TASK-CV 2014 : Transferring and Adapting Source Knowledge in Computer Vision

Country: Switzerland

City: Zürich

Abstr. due: 07.07.2014

Dates: 12.09.14 — 12.09.14

Area Of Sciences: Computer science;

Organizing comittee e-mail: sramosp@cvc.uab.es

Organizers: ECCV


During the first decade of the XXI century, progress in machine learning has had an enormous impact in computer vision. The ability to learn models from data has boosted tasks such as classification, detection, segmentation, recognition, tracking, etc.

A key ingredient of such a success has been the use of visual data with annotations, both for training and testing, and well established protocols for evaluating the results.

However, most of the time, annotating visual information is a tiresome human activity prone to errors. Thus, for addressing new tasks and/or operating in new domains, it is worth it to aspire to reuse the available annotations or the models learned from them.

Therefore, transferring and adapting source knowledge (in the form of annotated data or learned models) has recently emerged as a challenge to develop computer vision methods that are reliable across domains and tasks.

Accordingly, the TASK-CV workshop aims to bring together research in transfer learning (TL) and domain adaptation (DA) for computer vision. We invite the submission of original research contributions such as:
- TL/DA learning methods for challenging paradigms like unsupervised, and incremental or on-line learning.
- TL/DA focusing on specific visual features (HOG, LBP, etc.), models (holistic, DPM, BoW, etc.), or learning algorithms (SVM, AdaBoost, CNN, Random Forest, etc.).
- TL/DA focusing on specific computer vision tasks such as classification, detection, segmentation, recognition, tracking, 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.
- Facing 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; therefore, we welcome other interesting and relevant research on TASK for CV problems.

Conference Web-Site: http://www.cvc.uab.es/adas/task-cv2014/