Browsing by Author "Josias, Shane"

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    Multitask learning and data distribution search in visual relationship recognition
    (Stellenbosch : Stellenbosch University., 2020-03) Josias, Shane; Brink, Willie; Stellenbosch University. Faculty of Science. Department of Mathematical Sciences (Applied Mathematics).
    ENGLISH ABSTRACT: An image can be described by the objects within it, as well as the interactions between those objects. A pair of object labels together with an interaction label can be assembled into what is known as a visual relationship, represented as a triplet of the form (subject, predicate, object). Recognising visual relationships in a given image is a challenging task, owing to the combinatorially large number of possible relationship triplets which lead to a so-called extreme classification problem, as well as a very long tail found typically in the distribution of those possible triplets. We investigate the efficacy of four strategies that could potentially address these issues. Firstly, instead of predicting the full triplet we opt to predict each element separately. Secondly, we investigate the use of shared network parameters to perform these separate predictions in a basic multitask setting. Thirdly, we extend the multitask setting by including an online ranking loss that acts on a trio of samples (an anchor, a positive sample, and a negative sample). Semi-hard negative mining is used to select negative samples. Finally, we consider a class-selective batch construction strategy to expose the network to more of the many rare classes during mini-batch training. We view semihard negative mining and class-selective batch construction as training data distribution search, in the sense that they both attempt to carefully select training samples in order to improve model performance. In addition to the aforementioned strategies, we also introduce a means of evaluating model behaviour in visual relationship recognition. This evaluation motivates the use of semantics. Our experiments demonstrate that batch construction can improve performance on the long tail, possibly at the expense of accuracy on the small number of dominating classes. We also find that a basic multitask model neither improves nor impedes performance in any significant way, but that its smaller size may be beneficial. Moreover, multitask models trained with a ranking loss yield a decrease in performance, possibly due to limited batch sizes.