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, and incremental or on-line 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 deep neural networks (e.g., 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 knowledge across modalities (e.g., learning from 3D data for recognizing 2D data, and heterogeneous transfer learning)

• 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.