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.