Discovering and synthesizing novel concepts with minimal supervision
Scaling up visual category recognition to large numbers of classes remains challenging. A promising research direction is zero-shot learning, which does not require any training data to recognize new classes, but rather relies on some form of auxiliary information describing new classes. We propose and compare different class embeddings learned automatically from unlabeled text corpora, expert annotated attributes and detailed visual descriptions. Furthermore, we use detailed visual descriptions to generate images from scratch and to generate visual explanations which justify a classification decision. Finally, we explore humans' natural ability to determine distinguishing properties of unknown objects through gaze fixations.
Zeynep Akata is an Assistant Professor at the University of Amsterdam and a Senior Researcher at the Max Planck Institute for Informatics. She received a MSc degree in 2011 from RWTH Aachen and a PhD degree in 2014 from INRIA Grenoble. Her research interests include machine learning with applications to computer vision, such as zero-shot learning and multimodal deep learning with generative models that combine vision and language. She received Lise-Meitner Award for Excellent Women in Computer Science from Max Planck Society in 2014 and a DARPA grant Explainable Artificial Intelligence in 2017 in collaboration with UC Berkeley.