LAMP

Category: NeurIPS

  • Task-recency bias strikes back: Adapting covariances in Exemplar-Free Class Incremental Learning

    Grzegorz Rypeść, Sebastian Cygert, Tomasz Trzciński, Bartłomiej Twardowski Read Full Paper → Exemplar-Free Class Incremental Learning (EFCIL) tackles the problem of training a model on a sequence of tasks without access to past data. Existing state-of-the-art methods represent classes as Gaussian distributions in the feature extractor’s latent space, enabling Bayes classification or training the classifier by replaying pseudo […]

  • Token Merging for Training-Free Semantic Binding in Text-to-Image Synthesis

    Taihang Hu, Linxuan Li, Joost van de Weijer, Hongcheng Gao, Fahad Shahbaz Khan, Jian Yang, Ming-Ming Cheng, Kai Wang, Yaxing Wang Read Full Paper → Although text-to-image (T2I) models exhibit remarkable generation capabilities, they frequently fail to accurately bind semantically related objects or attributes in the input prompts; a challenge termed semantic binding. Previous approaches either involve intensive fine-tuning of the entire T2I […]

  • Faster Diffusion: Rethinking the Role of the Encoder for Diffusion Model Inference

    Senmao Li, Taihang Hu, Joost van de Weijer, Fahad Shahbaz Khan, Tao Liu, Linxuan Li, Shiqi Yang, Yaxing Wang, Ming-Ming Cheng, Jian Yang Read Full Paper → One of the main drawback of diffusion models is the slow inference time for image generation. Among the most successful approaches to addressing this problem are distillation methods. However, these methods require considerable computational resources. In this […]

  • FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning

    Dipam Goswami, Yuyang Liu, Bartłomiej Twardowski, Joost van de Weijer Read Full Paper → Exemplar-free class-incremental learning (CIL) poses several challenges since it prohibits the rehearsal of data from previous tasks and thus suffers from catastrophic forgetting. Recent approaches to incrementally learning the classifier by freezing the feature extractor after the first task have gained much attention. In […]

  • Two papers accepted at Neural Information Processing Systems (NeurIPS) 2023

  • IterInv: Iterative Inversion for Pixel-Level T2I Models

    Chuanming Tang, Kai Wang, Joost van de Weijer Read Full Paper → Large-scale text-to-image diffusion models have been a ground-breaking development in generating convincing images following an input text prompt. The goal of image editing research is to give users control over the generated images by modifying the text prompt. Current image editing techniques predominantly hinge on […]

  • Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing

    Kai Wang, Fei Yang, Shiqi Yang, Muhammad Atif Butt, Joost van de Weijer Read Full Paper → Large-scale text-to-image generative models have been a ground-breaking development in generative AI, with diffusion models showing their astounding ability to synthesize convincing images following an input text prompt. The goal of image editing research is to give users control over the generated […]

  • Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation

    Shiqi Yang, Yaxing Wang, Kai Wang, Shangling Jui, Joost van de Weijer Read Full Paper → We propose a simple but effective source-free domain adaptation (SFDA) method. Treating SFDA as an unsupervised clustering problem and following the intuition that local neighbors in feature space should have more similar predictions than other features, we propose to optimize an objective of […]

  • Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation

    Shiqi Yang, Yaxing Wang, Joost van de Weijer, Luis Herranz, Shangling Jui Read Full Paper → Domain adaptation (DA) aims to alleviate the domain shift between source domain and target domain. Most DA methods require access to the source data, but often that is not possible (e.g. due to data privacy or intellectual property). In this paper, we address […]

  • RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning

    Riccardo Del Chiaro, Bartłomiej Twardowski, Andrew D. Bagdanov, Joost van de Weijer Read Full Paper → Research on continual learning has led to a variety of approaches to mitigating catastrophic forgetting in feed-forward classification networks. Until now surprisingly little attention has been focused on continual learning of recurrent models applied to problems like image captioning. In this paper […]