Search Results for author: Ivona Najdenkoska

Found 6 papers, 3 papers with code

Context Diffusion: In-Context Aware Image Generation

no code implementations6 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.

Image Generation In-Context Learning

Self-Supervised Open-Ended Classification with Small Visual Language Models

no code implementations30 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.

Few-Shot Learning Image Captioning

Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot Learning

1 code implementation28 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.

Few-Shot Learning

LifeLonger: A Benchmark for Continual Disease Classification

1 code implementation12 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.

Classification Class Incremental Learning +1

Variational Topic Inference for Chest X-Ray Report Generation

no code implementations15 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.

Sentence Text Generation +1

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