Workshop On Learning with Few or Without Annotated Face, Body and Gesture Data Call for Papers

In conjunction with the 17th International Conference on Automatic
Face and Gesture Recognition (FG 2023), January 4-8 2023, we are
organizing a workshop on learning with few or without annotated Face,
Body and Gesture data.

The deadline for submission has been extended: September 22nd, 2022
Please find more information here:, as well as the CFP below.

Best Regards,
Mohamed, Stefano, Jonathan, Germain and Maxime




Since more than a decade, Deep Learning has been successfully employed
for vision-based face, body and gesture analysis, both for static and
dynamic granularities. This is particularly due to the development of
effective deep architectures and the release of quite consequent

However, one of the main limitations of Deep Learning is that it
requires large scale annotated datasets to train efficient
models. Gathering such face, body or gesture data and annotating them
can be very very time consuming and laborious. This is particularly
the case in areas where experts from the field are required, like in
the medical domain. In such a case, using crowdsourcing may not be

In addition, currently available face and/or datasets cover a limited
set of categories. This makes the adaptation of trained models to
novel categories not straightforward. Finally, while most of the
available datasets focus on classification problems with discretized
labels, continuous annotations are required in many scenarios. Hence,
this significantly complicates the annotation process.  The goal of
this workshop is to explore approaches to overcome such limitations by
investigating ways to learn from few annotated data, to transfer
knowledge from similar domains or problems, or to benefit from the
community to gather novel large scale annotated datasets.


We encourage scientists and industrials to submit their contribution
under one of the following topic of interest but also welcome any
novel relevant research in the field:

    Data augmentation methods for face, body and gesture
    Generative models for face, body and gesture
    Zero-shot / few-shot learning for face, body and gesture
    Self supervised Learning for face, body and gesture
    Weakly supervised learning for face, body and gesture
    Semi-supervised learning for face, body and gesture
    Transfer Learning for face, body and gesture
    Adaptive/Continuous learning for face, body and gesture
    New annotated face, body and gesture benchmarks


Paper Submission: September 22nd, 2022 
Authors Notification: October 15th, 2022
Camera Ready: October 31st, 2022


Dr. Maxime Devanne, UniversitÚ de Haute-Alsace,
Prof. Mohamed Daoudi, IMT Nord Europe,
Prof. Stefano Berretti, UniversitÓ di Firenze,
Dr. Jonathan Weber, UniversitÚ de Haute-Alsace,
Prof. Germain Forestier, UniversitÚ de Haute-Alsace,