11 citations · 23 across the 11 of their papers we have counts for
11 papers
FUSED: Forensic-Semantic Mixture-of-Experts for AI Inpainting Detection and Localization
Anton Nuzhdin, Marcel Worring, Ivona Najdenkoska
Diffusion-based inpainting models modify only a localized part of an image, while many AI-image detectors rely on global artifacts and do not localize. These artifacts vary across…
LATTE: Latent Trajectory Embedding for Diffusion-Generated Image Detection
Ana Vasilcoiu, Ivona Najdenkoska, Zeno Geradts +1
The rapid advancement of diffusion-based image generators has made it increasingly difficult to distinguish generated from real images. This erodes trust in digital media, making i…
ArtRAG: Retrieval-Augmented Generation with Structured Context for Visual Art Understanding
Shuai Wang, Ivona Najdenkoska, Hongyi Zhu +4
Understanding visual art requires reasoning across multiple perspectives -- cultural, historical, and stylistic -- beyond mere object recognition. While recent multimodal large lan…
TULIP: Token-length Upgraded CLIP
Ivona Najdenkoska, Mohammad Mahdi Derakhshani, Yuki M. Asano +3
We address the challenge of representing long captions in vision-language models, such as CLIP. By design these models are limited by fixed, absolute positional encodings, restrict…
In-Context Learning Improves Compositional Understanding of Vision-Language Models
Matteo Nulli, Anesa Ibrahimi, Avik Pal +2
Vision-Language Models (VLMs) have shown remarkable capabilities in a large number of downstream tasks. Nonetheless, compositional image understanding remains a rather difficult ta…
Context Diffusion: In-Context Aware Image Generation
Ivona Najdenkoska, Animesh Sinha, Abhimanyu Dubey +3
We propose Context Diffusion, a diffusion-based framework that enables image generation models to learn from visual examples presented in context. Recent work tackles such in-conte…
Self-Supervised Open-Ended Classification with Small Visual Language Models
Mohammad Mahdi Derakhshani, Ivona Najdenkoska, Cees G. M. Snoek +2
We present Self-Context Adaptation (SeCAt), a self-supervised approach that unlocks few-shot abilities for open-ended classification with small visual language models. Our approach…
Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language Models
Tom van Sonsbeek, Mohammad Mahdi Derakhshani, Ivona Najdenkoska +2
Medical Visual Question Answering (VQA) is an important challenge, as it would lead to faster and more accurate diagnoses and treatment decisions. Most existing methods approach it…
Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot Learning
Ivona Najdenkoska, Xiantong Zhen, Marcel Worring
Multimodal few-shot learning is challenging due to the large domain gap between vision and language modalities. Existing methods are trying to communicate visual concepts as prompt…
LifeLonger: A Benchmark for Continual Disease Classification
Mohammad Mahdi Derakhshani, Ivona Najdenkoska, Tom van Sonsbeek +4
Deep learning models have shown a great effectiveness in recognition of findings in medical images. However, they cannot handle the ever-changing clinical environment, bringing new…
Variational Topic Inference for Chest X-Ray Report Generation
Ivona Najdenkoska, Xiantong Zhen, Marcel Worring +1
Automating report generation for medical imaging promises to reduce workload and assist diagnosis in clinical practice. Recent work has shown that deep learning models can successf…