1 citations · 2 across the 6 of their papers we have counts for
8 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…