With the recent renaissance of deep convolutional neural networks (CNNs), encouraging
breakthroughs have been achieved on the supervised recognition tasks, where each class …

From the beginning of zero-shot learning research, visual attributes have been shown to
play an important role. In order to better transfer attribute-based knowledge from known to …

Most machine-learning methods focus on classifying instances whose classes have already
been seen in training. In practice, many applications require classifying instances whose …

A number of studies have found that today's Visual Question Answering (VQA) models are
heavily driven by superficial correlations in the training data and lack sufficient image …

Zero-shot learning consists in learning how to recognize new concepts by just having a
description of them. Many sophisticated approaches have been proposed to address the …

Given semantic descriptions of object classes, zero-shot learning aims to accurately
recognize objects of the unseen classes, from which no examples are available at the …

Automatic facial behavior analysis has a long history of studies in the intersection of
computer vision, physiology and psychology. However it is only recently, with the collection …

People easily recognize new visual categories that are new combinations of known
components. This compositional generalization capacity is critical for learning in real-world …

This paper proposes a joint multi-task learning algorithm to better predict attributes in
images using deep convolutional neural networks (CNN). We consider learning binary …

Compositionality and contextuality are key building blocks of intelligence. They allow us to
compose known concepts to generate new and complex ones. However, traditional learning …