interest. (3) Our model can be learned with a mixture of weakly labelled and unlabelled data, allowing the large volume of unlabelled images on the Internet to be exploited for learning. Moreover, 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. Extensive experiments on the PASCAL VOC, ImageNet and YouTube-Object videos datasets demonstrate the effectiveness of our Bayesian joint model for weakly supervised object localisation

    Probabilistic graphical models parameter learning with transferred prior and constraints

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    Learning accurate Bayesian networks (BNs) is a key challenge in real-world applications, especially when training data are hard to acquire. Two approaches have been used to address this challenge: 1) introducing expert judgements and 2) transferring knowledge from related domains. This is the first paper to present a generic framework that combines both approaches to improve BN parameter learning. This framework is built upon an extended multinomial parameter learning model, that itself is an auxiliary BN. It serves to integrate both knowledge transfer and expert constraints. Experimental results demonstrate improved accuracy of the new method on a variety of benchmark BNs, showing its potential to benefit many real-world problems
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