Low-shot learning with large-scale diffusion

This paper considers the problem of inferring image labels from images when only a few annotated examples are available at training time. This setup is often referred to as low-shot learning, where a standard approach is to re-train the last few layers of a convolutional neural network learned on separate classes for which training examples are abundant. We consider a semi-supervised setting based on a large collection of images to support label propagation. This is possible by leveraging the recent advances on large-scale similarity graph construction. We show that despite its conceptual simplicity, scaling label propagation up to hundred millions of images leads to state of the art accuracy in the low-shot learning regime

Multi-score Learning for Affect Recognition: the Case of Body Postures

An important challenge in building automatic affective state recognition systems is establishing the ground truth. When the groundtruth is not available, observers are often used to label training and testing sets. Unfortunately, inter-rater reliability between observers tends to vary from fair to moderate when dealing with naturalistic expressions. Nevertheless, the most common approach used is to label each expression with the most frequent label assigned by the observers to that expression. In this paper, we propose a general pattern recognition framework that takes into account the variability between observers for automatic affect recognition. This leads to what we term a multi-score learning problem in which a single expression is associated with multiple values representing the scores of each available emotion label. We also propose several performance measurements and pattern recognition methods for this framework, and report the experimental results obtained when testing and comparing these methods on two affective posture datasets