One-Shot Learning using Mixture of Variational Autoencoders: a Generalization Learning approach

Deep learning, even if it is very successful nowadays, traditionally needs very large amounts of labeled data to perform excellent on the classification task. In an attempt to solve this problem, the one-shot learning paradigm, which makes use of just one labeled sample per class and prior knowledge, becomes increasingly important. In this paper, we propose a new one-shot learning method, dubbed MoVAE (Mixture of Variational AutoEncoders), to perform classification. Complementary to prior studies, MoVAE represents a shift of paradigm in comparison with the usual one-shot learning methods, as it does not use any prior knowledge. Instead, it starts from zero knowledge and one labeled sample per class. Afterward, by using unlabeled data and the generalization learning concept (in a way, more as humans do), it is capable to gradually improve by itself its performance. Even more, if there are no unlabeled data available MoVAE can still perform well in one-shot learning classification. We demonstrate empirically the efficiency of our proposed approach on three datasets, i.e. the handwritten digits (MNIST), fashion products (Fashion-MNIST), and handwritten characters (Omniglot), showing that MoVAE outperforms state-of-the-art one-shot learning algorithms

Multi-View Dynamic Shape Refinement Using Local Temporal Integration

International audienceWe consider 4D shape reconstructions in multi-view environments and investigate how to exploit temporal redundancy for precision refinement. In addition to being beneficial to many dynamic multi-view scenarios this also enables larger scenes where such increased precision can compensate for the reduced spatial resolution per image frame. With precision and scalability in mind, we propose a symmetric (non-causal) local time-window geometric integration scheme over temporal sequences, where shape reconstructions are refined framewise by warping local and reliable geometric regions of neighboring frames to them. This is in contrast to recent comparable approaches targeting a different context with more compact scenes and real-time applications. These usually use a single dense volumetric update space or geometric template, which they causally track and update globally frame by frame, with limitations in scalability for larger scenes and in topology and precision with a template based strategy. Our templateless and local approach is a first step towards temporal shape super-resolution. We show that it improves reconstruction accuracy by considering multiple frames. To this purpose, and in addition to real data examples, we introduce a multi-camera synthetic dataset that provides ground-truth data for mid-scale dynamic scenes

Evaluation of sets of oriented and non-oriented receptive fields as local descriptors

Local descriptors are increasingly used for the task of object recognition because of their perceived robustness with respect to occlusions and to global geometrical deformations. We propose a performance criterion for a local descriptor based on the tradeoff between selectivity and invariance. In this paper, we evaluate several local descriptors with respect to selectivity and invariance. The descriptors that we evaluated are Gaussian derivatives up to the third order, gray image patches, and Laplacian-based descriptors with either three scales or one scale filters. We compare selectivity and invariance to several affine changes such as rotation, scale, brightness, and viewpoint. Comparisons have been made keeping the dimensionality of the descriptors roughly constant. The overall results indicate a good performance by the descriptor based on a set of oriented Gaussian filters. It is interesting that oriented receptive fields similar to the Gaussian derivatives as well as receptive fields similar to the Laplacian are found in primate visual cortex

Super Résolution Temporelle de Formes Multi-Vues

International audienceNous considérons le problème de super résolution temporelle de formes, par l'utilisation de multiples observations d'un même modèle déformé. Sans pertes de généralité, nous nous concentrons plus particulièrement au scénario multi-camera moyenne échelle, c'est à dire des scènes dynamiques, pouvant contenir plusieurs sujets. Ce contexte favorise l'utilisation de caméras couleur, mais nécessite une méthode de reconstruction robuste aux inconsistances photométriques. Dans ce but, nous proposons une nouvelle approche, spécialement dédiée à ce contexte moyenne échelle, utilisant des descripteurs et des schémas de votes adaptés. Cette méthode est étendue à la dimension temporelle de manière à améliorer les reconstructions à chaque instant, en exploitant la redondance des informations dans le temps. Pour cela, les informations photométriques fiables sont accumulées dans le temps à l'aide de champs de déformations combinés à une stratégie de croissance de région. Nous démontrons l'amélioration des reconstructions apportée par notre approche à l'aide de séquences multi-camera synthétiques