LAMP

Tag: CVPR 2021 Workshop

  • Invited Talk at CL-Vision 2021

    Joost van de Weijer presented at the 2nd workshop on continual learning in computer vision at CVPR 2021. See here for the video and the slides.

  • Avalanche: an End-to-End Library for Continual Learning

    Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu, Antonio Carta, Gabriele Graffieti, Tyler L. Hayes, Matthias De Lange, Marc Masana, Jary Pomponi, Gido van de Ven, Martin Mundt, Qi She, Keiland Cooper, Jeremy Forest, Eden Belouadah, Simone Calderara, German I. Parisi, Fabio Cuzzolin, Andreas Tolias, Simone Scardapane, Luca Antiga, Subutai Amhad, Adrian Popescu, Christopher Kanan, Joost van de Weijer, Tinne Tuytelaars, Davide Bacciu, Davide Maltoni Read Full Paper → Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in […]

  • Ternary Feature Masks: zero-forgetting for task-incremental learning

    Marc Masana, Tinne Tuytelaars, Joost van de Weijer Read Full Paper → We propose an approach without any forgetting to continual learning for the task-aware regime, where at inference the task-label is known. By using ternary masks we can upgrade a model to new tasks, reusing knowledge from previous tasks while not forgetting anything about them. Using […]

  • Continual learning in cross-modal retrieval

    Kai Wang, Luis Herranz, Joost van de Weijer Read Full Paper → Multimodal representations and continual learning are two areas closely related to human intelligence. The former considers the learning of shared representation spaces where information from different modalities can be compared and integrated (we focus on cross-modal retrieval between language and visual representations). The latter studies […]

  • DANICE: Domain adaptation without forgetting in neural image compression

    Sudeep Katakol, Luis Herranz, Fei Yang, Marta Mrak Read Full Paper → Neural image compression (NIC) is a new coding paradigm where coding capabilities are captured by deep models learned from data. This data-driven nature enables new potential functionalities. In this paper, we study the adaptability of codecs to custom domains of interest. We show that NIC codecs […]