Vision for All Seasons

Adverse weather and illumination conditions (e.g. fog, rain, snow, nighttime, glare and shadows) create visibility problems for the sensors that power automated systems. Many outdoor applications such as autonomous cars and surveillance systems are required to operate smoothly in the frequent scenarios of bad weather. While rapid progress is being made in this direction, the performance of current vision algorithms is still mainly benchmarked under clear weather conditions. Even the top-performing algorithms undergo a severe performance degradation under adverse conditions. The aim of this workshop is to promote research into the design of robust vision algorithms for adverse weather and illumination conditions.

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DeepMTL: 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.

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Workshop on Autonomous Driving

Autonomous driving (AD) will have a substantial impact on people’s daily life, both personally and professionally. As such, developing automated vehicles is becoming the core interest of several industrial and academic players. With so much effort poured into this field, all technologies concerned with AD are enjoying great progress. While it is exciting to see rapid advances in so many sub-fields, it is becoming hard to keep an overview of topics related to Autonomous Driving. Our goal therefore is to provide a better overview of recent challenges and trends for both researchers and practitioners.

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