activity
20242026
collaborators

7 papers

cs.CV2026

Example-based Robust Abnormality Detection with Minimal Annotations using Exemplar Med-DETR

Sheethal Bhat, Bogdan Georgescu, Awais Mansoor +5

Reducing annotation requirements remains a key challenge in developing robust medical object detectors. To address this, Vision-Language (VL) object detection methods leverage grou…

cs.CV2025

Data Augmentation via Latent Diffusion Models for Detecting Smell-Related Objects in Historical Artworks

Ahmed Sheta, Mathias Zinnen, Aline Sindel +2

Finding smell references in historic artworks is a challenging problem. Beyond artwork-specific challenges such as stylistic variations, their recognition demands exceptionally det…

cs.CV2025

Exemplar Med-DETR: Toward Generalized and Robust Lesion Detection in Mammogram Images and beyond

Sheethal Bhat, Bogdan Georgescu, Adarsh Bhandary Panambur +8

Detecting abnormalities in medical images poses unique challenges due to differences in feature representations and the intricate relationship between anatomical structures and abn…

cs.CV2025

Smelly, dense, and spreaded: The Object Detection for Olfactory References (ODOR) dataset

Mathias Zinnen, Prathmesh Madhu, Inger Leemans +6

Real-world applications of computer vision in the humanities require algorithms to be robust against artistic abstraction, peripheral objects, and subtle differences between fine-g…

cs.CV2024

Gesture Classification in Artworks Using Contextual Image Features

Azhar Hussian, Mathias Zinnen, Thi My Hang Tran +2

Recognizing gestures in artworks can add a valuable dimension to art understanding and help to acknowledge the role of the sense of smell in cultural heritage. We propose a method…

cs.CV2024

Novel Artistic Scene-Centric Datasets for Effective Transfer Learning in Fragrant Spaces

Shumei Liu, Haiting Huang, Mathias Zinnen +2

Olfaction, often overlooked in cultural heritage studies, holds profound significance in shaping human experiences and identities. Examining historical depictions of olfactory scen…