no code implementations • 23 Sep 2024 • Théo Bodrito, Olivier Flasseur, Julien Mairal, Jean Ponce, Maud Langlois, Anne-Marie Lagrange
Most of these post-processing methods build a model of the nuisances from the target observations themselves, resulting in strongly limited detection sensitivity at short angular separations due to the lack of angular diversity.
1 code implementation • 14 Sep 2024 • Elliot Vincent, Mehraïl Saroufim, Jonathan Chemla, Yves Ubelmann, Philippe Marquis, Jean Ponce, Mathieu Aubry
Archaeological sites are the physical remains of past human activity and one of the main sources of information about past societies and cultures.
1 code implementation • 10 Jul 2024 • Elliot Vincent, Jean Ponce, Mathieu Aubry
We show that the spatial domain shift represents the most complex setting and that the impact of temporal shift on performance is more pronounced on change detection than on semantic segmentation, highlighting that it is a specific issue deserving further attention.
1 code implementation • 13 Mar 2024 • Nicolas Chahine, Sira Ferradans, Jean Ponce
Blind image quality assessment (BIQA) approaches, while promising for automating image quality evaluation, often fall short in real-world scenarios due to their reliance on a generic quality standard applied uniformly across diverse images.
1 code implementation • arXiv preprint 2024 • Adrien Bardes, Quentin Garrido, Jean Ponce, Xinlei Chen, Michael Rabbat, Yann Lecun, Mahmoud Assran, Nicolas Ballas
This paper explores feature prediction as a stand-alone objective for unsupervised learning from video and introduces V-JEPA, a collection of vision models trained solely using a feature prediction objective, without the use of pretrained image encoders, text, negative examples, reconstruction, or other sources of supervision.
1 code implementation • 14 Feb 2024 • Nicolas Chahine, Sira Ferradans, Javier Vazquez-Corral, Jean Ponce
Automated and robust portrait quality assessment (PQA) is of paramount importance in high-impact applications such as smartphone photography.
no code implementations • 8 Dec 2023 • Bruno Lecouat, Yann Dubois de Mont-Marin, Théo Bodrito, Julien Mairal, Jean Ponce
This positions our approach as a versatile tool for various burst image processing applications.
1 code implementation • CVPR 2024 • Guillaume Le Moing, Jean Ponce, Cordelia Schmid
Code, data, and videos showcasing the capabilities of our approach are available in the project webpage: https://16lemoing. github. io/dot .
1 code implementation • 29 Nov 2023 • Alexandre Araujo, Jean Ponce, Julien Mairal
Focus stacking is widely used in micro, macro, and landscape photography to reconstruct all-in-focus images from multiple frames obtained with focus bracketing, that is, with shallow depth of field and different focus planes.
2 code implementations • 3 Aug 2023 • Xinglong Sun, Jean Ponce, Yu-Xiong Wang
Our study reveals that, different from prior work, deformable convolution needs to be applied on an estimated depth map with a relatively high density for better performance.
no code implementations • 24 Jul 2023 • Adrien Bardes, Jean Ponce, Yann Lecun
Self-supervised learning of visual representations has been focusing on learning content features, which do not capture object motion or location, and focus on identifying and differentiating objects in images and videos.
no code implementations • 21 Jun 2023 • Olivier Flasseur, Théo Bodrito, Julien Mairal, Jean Ponce, Maud Langlois, Anne-Marie Lagrange
Exoplanet detection by direct imaging is a difficult task: the faint signals from the objects of interest are buried under a spatially structured nuisance component induced by the host star.
1 code implementation • CVPR 2023 • Nicolas Chahine, Ana-Stefania Calarasanu, Davide Garcia-Civiero, Theo Cayla, Sira Ferradans, Jean Ponce
This costly procedure can be partially replaced by automated learning-based methods for image quality assessment (IQA).
1 code implementation • 22 Mar 2023 • Elliot Vincent, Jean Ponce, Mathieu Aubry
We study different levels of supervision and show this simple and highly interpretable method achieves the best performance in the low data regime and significantly improves the state of the art for unsupervised classification of agricultural time series on four recent SITS datasets.
1 code implementation • ICCV 2023 • Guillaume Le Moing, Jean Ponce, Cordelia Schmid
This paper presents WALDO (WArping Layer-Decomposed Objects), a novel approach to the prediction of future video frames from past ones.
no code implementations • 16 Nov 2022 • Minttu Alakuijala, Gabriel Dulac-Arnold, Julien Mairal, Jean Ponce, Cordelia Schmid
Unlike prior work on leveraging human videos to teach robots, our method, Human Offline Learned Distances (HOLD) requires neither a priori data from the robot environment, nor a set of task-specific human demonstrations, nor a predefined notion of correspondence across morphologies, yet it is able to accelerate training of several manipulation tasks on a simulated robot arm compared to using only a sparse reward obtained from task completion.
3 code implementations • 4 Oct 2022 • Adrien Bardes, Jean Ponce, Yann Lecun
Most recent self-supervised methods for learning image representations focus on either producing a global feature with invariance properties, or producing a set of local features.
no code implementations • 29 Jul 2022 • Bruno Lecouat, Thomas Eboli, Jean Ponce, Julien Mairal
Photographs captured by smartphones and mid-range cameras have limited spatial resolution and dynamic range, with noisy response in underexposed regions and color artefacts in saturated areas.
1 code implementation • 25 Jul 2022 • Huy V. Vo, Oriane Siméoni, Spyros Gidaris, Andrei Bursuc, Patrick Pérez, Jean Ponce
On COCO, using on average 10 fully-annotated images per class, or equivalently 1% of the training set, BiB also reduces the performance gap (in AP) between the weakly-supervised detector and the fully-supervised Fast RCNN by over 70%, showing a good trade-off between performance and data efficiency.
no code implementations • 20 Apr 2022 • Thomas Chabal, Robin Strudel, Etienne Arlaud, Jean Ponce, Cordelia Schmid
This paper addresses the problem of copying an unknown assembly of primitives with known shape and appearance using information extracted from a single photograph by an off-the-shelf procedure for object detection and pose estimation.
1 code implementation • NeurIPS 2021 • Oumayma Bounou, Jean Ponce, Justin Carpentier
Identifying an effective model of a dynamical system from sensory data and using it for future state prediction and control is challenging.
2 code implementations • 29 Sep 2021 • Oriane Siméoni, Gilles Puy, Huy V. Vo, Simon Roburin, Spyros Gidaris, Andrei Bursuc, Patrick Pérez, Renaud Marlet, Jean Ponce
We also show that training a class-agnostic detector on the discovered objects boosts results by another 7 points.
Ranked #4 on
Weakly-Supervised Object Localization
on CUB-200-2011
(Top-1 Localization Accuracy metric)
1 code implementation • NeurIPS 2021 • Guillaume Le Moing, Jean Ponce, Cordelia Schmid
The prediction model is doubly autoregressive, in the latent space of an autoencoder for forecasting, and in image space for updating contextual information, which is also used to enforce spatio-temporal consistency through a learnable optical flow module.
Ranked #8 on
Video Generation
on BAIR Robot Pushing
no code implementations • 15 Jun 2021 • Minttu Alakuijala, Gabriel Dulac-Arnold, Julien Mairal, Jean Ponce, Cordelia Schmid
Residual reinforcement learning (RL) has been proposed as a way to solve challenging robotic tasks by adapting control actions from a conventional feedback controller to maximize a reward signal.
1 code implementation • NeurIPS 2021 • Huy V. Vo, Elena Sizikova, Cordelia Schmid, Patrick Pérez, Jean Ponce
Extensive experiments on COCO and OpenImages show that, in the single-object discovery setting where a single prominent object is sought in each image, the proposed LOD (Large-scale Object Discovery) approach is on par with, or better than the state of the art for medium-scale datasets (up to 120K images), and over 37% better than the only other algorithms capable of scaling up to 1. 7M images.
no code implementations • 7 Jun 2021 • Goutam Bhat, Martin Danelljan, Radu Timofte, Kazutoshi Akita, Wooyeong Cho, Haoqiang Fan, Lanpeng Jia, Daeshik Kim, Bruno Lecouat, Youwei Li, Shuaicheng Liu, Ziluan Liu, Ziwei Luo, Takahiro Maeda, Julien Mairal, Christian Micheloni, Xuan Mo, Takeru Oba, Pavel Ostyakov, Jean Ponce, Sanghyeok Son, Jian Sun, Norimichi Ukita, Rao Muhammad Umer, Youliang Yan, Lei Yu, Magauiya Zhussip, Xueyi Zou
This paper reviews the NTIRE2021 challenge on burst super-resolution.
5 code implementations • NeurIPS 2021 • Adrien Bardes, Jean Ponce, Yann Lecun
Recent self-supervised methods for image representation learning are based on maximizing the agreement between embedding vectors from different views of the same image.
Representation Learning
Self-Supervised Image Classification
+2
1 code implementation • ICCV 2021 • Tom Monnier, Elliot Vincent, Jean Ponce, Mathieu Aubry
We present an unsupervised learning framework for decomposing images into layers of automatically discovered object models.
no code implementations • ICCV 2021 • Bruno Lecouat, Jean Ponce, Julien Mairal
This presentation addresses the problem of reconstructing a high-resolution image from multiple lower-resolution snapshots captured from slightly different viewpoints in space and time.
no code implementations • 13 Apr 2021 • Thomas Eboli, Jian Sun, Jean Ponce
We address the problem of non-blind deblurring and demosaicking of noisy raw images.
1 code implementation • ECCV 2020 • Juhong Min, Jongmin Lee, Jean Ponce, Minsu Cho
Feature representation plays a crucial role in visual correspondence, and recent methods for image matching resort to deeply stacked convolutional layers.
Ranked #2 on
Semantic correspondence
on Caltech-101
1 code implementation • ECCV 2020 • Huy V. Vo, Patrick Pérez, Jean Ponce
This paper addresses the problem of discovering the objects present in a collection of images without any supervision.
Ranked #1 on
Multi-object colocalization
on VOC_all
1 code implementation • ECCV 2020 • Thomas Eboli, Jian Sun, Jean Ponce
Non-blind image deblurring is typically formulated as a linear least-squares problem regularized by natural priors on the corresponding sharp picture's gradients, which can be solved, for example, using a half-quadratic splitting method with Richardson fixed-point iterations for its least-squares updates and a proximal operator for the auxiliary variable updates.
1 code implementation • NeurIPS 2020 • Bruno Lecouat, Jean Ponce, Julien Mairal
We introduce a general framework for designing and training neural network layers whose forward passes can be interpreted as solving non-smooth convex optimization problems, and whose architectures are derived from an optimization algorithm.
no code implementations • 16 Jun 2020 • Thomas Eboli, Alex Nowak-Vila, Jian Sun, Francis Bach, Jean Ponce, Alessandro Rudi
We present a novel approach to image restoration that leverages ideas from localized structured prediction and non-linear multi-task learning.
1 code implementation • ECCV 2020 • Bruno Lecouat, Jean Ponce, Julien Mairal
Non-local self-similarity and sparsity principles have proven to be powerful priors for natural image modeling.
no code implementations • 29 Nov 2019 • Junghyup Lee, Dohyung Kim, Wonkyung Lee, Jean Ponce, Bumsub Ham
We address the problem of semantic correspondence, that is, establishing a dense flow field between images depicting different instances of the same object or scene category.
2 code implementations • 17 Oct 2019 • Beomjun Kim, Jean Ponce, Bumsub Ham
Previous methods based on convolutional neural networks (CNNs) combine nonlinear activations of spatially-invariant kernels to estimate structural details and regress the filtering result.
no code implementations • 28 Aug 2019 • Juhong Min, Jongmin Lee, Jean Ponce, Minsu Cho
In this paper, we present a new large-scale benchmark dataset of semantically paired images, SPair-71k, which contains 70, 958 image pairs with diverse variations in viewpoint and scale.
1 code implementation • ICCV 2019 • Juhong Min, Jongmin Lee, Jean Ponce, Minsu Cho
Establishing visual correspondences under large intra-class variations requires analyzing images at different levels, from features linked to semantics and context to local patterns, while being invariant to instance-specific details.
Ranked #1 on
Semantic correspondence
on Caltech-101
1 code implementation • CVPR 2019 • Huy V. Vo, Francis Bach, Minsu Cho, Kai Han, Yann Lecun, Patrick Perez, Jean Ponce
Learning with complete or partial supervision is powerful but relies on ever-growing human annotation efforts.
Ranked #2 on
Single-object colocalization
on Object Discovery
no code implementations • CVPR 2019 • Junghyup Lee, Dohyung Kim, Jean Ponce, Bumsub Ham
We address the problem of semantic correspondence, that is, establishing a dense flow field between images depicting different instances of the same object or scene category.
no code implementations • 27 Mar 2019 • Beomjun Kim, Jean Ponce, Bumsub Ham
We address the problem of upsampling a low-resolution (LR) depth map using a registered high-resolution (HR) color image of the same scene.
1 code implementation • ECCV 2018 • Matthew Trager, Brian Osserman, Jean Ponce
A set of fundamental matrices relating pairs of cameras in some configuration can be represented as edges of a "viewing graph".
no code implementations • CVPR 2017 • Rafael S. Rezende, Joaquin Zepeda, Jean Ponce, Francis Bach, Patrick Perez
Zepeda and Perez have recently demonstrated the promise of the exemplar SVM (ESVM) as a feature encoder for image retrieval.
1 code implementation • ICCV 2017 • Kai Han, Rafael S. Rezende, Bumsub Ham, Kwan-Yee K. Wong, Minsu Cho, Cordelia Schmid, Jean Ponce
This paper addresses the problem of establishing semantic correspondences between images depicting different instances of the same object or scene category.
no code implementations • 21 Mar 2017 • Bumsub Ham, Minsu Cho, Cordelia Schmid, Jean Ponce
Finding image correspondences remains a challenging problem in the presence of intra-class variations and large changes in scene layout.
no code implementations • CVPR 2017 • Matthew Trager, Bernd Sturmfels, John Canny, Martial Hebert, Jean Ponce
The rational camera model recently introduced in [19] provides a general methodology for studying abstract nonlinear imaging systems and their multi-view geometry.
no code implementations • 21 Aug 2016 • Jean Ponce, Bernd Sturmfels, Matthew Trager
We present a new framework for multi-view geometry in computer vision.
no code implementations • CVPR 2016 • Matthew Trager, Martial Hebert, Jean Ponce
Silhouettes provide rich information on three-dimensional shape, since the intersection of the associated visual cones generates the "visual hull", which encloses and approximates the original shape.
no code implementations • ICCV 2015 • Matthew Trager, Martial Hebert, Jean Ponce
Given multiple perspective photographs, point correspondences form the "joint image", effectively a replica of three dimensional space distributed across its two-dimensional projections.
no code implementations • CVPR 2016 • Bumsub Ham, Minsu Cho, Cordelia Schmid, Jean Ponce
Finding image correspondences remains a challenging problem in the presence of intra-class variations and large changes in scene layout.~Semantic flow methods are designed to handle images depicting different instances of the same object or scene category.
no code implementations • CVPR 2015 • Bumsub Ham, Minsu Cho, Jean Ponce
Regularizing images under a guidance signal has been used in various tasks in computer vision and computational photography, particularly for noise reduction and joint upsampling.
no code implementations • ICCV 2015 • Piotr Bojanowski, Rémi Lajugie, Edouard Grave, Francis Bach, Ivan Laptev, Jean Ponce, Cordelia Schmid
Given vectorial features for both video and text, we propose to cast this task as a temporal assignment problem, with an implicit linear mapping between the two feature modalities.
no code implementations • ICCV 2015 • Suha Kwak, Minsu Cho, Ivan Laptev, Jean Ponce, Cordelia Schmid
This paper addresses the problem of automatically localizing dominant objects as spatio-temporal tubes in a noisy collection of videos with minimal or even no supervision.
no code implementations • CVPR 2015 • Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce
In this paper, we address the problem of estimating and removing non-uniform motion blur from a single blurry image.
no code implementations • CVPR 2015 • Minsu Cho, Suha Kwak, Cordelia Schmid, Jean Ponce
This paper addresses unsupervised discovery and localization of dominant objects from a noisy image collection with multiple object classes.
no code implementations • 12 Nov 2014 • Julien Mairal, Francis Bach, Jean Ponce
In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications.
no code implementations • 4 Jul 2014 • Piotr Bojanowski, Rémi Lajugie, Francis Bach, Ivan Laptev, Jean Ponce, Cordelia Schmid, Josef Sivic
We are given a set of video clips, each one annotated with an {\em ordered} list of actions, such as "walk" then "sit" then "answer phone" extracted from, for example, the associated text script.
no code implementations • CVPR 2014 • Jean Ponce, Martial Hebert
When do the visual rays associated with triplets of point correspondences converge, that is, intersect in a common point?
no code implementations • CVPR 2014 • Minsu Cho, Jian Sun, Olivier Duchenne, Jean Ponce
A major challenge in real-world feature matching problems is to tolerate the numerous outliers arising in typical visual tasks.
no code implementations • CVPR 2013 • Florent Couzinie-Devy, Jian Sun, Karteek Alahari, Jean Ponce
This paper addresses the problem of restoring images subjected to unknown and spatially varying blur caused by defocus or linear (say, horizontal) motion.
no code implementations • NeurIPS 2010 • Armand Joulin, Jean Ponce, Francis R. Bach
To avoid this problem, we introduce a local approximation of this cost function, which leads to a quadratic non-convex optimization problem over a product of simplices.
no code implementations • 27 Sep 2010 • Julien Mairal, Francis Bach, Jean Ponce
Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience and signal processing.
no code implementations • NeurIPS 2008 • Julien Mairal, Jean Ponce, Guillermo Sapiro, Andrew Zisserman, Francis R. Bach
It is now well established that sparse signal models are well suited to restoration tasks and can effectively be learned from audio, image, and video data.