Transferring and Adapting Source Knowledge in Computer Vision and VisDA Challenge
In conjunction with ICCV 2019, Seoul, Korea
This is the 6th annual workshop that brings together computer vision researchers interested in domain adaptation and knowledge transfer techniques. Given the last year success, we will keep the Domain Adaptation Challenge.
A key ingredient of the recent successes in computer vision has been the availability of visual data with annotations, both for training and testing, and well-established protocols for evaluating the results. However, this traditional supervised learning framework is limited when it comes to deployment on new tasks and/or operating in new domains. In order to scale to such situations, we must find mechanisms to reuse the available annotations or the models learned from them.
Accordingly, TASK-CV aims to bring together research in transfer learning and domain adaptation for computer vision and invites the submission of research contributions on the following topics:
- TL/DA learning methods for challenging paradigms like unsupervised, incremental, and online learning.
- TL/DA focusing on specific visual features, models or learning algorithms.
- TL/DA jointly applied with other learning paradigms such as reinforcement learning.
- TL/DA in the era of convolutional neural networks (CNNs), adaptation effects of fine-tuning, regularization techniques, transfer of architectures and weights, etc.
- TL/DA focusing on specific computer vision tasks (e.g., image classification, object detection, semantic segmentation, recognition, retrieval, tracking, etc.) and applications (biomedical, robotics, multimedia, autonomous driving, etc.).
- Comparative studies of different TL/DA methods.
- Working frameworks with appropriate CV-oriented datasets and evaluation protocols to assess TL/DA methods.
- Transferring part representations between categories.
- Transferring tasks to new domains.
- Solving domain shift due to sensor differences (e.g., low-vs-high resolution, power spectrum sensitivity) and compression schemes.
- Datasets and protocols for evaluating TL/DA methods.
This is not a closed list; thus, we welcome other interesting and relevant research for TASK-CV.
Best Paper Awards