LAMP

Category: ECCV

  • Exemplar-free Continual Representation Learning via Learnable Drift Compensation

    Alex Gomez-Villa, Dipam Goswami, Kai Wang, Andrew D. Bagdanov, Bartlomiej Twardowski, Joost van de Weijer Read Full Paper → Exemplar-free class-incremental learning using a backbone trained from scratch and starting from a small first task presents a significant challenge for continual representation learning. Prototype-based approaches, when continually updated, face the critical issue of semantic drift due to which the old […]

  • ColorPeel: Color Prompt Learning with Diffusion Models via Color and Shape Disentanglement

    Muhammad Atif Butt, Kai Wang, Javier Vazquez-Corral, Joost van de Weijer Read Full Paper → Text-to-Image (T2I) generation has made significant advancements with the advent of diffusion models. These models exhibit remarkable abilities to produce images based on textual prompts. Current T2I models allow users to specify object colors using linguistic color names. However, these labels encompass broad […]

  • Enhancing Perceptual Quality in Video Super-Resolution through Temporally-Consistent Detail Synthesis using Diffusion Models

    Claudio Rota, Marco Buzzelli, Joost van de Weijer Read Full Paper → In this paper, we address the problem of enhancing perceptual quality in video super-resolution (VSR) using Diffusion Models (DMs) while ensuring temporal consistency among frames. We present StableVSR, a VSR method based on DMs that can significantly enhance the perceptual quality of upscaled videos by […]

  • MagMax: Leveraging Model Merging for Seamless Continual Learning

    Daniel Marczak, Bartłomiej Twardowski, Tomasz Trzciński, Sebastian Cygert Read Full Paper → This paper introduces a continual learning approach named MagMax, which utilizes model merging to enable large pre-trained models to continuously learn from new data without forgetting previously acquired knowledge. Distinct from traditional continual learning methods that aim to reduce forgetting during task training, MagMax combines sequential […]

  • Revisiting Supervision for Continual Representation Learning

    Daniel Marczak, Sebastian Cygert, Tomasz Trzciński, Bartłomiej Twardowski Read Full Paper → In the field of continual learning, models are designed to learn tasks one after the other. While most research has centered on supervised continual learning, there is a growing interest in unsupervised continual learning, which makes use of the vast amounts of unlabeled data. Recent studies […]

  • Category Adaptation Meets Projected Distillation in Generalized Continual Category Discovery

    Grzegorz Rypeść, Daniel Marczak, Sebastian Cygert, Tomasz Trzciński, Bartłomiej Twardowski Read Full Paper → Generalized Continual Category Discovery (GCCD) tackles learning from sequentially arriving, partially labeled datasets while uncovering new categories. Traditional methods depend on feature distillation to prevent forgetting the old knowledge. However, this strategy restricts the model’s ability to adapt and effectively distinguish new categories. To address […]

  • Bookworm continual learning: beyond zero-shot learning and continual learning

    Kai Wang, Luis Herranz, Anjan Dutta, Joost van de Weijer Read Full Paper → We propose bookworm continual learning(BCL), a flexible setting where unseen classes can be inferred via a semantic model, and the visual model can be updated continually. Thus BCL generalizes both continual learning (CL) and zero-shot learning (ZSL). We also propose the bidirectional imagination (BImag) […]

  • Transferring GANs: generating images from limited data

    Yaxing Wang, Chenshen Wu, Luis Herranz, Joost van de Weijer, Abel Gonzalez-Garcia, Bogdan Raducanu Read Full Paper → Transferring the knowledge of pretrained networks to new domains by means of finetuning is a widely used practice for applications based on discriminative models. To the best of our knowledge this practice has not been studied within the context of generative deep […]