Domain Adaptation and Continual Learning for Visual Recognition

Deep networks have significantly improved the state of the arts for several tasks in computer vision. Unfortunately, the impressive performance gains have come at the price of the use of massive amounts of labeled data. As the cost of collecting and annotating data is often prohibitive, given a target task where few or no training samples are available, it would be desirable to build effective learners that can leverage information from labeled data
of a different but related source domain. However, a major obstacle in adapting models to the target task is the shift in data distributions across different domains. This problem, typically referred as domain shift, has motivated research into Domain Adaptation (DA). Another major issue with deep networks is their inherent difficulty to learn sequentially over a large number of tasks without forgetting knowledge obtained from the previous tasks. This last problem is addressed by Continual Learning (CL) methods. In the last few years, we developed several deep learning-based approaches for DA and CL for visual recognition and robot perception tasks.

Relevant publications
  • F.M. Carlucci, L. Porzi, B. Caputo, E. Ricci, S.R. Bulo, MultiDIAL: Domain Alignment Layers for (Multisource) Unsupervised Domain Adaptation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.
  • M. Mancini, L. Porzi, S. Rota Bulò, B. Caputo, E. Ricci, Inferring Latent Domains for Unsupervised Deep Domain Adaptation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019.
  • E. Fini, S. Lathuilière, E. Sangineto, M. Nabi, E. Ricci, Online Continual Learning under Extreme Memory Constraints. European Conference on Computer Vision (ECCV), 2020.
  • W. Menapace, S. Lathuilière, E. Ricci, Learning to Cluster under Domain Shift, European Conference on Computer Vision (ECCV), 2020.
  • M. Mancini, S.R. Bulò, B. Caputo, E. Ricci, AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs, CVPR 2019. Spotlight Oral, 8% acceptance rate
  • S Roy, A Siarohin, E Sangineto, SR Bulo, N Sebe, E Ricci, Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss, CVPR 2019.
  • M. Mancini, L. Porzi, S. Rota Bulò, B. Caputo, E. Ricci: Boosting Domain Adaptation by Discovering Latent Domains. CVPR 2018. Spotlight Oral, 8% acceptance rate