Dipam Goswami — 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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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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2024

Exemplar-free Continual Representation Learning via Learnable Drift Compensation

Alex Gomez-Villa*, Dipam Goswami, Kai Wang, Andy Bagdanov, Bartlomiej Twardowski, Joost van de Weijer

ECCV 2024poster

"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 th…

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2024

Resurrecting Old Classes with New Data for Exemplar-Free Continual Learning

Dipam Goswami, Albin Soutif-Cormerais, Yuyang Liu, Sandesh Kamath, Bart?omiej Twardowski, Joost van de Weijer

CVPR 2024poster

Continual learning methods are known to suffer from catastrophic forgetting a phenomenon that is particularly hard to counter for methods that do not store exemplars of previous tasks. Therefore to reduce potential drift in the feature extractor existing exemplar-free methods are typically evaluated…

Cited by 20 Source ↗PDF ↗code ↗Scholar ↗

2023

Augmented Box Replay: Overcoming Foreground Shift for Incremental Object Detection

Yuyang Liu, Yang Cong, Dipam Goswami, Xialei Liu, Joost van de Weijer

ICCV 2023poster

In incremental learning, replaying stored samples from previous tasks together with current task samples is one of the most efficient approaches to address catastrophic forgetting. However, unlike incremental classification, image replay has not been successfully applied to incremental object detect…

Cited by 32 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 ↗