This workshop aims at promoting discussions among researchers investigating learning from the scarce data. Typically, domain adaptation, transfer of knowledge, zero-, one- or few-shot learning are examples of inference from the scarce data. However, rapid progress has been made in domain adaptation and few-shot learning thanks to Convolutional Neural Networks. The well-established benchmarks such as the famous Office dataset have been nearly saturated with algorithms reaching ~90% accuracy. This workshop aims at going beyond conventional datasets and conventional approaches thus posing a new challenge that aiming to shake up the status quo.


We encourage discussions on recent advances, ongoing developments, and novel applications of domain adaptation, zero-, one- and few-shot learning. We are soliciting ivited talks that address a wide range of theoretical and practical issues including, but not limited to:


Below is the program of the workshop on the 2nd of December, 2018. Bear in mind there will be some last minute reshuffling of the schedule due to any unforseen clashes in timetables of our speakers etc. Please check Detailed Program below for the abstracts and biographies of our invited speakers (or click on links in tables).

Afternoon Session

Time Invited Speaker Title
13:30 Dr. P. Koniusz, Dr. M. Harandi Welcome
13:35 Assoc. Prof. Krystian Mikolajczyk Domain Transfer with Semantic Grouping and Robust Pseudo Labelling
14:05 Assoc. Prof. Lei Wang Unsupervised Feature Adaptation for Image Retrieval via Diffusion Process
14:50 Dr. Mathieu Salzmann Unshared Weights for Deep Domain Adaptation
15:35 Coffee break Venue
16:00 Dr. Gabriela Csurka New Trends in Visual Domain Adaptation
16:45 Dr. P. Koniusz, Dr. M. Harandi Few words about the Open MIC dataset and closing remarks



Below is the list of speakers (in alphabetical order) who have kindly agreed to give talks during the workshop: