57 citations · 99 across the 6 of their papers we have counts for
7 papers
No Annotations for Object Detection in Art through Stable Diffusion
Patrick Ramos, Nicolas Gonthier, Selina Khan +2
Object detection in art is a valuable tool for the digital humanities, as it allows for faster identification of objects in artistic and historical images compared to humans. Howev…
GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates
Valerio Marsocci, Nicolas Gonthier, Anatol Garioud +2
Land cover maps are a pivotal element in a wide range of Earth Observation (EO) applications. However, annotating large datasets to develop supervised systems for remote sensing (R…
The Learnable Typewriter: A Generative Approach to Text Analysis
Ioannis Siglidis, Nicolas Gonthier, Julien Gaubil +2
We present a generative document-specific approach to character analysis and recognition in text lines. Our main idea is to build on unsupervised multi-object segmentation methods…
An analysis of the transfer learning of convolutional neural networks for artistic images
Nicolas Gonthier, Yann Gousseau, Saïd Ladjal
Transfer learning from huge natural image datasets, fine-tuning of deep neural networks and the use of the corresponding pre-trained networks have become de facto the core of art a…
High resolution neural texture synthesis with long range constraints
Nicolas Gonthier, Yann Gousseau, Saïd Ladjal
The field of texture synthesis has witnessed important progresses over the last years, most notably through the use of Convolutional Neural Networks. However, neural synthesis meth…
Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts
Nicolas Gonthier, Saïd Ladjal, Yann Gousseau
Weakly supervised object detection (WSOD) using only image-level annotations has attracted a growing attention over the past few years. Whereas such task is typically addressed wit…
Weakly Supervised Object Detection in Artworks
Nicolas Gonthier, Yann Gousseau, Said Ladjal +1
We propose a method for the weakly supervised detection of objects in paintings. At training time, only image-level annotations are needed. This, combined with the efficiency of ou…