Notes from Memisevic – Learning to Relate Images

Notes from Memisevic – Learning to Relate Images

Annotations, Dissertation, General, Research
Citation Memisevic, Roland. “Learning to Relate Images.” IEEE Transactions on Pattern Analysis and Machine Intelligence 35, no. 8 (2013): 1829–1846. doi:10.1109/TPAMI.2013.53. Abstract A fundamental operation in many vision tasks, including motion understanding, stereopsis, visual odometry, or invariant recognition, is establishing correspondences between images or between images and data from other modalities. Recently, there has been increasing interest in learning to infer correspondences from data using relational, spatiotemporal, and bilinear variants of deep learning methods. These methods use multiplicative interactions between pixels or between features to represent correlation patterns across multiple images. In this paper, we review the recent work on relational feature learning, and we provide an analysis of the role that multiplicative interactions play in learning to encode relations. We also discuss how square-pooling and complex cell models can…
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Notes from Memisevic – Gradient-based learning of higher-order image features

Notes from Memisevic – Gradient-based learning of higher-order image features

Annotations, Dissertation, Research
Citation Memisevic, Roland. “Gradient-Based Learning of Higher-Order Image Features.” Proceedings of the IEEE International Conference on Computer Vision (November 2011): 1591–1598. doi:10.1109/ICCV.2011.6126419. Abstract Recent work on unsupervised feature learning has shown that learning on polynomial expansions of input patches, such as on pair-wise products of pixel intensities, can improve the performance of feature learners and extend their applicability to spatio-temporal problems, such as human action recognition or learning of image transformations.  Learning of such higher order features, however, has been much more difficult than standard dictionary learning, because of the high dimensionality and because standard learning criteria are not applicable.  here, we show how one can cast the problem of learning higher-order features as the problem of learning a parametric family of manifolds.  This allows us to apply a variant…
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