Learning by Asking Questions

We introduce an interactive learning framework for the development and testing of intelligent visual systems, called learning-by-asking (LBA). We explore LBA in context of the Visual Question Answering (VQA) task. LBA differs from standard VQA training in that most questions are not observed during training time, and the learner must ask questions it wants answers to. Thus, LBA more closely mimics natural learning and has the potential to be more data-efficient than the traditional VQA setting. We present a model that performs LBA on the CLEVR dataset, and show that it automatically discovers an easy-to-hard curriculum when learning interactively from an oracle. Our LBA generated data consistently matches or outperforms the CLEVR train data and is more sample efficient. We also show that our model asks questions that generalize to state-of-the-art VQA models and to novel test time distributions

Resilience, moorings and international student mobilities - exploring biographical narratives of social science students in the UK

Whilst research into the changing landscape of the UK Higher Education (HE) has produced a burgeoning literature on ‘internationalisation’ and ‘transnational student mobility’ over the past few years, still fairly little is known about international students’ experiences on their way to and through the UK higher and further education. Frequently approaching inter- and transnational education as ‘neutral’ by-products of neoliberal globalisation, elitism and power flows, much HE policy and scholarly debate tend to operate with simplistic classifications of ‘international students’ and therefore fail to account for the multifaceted nature of students’ aspirations, mobilities and life experiences. Drawing on the notion of ‘resilience’ and insights from the ‘new mobilities paradigm’, this paper envisages alternative student mobilities which run parallel or counter to the dominant flows of power, financial and human capital commonly associated with an emerging global knowledge economy. Engaging with ‘resilient’ biographies of social science students studying at three UK HE institutions, the paper challenges narrow student classification regimes and calls for a critical re-evaluation of the relationship between international student mobility and other contemporary forms of migration, displacement and diaspora

G\mathcal{G}-softmax: Improving Intra-class Compactness and Inter-class Separability of Features

Intra-class compactness and inter-class separability are crucial indicators to measure the effectiveness of a model to produce discriminative features, where intra-class compactness indicates how close the features with the same label are to each other and inter-class separability indicates how far away the features with different labels are. In this work, we investigate intra-class compactness and inter-class separability of features learned by convolutional networks and propose a Gaussian-based softmax ($\mathcal{G}$-softmax) function that can effectively improve intra-class compactness and inter-class separability. The proposed function is simple to implement and can easily replace the softmax function. We evaluate the proposed $\mathcal{G}$-softmax function on classification datasets (i.e., CIFAR-10, CIFAR-100, and Tiny ImageNet) and on multi-label classification datasets (i.e., MS COCO and NUS-WIDE). The experimental results show that the proposed $\mathcal{G}$-softmax function improves the state-of-the-art models across all evaluated datasets. In addition, analysis of the intra-class compactness and inter-class separability demonstrates the advantages of the proposed function over the softmax function, which is consistent with the performance improvement. More importantly, we observe that high intra-class compactness and inter-class separability are linearly correlated to average precision on MS COCO and NUS-WIDE. This implies that improvement of intra-class compactness and inter-class separability would lead to improvement of average precision.Comment: 15 pages, published in TNNL