Workshop on Multi-Task Learning in Computer Vision
Despite the recent progress in deep learning, most approaches still go for a silo-like solution, training a separate neural network for each individual task. Many real-world problems, however, call for a multi-modal approach and, therefore, for multi-tasking models. In this full-day workshop, we aim to provide a well-rounded view of recent trends in multi-task learning, while also identifying the current challenges in the field. More specifically, we aim to examine a variety of subtopics under the multi-task learning setup, including network architecture designs, neural architecture search, optimization strategies, task transfer relationships, meta-learning, single-tasking of multiple tasks, etc.
With the organization of this workshop, we hope to bring together a diverse group of researchers that have worked on multi-task learning, and raise attention at large to further investigate a topic that has been mostly under-explored by the computer vision community.
Update: The recordings of our invited talks are now available on Youtube.
We would like to acknowledge support by Toyota via the TRACE project and MACCHINA (KU Leuven, C14/18/065). This initiative is also sponsored by the Flemish Government under the Flemish AI program. Finally, we thank the people that helped us with the paper review process (see call for papers).