no code implementations • 6 Dec 2023 • Ivona Najdenkoska, Animesh Sinha, Abhimanyu Dubey, Dhruv Mahajan, Vignesh Ramanathan, Filip Radenovic
We propose Context Diffusion, a diffusion-based framework that enables image generation models to learn from visual examples presented in context.
no code implementations • 30 Sep 2023 • Mohammad Mahdi Derakhshani, Ivona Najdenkoska, Cees G. M. Snoek, Marcel Worring, Yuki M. Asano
We present Self-Context Adaptation (SeCAt), a self-supervised approach that unlocks few-shot abilities for open-ended classification with small visual language models.
1 code implementation • 10 Mar 2023 • Tom van Sonsbeek, Mohammad Mahdi Derakhshani, Ivona Najdenkoska, Cees G. M. Snoek, Marcel Worring
Most existing methods approach it as a multi-class classification problem, which restricts the outcome to a predefined closed-set of curated answers.
Ranked #1 on Medical Visual Question Answering on OVQA
1 code implementation • 28 Feb 2023 • Ivona Najdenkoska, XianTong Zhen, Marcel Worring
Existing methods are trying to communicate visual concepts as prompts to frozen language models, but rely on hand-engineered task induction to reduce the hypothesis space.
1 code implementation • 12 Apr 2022 • Mohammad Mahdi Derakhshani, Ivona Najdenkoska, Tom van Sonsbeek, XianTong Zhen, Dwarikanath Mahapatra, Marcel Worring, Cees G. M. Snoek
Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch, while cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge.
no code implementations • 15 Jul 2021 • Ivona Najdenkoska, XianTong Zhen, Marcel Worring, Ling Shao
The topics are inferred in a conditional variational inference framework, with each topic governing the generation of a sentence in the report.