1 code implementation • 13 Dec 2023 • Gwilherm Lesné, Yann Gousseau, Saïd Ladjal, Alasdair Newson
Recent advances in the field of generative models and in particular generative adversarial networks (GANs) have lead to substantial progress for controlled image editing, especially compared with the pre-deep learning era.
no code implementations • 2 Nov 2023 • Nicolas Cherel, Andrés Almansa, Yann Gousseau, Alasdair Newson
We show that in the case of video inpainting, thanks to the highly auto-similar nature of videos, the training of a diffusion model can be restricted to the video to inpaint and still produce very satisfying results.
no code implementations • 3 Feb 2023 • Pierrick Chatillon, Yann Gousseau, Sidonie Lefebvre
In particular, various papers have shown that the learning stage can be performed on a single image, resulting in the so-called internal approaches.
1 code implementation • 3 Feb 2023 • Pierrick Chatillon, Yann Gousseau, Sidonie Lefebvre
We propose an auto-encoder architecture for multi-texture synthesis.
1 code implementation • 7 Feb 2022 • Nicolas Cherel, Andrés Almansa, Yann Gousseau, Alasdair Newson
We refer to our proposed layer as a "Patch-based Stochastic Attention Layer" (PSAL).
1 code implementation • 4 Feb 2022 • Xu Yao, Alasdair Newson, Yann Gousseau, Pierre Hellier
Additionally, we demonstrate that the proposed encoder is especially well-suited for inversion and editing on videos.
no code implementations • 31 Dec 2021 • Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch, Yann Gousseau
Large scale datasets created from crowdsourced labels or openly available data have become crucial to provide training data for large scale learning algorithms.
1 code implementation • ICCV 2021 • Xu Yao, Alasdair Newson, Yann Gousseau, Pierre Hellier
Previous works that attempt to tackle this problem may suffer from the entanglement of facial attributes and the loss of the person's identity.
no code implementations • 5 Nov 2020 • 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 analysis applications.
2 code implementations • 4 Aug 2020 • Nicolas Gonthier, Yann Gousseau, Saïd Ladjal
Experiments show the interest of the multi-scale scheme for high resolution textures and the interest of combining it with additional constraints for regular textures.
2 code implementations • 3 Aug 2020 • 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.
Ranked #1 on Weakly Supervised Object Detection on CASPAPaintings
2 code implementations • 9 May 2020 • Xu Yao, Gilles Puy, Alasdair Newson, Yann Gousseau, Pierre Hellier
We present an encoder-decoder architecture for face age editing.
no code implementations • 17 Apr 2019 • Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch, Yann Gousseau
Large scale datasets created from user labels or openly available data have become crucial to provide training data for large scale learning algorithms.
no code implementations • 15 Apr 2019 • Alasdair Newson, Andrés Almansa, Yann Gousseau, Saïd Ladjal
This results in a wide range of practical problems, such as difficulties in training, the tendency to sample images with little or no variability, and generalisation problems.
1 code implementation • 19 Oct 2018 • Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch, Yann Gousseau
The Copernicus Sentinel-2 program now provides multispectral images at a global scale with a high revisit rate.
no code implementations • 19 Oct 2018 • Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch, Yann Gousseau
In this paper we present the first large scale high resolution semantic change detection (HRSCD) dataset, which enables the usage of deep learning methods for semantic change detection.
2 code implementations • ECCV 2018 Workshop Computer Vision for Art Analysis - VISART 2018 2018 • Nicolas Gonthier, Yann Gousseau, Said Ladjal, Olivier Bonfait
We propose a method for the weakly supervised detection of objects in paintings.
Ranked #2 on Weakly Supervised Object Detection on PeopleArt
no code implementations • ICLR 2018 • Alasdair Newson, Andres Almansa, Yann Gousseau, Said Ladjal
We study the precise mechanisms which allow autoencoders to encode and decode a simple geometric shape, the disk.
no code implementations • 10 Jun 2017 • Cecilia Aguerrebere, Andrés Almansa, Julie Delon, Yann Gousseau, Pablo Musé
In this work, we propose the use of a hyperprior to model image patches, in order to stabilize the estimation procedure.
2 code implementations • 4 May 2016 • Gang Liu, Yann Gousseau, Gui-Song Xia
This paper presents a significant improvement for the synthesis of texture images using convolutional neural networks (CNNs), making use of constraints on the Fourier spectrum of the results.
no code implementations • 18 Mar 2015 • Alasdair Newson, Andrés Almansa, Matthieu Fradet, Yann Gousseau, Patrick Pérez
Our algorithm is able to deal with a variety of challenging situations which naturally arise in video inpainting, such as the correct reconstruction of dynamic textures, multiple moving objects and moving background.