Bartłomiej Twardowski — accepted AI-conference papers

2026

IsoCLIP: Decomposing CLIP Projectors for Efficient Intra-modal Alignment

Simone Magistri, Dipam Goswami, Marco Mistretta, Bartłomiej Twardowski, Joost van de Weijer, Andrew D. Bagdanov

CVPR 2026

Vision-Language Models like CLIP are extensively used for inter-modal tasks which involve both visual and text modalities. However, when the individual modality encoders are applied to inherently intra-modal tasks like image-to-image retrieval, their performance suffers from the intra-modal misalign

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2026

Online Continual Learning with Dynamic Label Hierarchies

Xinrui Wang, Shao-Yuan Li, Bartłomiej Twardowski, Alexandra Gomez-Villa, Songcan Chen

ICML 2026poster

Online Continual Learning (OCL) aims to learn from endless non\text{-}stationary data streams, yet most existing methods assume a flat label space and overlook the hierarchical organization of real\text{-}world concepts that evolves both horizontally (sibling classes) and vertically (coarse or fine …

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2025

Accurate and Efficient Low-Rank Model Merging in Core Space

Aniello Panariello, Daniel Marczak, Simone Magistri, Angelo Porrello, Bartłomiej Twardowski, Andrew D. Bagdanov +2

NeurIPS 2025poster

In this paper, we address the challenges associated with merging low-rank adaptations of large neural networks. With the rise of parameter-efficient adaptation techniques, such as Low-Rank Adaptation (LoRA), model fine-tuning has become more accessible. While fine-tuning models with LoRA is highly e…

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2025

Covariances for Free: Exploiting Mean Distributions for Training-free Federated Learning

Dipam Goswami, Simone Magistri, Kai Wang, Bartłomiej Twardowski, Andrew D. Bagdanov, Joost van de Weijer

NeurIPS 2025poster

Using pre-trained models has been found to reduce the effect of data heterogeneity and speed up federated learning algorithms. Recent works have explored training-free methods using first- and second-order statistics to aggregate local client data distributions at the server and achieve high perform…

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2025

Improving Continual Learning Performance and Efficiency with Auxiliary Classifiers

Filip Szatkowski, Yaoyue Zheng, Fei Yang, Tomasz Trzcinski, Bartłomiej Twardowski, Joost van de Weijer

ICML 2025poster

Continual learning is crucial for applying machine learning in challenging, dynamic, and often resource-constrained environments. However, catastrophic forgetting — overwriting previously learned knowledge when new information is acquired — remains a major challenge. In this work, we examine the int…

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2025

No Task Left Behind: Isotropic Model Merging with Common and Task-Specific Subspaces

Daniel Marczak, Simone Magistri, Sebastian Cygert, Bartłomiej Twardowski, Andrew D. Bagdanov, Joost van de Weijer

ICML 2025poster

Model merging integrates the weights of multiple task-specific models into a single multi-task model. Despite recent interest in the problem, a significant performance gap between the combined and single-task models remains. In this paper, we investigate the key characteristics of task matrices --…

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2025

The Art of Deception: Color Visual Illusions and Diffusion Models

Alexandra Gomez-Villa, Kai Wang, C.Alejandro Parraga, Bartłomiej Twardowski, Jesus Malo, Javier Vazquez-Corral +1

CVPR 2025poster

Visual illusions in humans arise when interpreting out-of-distribution stimuli: if the observer is adapted to certain statistics, perception of outliers deviates from reality. Recent studies have shown that artificial neural networks (ANNs) can also be deceived by visual illusions.This revelation ra…

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2024

Divide and not forget: Ensemble of selectively trained experts in Continual Learning

Grzegorz Rypeść, Sebastian Cygert, Valeriya Khan, Tomasz Trzcinski, Bartosz Michał Zieliński, Bartłomiej Twardowski

ICLR 2024poster

Class-incremental learning is becoming more popular as it helps models widen their applicability while not forgetting what they already know. A trend in this area is to use a mixture-of-expert technique, where different models work together to solve the task. However, the experts are usually trained…

Cited by 44 Source ↗PDF ↗code ↗Scholar ↗

2024

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

Grzegorz Rypeść, Sebastian Cygert, Tomasz Trzcinski, Bartłomiej Twardowski

NeurIPS 2024poster

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 trainin…

Cited by 4 Source ↗PDF ↗code ↗Scholar ↗

2023

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

Dipam Goswami, Yuyang Liu, Bartłomiej Twardowski, Joost van de Weijer

NeurIPS 2023poster

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 g…

Cited by 72 Source ↗PDF ↗code ↗Scholar ↗

2023

Planckian Jitter: countering the color-crippling effects of color jitter on self-supervised training

Simone Zini, Alex Gomez-Villa, Marco Buzzelli, Bartłomiej Twardowski, Andrew D. Bagdanov, Joost van de weijer

ICLR 2023poster

Several recent works on self-supervised learning are trained by mapping different augmentations of the same image to the same feature representation. The data augmentations used are of crucial importance to the quality of learned feature representations. In this paper, we analyze how the color jitte…

Cited by 15 Source ↗PDF ↗code ↗Scholar ↗

2020

RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning

Riccardo Del Chiaro, Bartłomiej Twardowski, Andrew D. Bagdanov, Joost van de Weijer

NeurIPS 2020poster

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…

Cited by 59 Source ↗PDF ↗code ↗Scholar ↗