no code implementations • ECCV 2020 • Leonardo Citraro, Mateusz Koziński, Pascal Fua
Existing connectivity-oriented performance measures rank road delineation algorithms inconsistently, which makes it difficult to decide which one is best for a given application.
no code implementations • 27 Mar 2024 • Guoxing Sun, Rishabh Dabral, Pascal Fua, Christian Theobalt, Marc Habermann
Our key idea is to meta-learn the radiance field weights solely from potentially sparse multi-view videos, which can serve as a prior when fine-tuning them on sparse imagery depicting the human.
no code implementations • 26 Mar 2024 • Sihan Shang, Jiancheng Yang, Zhenglong Sun, Pascal Fua
This paper introduces a novel approach, named DataCook, designed to safeguard the copyright of healthcare data during the deployment phase.
no code implementations • 25 Mar 2024 • Nikita Durasov, Doruk Oner, Jonathan Donier, Hieu Le, Pascal Fua
Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance.
1 code implementation • 21 Mar 2024 • Hantao Zhang, Jiancheng Yang, Shouhong Wan, Pascal Fua
By redesigning the diffusion learning objectives to concentrate on lesion areas, it simplifies the model learning process and enhance the controllability of the synthetic output, while preserving background by integrating forward-diffused background contexts into the reverse diffusion process.
no code implementations • 14 Mar 2024 • Andrey Davydov, Martin Engilberge, Mathieu Salzmann, Pascal Fua
Even the best current algorithms for estimating body 3D shape and pose yield results that include body self-intersections.
no code implementations • 16 Feb 2024 • Soumava Kumar Roy, Ilia Badanin, Sina Honari, Pascal Fua
Occlusions remain one of the key challenges in 3D body pose estimation from single-camera video sequences.
no code implementations • 5 Feb 2024 • Andrey Davydov, Alexey Sidnev, Artsiom Sanakoyeu, Yuhua Chen, Mathieu Salzmann, Pascal Fua
When enough annotated training data is available, supervised deep-learning algorithms excel at estimating human body pose and shape using a single camera.
no code implementations • 17 Nov 2023 • Ren Li, Corentin Dumery, Benoît Guillard, Pascal Fua
While modeling people wearing tight-fitting clothing has made great strides in recent years, loose-fitting clothing remains a challenge.
no code implementations • 29 Sep 2023 • Kangxian Xie, Jiancheng Yang, Donglai Wei, Ziqiao Weng, Pascal Fua
Pulmonary diseases rank prominently among the principal causes of death worldwide.
no code implementations • 6 Sep 2023 • Víctor M. Batlle, José M. M. Montiel, Pascal Fua, Juan D. Tardós
It comes from the endoscope's light sources and decays with the inverse of the squared distance to the surface.
1 code implementation • 30 Aug 2023 • Jianning Li, Zongwei Zhou, Jiancheng Yang, Antonio Pepe, Christina Gsaxner, Gijs Luijten, Chongyu Qu, Tiezheng Zhang, Xiaoxi Chen, Wenxuan Li, Marek Wodzinski, Paul Friedrich, Kangxian Xie, Yuan Jin, Narmada Ambigapathy, Enrico Nasca, Naida Solak, Gian Marco Melito, Viet Duc Vu, Afaque R. Memon, Christopher Schlachta, Sandrine de Ribaupierre, Rajnikant Patel, Roy Eagleson, Xiaojun Chen, Heinrich Mächler, Jan Stefan Kirschke, Ezequiel de la Rosa, Patrick Ferdinand Christ, Hongwei Bran Li, David G. Ellis, Michele R. Aizenberg, Sergios Gatidis, Thomas Küstner, Nadya Shusharina, Nicholas Heller, Vincent Andrearczyk, Adrien Depeursinge, Mathieu Hatt, Anjany Sekuboyina, Maximilian Löffler, Hans Liebl, Reuben Dorent, Tom Vercauteren, Jonathan Shapey, Aaron Kujawa, Stefan Cornelissen, Patrick Langenhuizen, Achraf Ben-Hamadou, Ahmed Rekik, Sergi Pujades, Edmond Boyer, Federico Bolelli, Costantino Grana, Luca Lumetti, Hamidreza Salehi, Jun Ma, Yao Zhang, Ramtin Gharleghi, Susann Beier, Arcot Sowmya, Eduardo A. Garza-Villarreal, Thania Balducci, Diego Angeles-Valdez, Roberto Souza, Leticia Rittner, Richard Frayne, Yuanfeng Ji, Vincenzo Ferrari, Soumick Chatterjee, Florian Dubost, Stefanie Schreiber, Hendrik Mattern, Oliver Speck, Daniel Haehn, Christoph John, Andreas Nürnberger, João Pedrosa, Carlos Ferreira, Guilherme Aresta, António Cunha, Aurélio Campilho, Yannick Suter, Jose Garcia, Alain Lalande, Vicky Vandenbossche, Aline Van Oevelen, Kate Duquesne, Hamza Mekhzoum, Jef Vandemeulebroucke, Emmanuel Audenaert, Claudia Krebs, Timo Van Leeuwen, Evie Vereecke, Hauke Heidemeyer, Rainer Röhrig, Frank Hölzle, Vahid Badeli, Kathrin Krieger, Matthias Gunzer, Jianxu Chen, Timo van Meegdenburg, Amin Dada, Miriam Balzer, Jana Fragemann, Frederic Jonske, Moritz Rempe, Stanislav Malorodov, Fin H. Bahnsen, Constantin Seibold, Alexander Jaus, Zdravko Marinov, Paul F. Jaeger, Rainer Stiefelhagen, Ana Sofia Santos, Mariana Lindo, André Ferreira, Victor Alves, Michael Kamp, Amr Abourayya, Felix Nensa, Fabian Hörst, Alexander Brehmer, Lukas Heine, Yannik Hanusrichter, Martin Weßling, Marcel Dudda, Lars E. Podleska, Matthias A. Fink, Julius Keyl, Konstantinos Tserpes, Moon-Sung Kim, Shireen Elhabian, Hans Lamecker, Dženan Zukić, Beatriz Paniagua, Christian Wachinger, Martin Urschler, Luc Duong, Jakob Wasserthal, Peter F. Hoyer, Oliver Basu, Thomas Maal, Max J. H. Witjes, Gregor Schiele, Ti-chiun Chang, Seyed-Ahmad Ahmadi, Ping Luo, Bjoern Menze, Mauricio Reyes, Thomas M. Deserno, Christos Davatzikos, Behrus Puladi, Pascal Fua, Alan L. Yuille, Jens Kleesiek, Jan Egger
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
no code implementations • ICCV 2023 • Javier Rodríguez-Puigvert, Víctor M. Batlle, J. M. M. Montiel, Ruben Martinez-Cantin, Pascal Fua, Juan D. Tardós, Javier Civera
However, there are scenarios, especially in medicine in the case of endoscopies, where such data cannot be obtained.
no code implementations • 16 Jul 2023 • Hieu Le, Nicolas Talabot, Jiancheng Yang, Pascal Fua
Further, we show that our proposed method can be used to simulate various ways a hand can interact with an arbitrary object.
1 code implementation • 3 May 2023 • Zhen Wei, Pascal Fua, Michaël Bauerheim
The Latent Space Model (LSM) learns a low-dimensional latent representation of an object from a dataset of various geometries, while the Direct Mapping Model (DMM) builds parameterization on the fly using only one geometry of interest.
no code implementations • ICCV 2023 • Michał J. Tyszkiewicz, Pascal Fua, Eduard Trulls
Diffusion models generating images conditionally on text, such as Dall-E 2 and Stable Diffusion, have recently made a splash far beyond the computer vision community.
no code implementations • 29 Dec 2022 • Krzysztof Lis, Matthias Rottmann, Sina Honari, Pascal Fua, Mathieu Salzmann
In other words, vision transformers trained to segment a fixed set of object classes generalize to objects well beyond this set.
no code implementations • 23 Nov 2022 • Sina Honari, Chen Zhao, Mathieu Salzmann, Pascal Fua
Analyzing and training 3D body posture models depend heavily on the availability of joint labels that are commonly acquired through laborious manual annotation of body joints or via marker-based joint localization using carefully curated markers and capturing systems.
1 code implementation • CVPR 2023 • Luca De Luigi, Ren Li, Benoît Guillard, Mathieu Salzmann, Pascal Fua
Recent approaches to drape garments quickly over arbitrary human bodies leverage self-supervision to eliminate the need for large training sets.
no code implementations • 21 Nov 2022 • Nikita Durasov, Nik Dorndorf, Pascal Fua
Active Learning (AL) can be used to reduce this burden.
no code implementations • 21 Nov 2022 • Nikita Durasov, Nik Dorndorf, Hieu Le, Pascal Fua
Sampling-free approaches can be faster but suffer from other drawbacks, such as lower reliability of uncertainty estimates, difficulty of use, and limited applicability to different types of tasks and data.
no code implementations • 27 Oct 2022 • Edith Tretschk, Navami Kairanda, Mallikarjun B R, Rishabh Dabral, Adam Kortylewski, Bernhard Egger, Marc Habermann, Pascal Fua, Christian Theobalt, Vladislav Golyanik
3D reconstruction of deformable (or non-rigid) scenes from a set of monocular 2D image observations is a long-standing and actively researched area of computer vision and graphics.
1 code implementation • 19 Oct 2022 • Martin Engilberge, Haixin Shi, Zhiye Wang, Pascal Fua
Data augmentation has proven its usefulness to improve model generalization and performance.
1 code implementation • 19 Oct 2022 • Martin Engilberge, Weizhe Liu, Pascal Fua
Multi-view approaches to people-tracking have the potential to better handle occlusions than single-view ones in crowded scenes.
Ranked #2 on Multi-Object Tracking on Wildtrack
1 code implementation • 4 Oct 2022 • Krzysztof Lis, Sina Honari, Pascal Fua, Mathieu Salzmann
While road obstacle detection techniques have become increasingly effective, they typically ignore the fact that, in practice, the apparent size of the obstacles decreases as their distance to the vehicle increases.
1 code implementation • 22 Sep 2022 • Ren Li, Benoît Guillard, Edoardo Remelli, Pascal Fua
Existing data-driven methods for draping garments over human bodies, despite being effective, cannot handle garments of arbitrary topology and are typically not end-to-end differentiable.
no code implementations • 22 Sep 2022 • Benoit Guillard, Sai Vemprala, Jayesh K. Gupta, Ondrej Miksik, Vibhav Vineet, Pascal Fua, Ashish Kapoor
Simulating realistic sensors is a challenging part in data generation for autonomous systems, often involving carefully handcrafted sensor design, scene properties, and physics modeling.
1 code implementation • 5 Aug 2022 • Ziyi Zhao, Sena Kiciroglu, Hugues Vinzant, Yuan Cheng, Isinsu Katircioglu, Mathieu Salzmann, Pascal Fua
To evaluate our approach, we introduce a dataset with 3 different physical exercises.
1 code implementation • 14 Jul 2022 • Doruk Oner, Hussein Osman, Mateusz Kozinski, Pascal Fua
Many biological and medical tasks require the delineation of 3D curvilinear structures such as blood vessels and neurites from image volumes.
1 code implementation • 30 Jun 2022 • Jiancheng Yang, Rui Shi, Udaranga Wickramasinghe, Qikui Zhu, Bingbing Ni, Pascal Fua
Besides, we develop a new Adrenal gLand ANalysis (ALAN) dataset with the proposed NeAR, where each case consists of a 3D shape of adrenal gland and its diagnosis label (normal vs. abnormal) assigned by experts.
no code implementations • 21 Jun 2022 • Patrick M. Jensen, Udaranga Wickramasinghe, Anders B. Dahl, Pascal Fua, Vedrana A. Dahl
During training the latent vectors are constrained to have the same value, which avoids overfitting.
no code implementations • 29 Mar 2022 • Soumava Kumar Roy, Leonardo Citraro, Sina Honari, Pascal Fua
Supervised approaches to 3D pose estimation from single images are remarkably effective when labeled data is abundant.
3D Pose Estimation Weakly-supervised 3D Human Pose Estimation +1
1 code implementation • 18 Mar 2022 • Yinlin Hu, Pascal Fua, Mathieu Salzmann
Given a rough pose estimate obtained from a first network, it uses a second network to predict a dense 2D correspondence field between the image rendered using the rough pose and the real image and infers the required pose correction.
no code implementations • CVPR 2022 • Jiancheng Yang, Udaranga Wickramasinghe, Bingbing Ni, Pascal Fua
Deep implicit shape models have become popular in the computer vision community at large but less so for biomedical applications.
no code implementations • CVPR 2022 • Andrey Davydov, Anastasia Remizova, Victor Constantin, Sina Honari, Mathieu Salzmann, Pascal Fua
The Skinned Multi-Person Linear (SMPL) model can represent a human body by mapping pose and shape parameters to body meshes.
1 code implementation • 6 Dec 2021 • Doruk Oner, Leonardo Citraro, Mateusz Koziński, Pascal Fua
Deep learning-based approaches to delineating 3D structure depend on accurate annotations to train the networks.
no code implementations • 2 Dec 2021 • Semih Günel, Florian Aymanns, Sina Honari, Pavan Ramdya, Pascal Fua
Relating animal behaviors to brain activity is a fundamental goal in neuroscience, with practical applications in building robust brain-machine interfaces.
no code implementations • 1 Dec 2021 • Isinsu Katircioglu, Costa Georgantas, Mathieu Salzmann, Pascal Fua
To evaluate this, and because no existing motion prediction datasets depict two closely-interacting subjects, we introduce the LindyHop600K dance dataset.
1 code implementation • 29 Nov 2021 • Benoit Guillard, Federico Stella, Pascal Fua
Unsigned Distance Fields (UDFs) can be used to represent non-watertight surfaces.
no code implementations • 29 Nov 2021 • Semih Günel, Florian Aymanns, Sina Honari, Pavan Ramdya, Pascal Fua
A fundamental goal in neuroscience is to understand the relationship between neural activity and behavior.
no code implementations • CVPR 2022 • Weizhe Liu, Bugra Tekin, Huseyin Coskun, Vibhav Vineet, Pascal Fua, Marc Pollefeys
To this end, we propose an approach to enforce temporal priors on the optimal transport matrix, which leverages temporal consistency, while allowing for variations in the order of actions.
1 code implementation • 12 Nov 2021 • Jan Bednarik, Noam Aigerman, Vladimir G. Kim, Siddhartha Chaudhuri, Shaifali Parashar, Mathieu Salzmann, Pascal Fua
The key to making these correspondences semantically meaningful is to guarantee that the metric tensors computed at corresponding points are as similar as possible.
Ranked #1 on Surface Reconstruction on ANIM
no code implementations • 14 Oct 2021 • Okan Altingövde, Anastasiia Mishchuk, Gulnaz Ganeeva, Emad Oveisi, Cecile Hebert, Pascal Fua
Curvilinear structures frequently appear in microscopy imaging as the object of interest.
no code implementations • 12 Oct 2021 • Doruk Oner, Adélie Garin, Mateusz Koziński, Kathryn Hess, Pascal Fua
Persistent Homology (PH) has been successfully used to train networks to detect curvilinear structures and to improve the topological quality of their results.
no code implementations • 29 Sep 2021 • Doruk Oner, Adélie Garin, Mateusz Kozinski, Kathryn Hess, Pascal Fua
Persistent Homologies have been successfully used to increase the performance of deep networks trained to detect curvilinear structures and to improve the topological quality of the results.
no code implementations • 28 Sep 2021 • Nikita Durasov, Artem Lukoyanov, Jonathan Donier, Pascal Fua
Graph Neural Networks (GNNs) can predict the performance of an industrial design quickly and accurately and be used to optimize its shape effectively.
no code implementations • 22 Sep 2021 • Subeesh Vasu, Nicolas Talabot, Artem Lukoianov, Pierre Baqué, Jonathan Donier, Pascal Fua
Deep implicit surfaces excel at modeling generic shapes but do not always capture the regularities present in manufactured objects, which is something simple geometric primitives are particularly good at.
no code implementations • 20 Jun 2021 • Benoit Guillard, Edoardo Remelli, Artem Lukoianov, Stephan R. Richter, Timur Bagautdinov, Pierre Baque, Pascal Fua
Our key insight is that by reasoning on how implicit field perturbations impact local surface geometry, one can ultimately differentiate the 3D location of surface samples with respect to the underlying deep implicit field.
no code implementations • 7 Jun 2021 • Udaranga Wickramasinghe, Patrick M. Jensen, Mian Shah, Jiancheng Yang, Pascal Fua
There are many approaches to weakly-supervised training of networks to segment 2D images.
2 code implementations • 30 Apr 2021 • Robin Chan, Krzysztof Lis, Svenja Uhlemeyer, Hermann Blum, Sina Honari, Roland Siegwart, Pascal Fua, Mathieu Salzmann, Matthias Rottmann
State-of-the-art semantic or instance segmentation deep neural networks (DNNs) are usually trained on a closed set of semantic classes.
no code implementations • 22 Apr 2021 • Weizhe Liu, David Ferstl, Samuel Schulter, Lukas Zebedin, Pascal Fua, Christian Leistner
We introduce a novel approach to unsupervised and semi-supervised domain adaptation for semantic segmentation.
1 code implementation • ICCV 2021 • Jan Bednarik, Vladimir G. Kim, Siddhartha Chaudhuri, Shaifali Parashar, Mathieu Salzmann, Pascal Fua, Noam Aigerman
We propose a method for the unsupervised reconstruction of a temporally-coherent sequence of surfaces from a sequence of time-evolving point clouds, yielding dense, semantically meaningful correspondences between all keyframes.
2 code implementations • 8 Apr 2021 • Wei Wang, Zheng Dang, Yinlin Hu, Pascal Fua, Mathieu Salzmann
Eigendecomposition of symmetric matrices is at the heart of many computer vision algorithms.
2 code implementations • CVPR 2021 • Yinlin Hu, Sebastien Speierer, Wenzel Jakob, Pascal Fua, Mathieu Salzmann
6D pose estimation in space poses unique challenges that are not commonly encountered in the terrestrial setting.
no code implementations • ICCV 2021 • Benoit Guillard, Edoardo Remelli, Pierre Yvernay, Pascal Fua
Reconstructing 3D shape from 2D sketches has long been an open problem because the sketches only provide very sparse and ambiguous information.
1 code implementation • CVPR 2022 • Weizhe Liu, Nikita Durasov, Pascal Fua
State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density.
1 code implementation • 22 Mar 2021 • David Honzátko, Engin Türetken, Pascal Fua, L. Andrea Dunbar
The problem of estimating a surface shape from its observed reflectance properties still remains a challenging task in computer vision.
no code implementations • 25 Dec 2020 • Krzysztof Lis, Sina Honari, Pascal Fua, Mathieu Salzmann
Vehicles can encounter a myriad of obstacles on the road, and it is impossible to record them all beforehand to train a detector.
no code implementations • 21 Dec 2020 • Mengshi Qi, Edoardo Remelli, Mathieu Salzmann, Pascal Fua
Deep learning-solutions for hand-object 3D pose and shape estimation are now very effective when an annotated dataset is available to train them to handle the scenarios and lighting conditions they will encounter at test time.
Generative Adversarial Network Unsupervised Domain Adaptation
3 code implementations • CVPR 2021 • Nikita Durasov, Timur Bagautdinov, Pierre Baque, Pascal Fua
Our central intuition is that there is a continuous spectrum of ensemble-like models of which MC-Dropout and Deep Ensembles are extreme examples.
1 code implementation • ICCV 2021 • Isinsu Katircioglu, Helge Rhodin, Jörg Spörri, Mathieu Salzmann, Pascal Fua
Self-supervised detection and segmentation of foreground objects aims for accuracy without annotated training data.
1 code implementation • 8 Dec 2020 • Sena Kiciroglu, Wei Wang, Mathieu Salzmann, Pascal Fua
Long term human motion prediction is essential in safety-critical applications such as human-robot interaction and autonomous driving.
no code implementations • 2 Dec 2020 • Sina Honari, Victor Constantin, Helge Rhodin, Mathieu Salzmann, Pascal Fua
In this paper we propose an unsupervised feature extraction method to capture temporal information on monocular videos, where we detect and encode subject of interest in each frame and leverage contrastive self-supervised (CSS) learning to extract rich latent vectors.
1 code implementation • 1 Dec 2020 • Weizhe Liu, Mathieu Salzmann, Pascal Fua
Modern methods for counting people in crowded scenes rely on deep networks to estimate people densities in individual images.
1 code implementation • CVPR 2021 • Frank Yu, Mathieu Salzmann, Pascal Fua, Helge Rhodin
Our conclusion is that it is important to utilize camera calibration information when available, for classical and deep-learning-based computer vision alike.
no code implementations • 23 Nov 2020 • Shaifali Parashar, Yuxuan Long, Mathieu Salzmann, Pascal Fua
A recent trend in Non-Rigid Structure-from-Motion (NRSfM) is to express local, differential constraints between pairs of images, from which the surface normal at any point can be obtained by solving a system of polynomial equations.
no code implementations • CVPR 2021 • Udaranga Wickramasinghe, Graham Knott, Pascal Fua
Active Surface Models have a long history of being useful to model complex 3D surfaces but only Active Contours have been used in conjunction with deep networks, and then only to produce the data term as well as meta-parameter maps controlling them.
no code implementations • 11 Nov 2020 • Isinsu Katircioglu, Helge Rhodin, Victor Constantin, Jörg Spörri, Mathieu Salzmann, Pascal Fua
While supervised object detection and segmentation methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on.
1 code implementation • 14 Oct 2020 • Zhantao Deng, Jan Bednařík, Mathieu Salzmann, Pascal Fua
We introduce an approach that explicitly encourages global consistency of the local mappings.
1 code implementation • 6 Oct 2020 • Tim Lebailly, Sena Kiciroglu, Mathieu Salzmann, Pascal Fua, Wei Wang
We argue that the diverse temporal scales are important as they allow us to look at the past frames with different receptive fields, which can lead to better predictions.
1 code implementation • 15 Sep 2020 • Doruk Oner, Mateusz Koziński, Leonardo Citraro, Nathan C. Dadap, Alexandra G. Konings, Pascal Fua
The main idea behind our loss is to express the connectivity of roads, or canals, in terms of disconnections that they create between background regions of the image.
no code implementations • 20 Jul 2020 • Erhan Gundogdu, Victor Constantin, Shaifali Parashar, Amrollah Seifoddini, Minh Dang, Mathieu Salzmann, Pascal Fua
We introduce a two-stream deep network model that produces a visually plausible draping of a template cloth on virtual 3D bodies by extracting features from both the body and garment shapes.
no code implementations • ECCV 2020 • Subeesh Vasu, Mateusz Kozinski, Leonardo Citraro, Pascal Fua
Instead, we use a more sophisticated discriminator that returns a label pyramid describing what portions of the road network are correct at several different scales.
3 code implementations • NeurIPS 2020 • Michał J. Tyszkiewicz, Pascal Fua, Eduard Trulls
Local feature frameworks are difficult to learn in an end-to-end fashion, due to the discreteness inherent to the selection and matching of sparse keypoints.
Ranked #2 on Image Matching on IMC PhotoTourism (using extra training data)
1 code implementation • NeurIPS 2020 • Edoardo Remelli, Artem Lukoianov, Stephan R. Richter, Benoît Guillard, Timur Bagautdinov, Pierre Baque, Pascal Fua
Unfortunately, these methods are often not suitable for applications that require an explicit mesh-based surface representation because converting an implicit field to such a representation relies on the Marching Cubes algorithm, which cannot be differentiated with respect to the underlying implicit field.
no code implementations • NeurIPS 2020 • Benoit Guillard, Edoardo Remelli, Pascal Fua
Most state-of-the-art deep geometric learning single-view reconstruction approaches rely on encoder-decoder architectures that output either shape parametrizations or implicit representations.
no code implementations • ICLR 2020 • Róger Bermúdez-Chacón, Mathieu Salzmann, Pascal Fua
We tackle unsupervised domain adaptation by accounting for the fact that different domains may need to be processed differently to arrive to a common feature representation effective for recognition.
no code implementations • 15 Apr 2020 • Zheng Dang, Kwang Moo Yi, Yinlin Hu, Fei Wang, Pascal Fua, Mathieu Salzmann
In this paper, we introduce an eigendecomposition-free approach to training a deep network whose loss depends on the eigenvector corresponding to a zero eigenvalue of a matrix predicted by the network.
no code implementations • CVPR 2020 • Edoardo Remelli, Shangchen Han, Sina Honari, Pascal Fua, Robert Wang
We present a lightweight solution to recover 3D pose from multi-view images captured with spatially calibrated cameras.
Ranked #4 on 3D Human Pose Estimation on Total Capture
no code implementations • 31 Mar 2020 • Leonardo Citraro, Pablo Márquez-Neila, Stefano Savarè, Vivek Jayaram, Charles Dubout, Félix Renaut, Andrés Hasfura, Horesh Ben Shitrit, Pascal Fua
Given an image sequence featuring a portion of a sports field filmed by a moving and uncalibrated camera, such as the one of the smartphones, our goal is to compute automatically in real time the focal length and extrinsic camera parameters for each image in the sequence without using a priori knowledges of the position and orientation of the camera.
5 code implementations • 3 Mar 2020 • Yuhe Jin, Dmytro Mishkin, Anastasiia Mishchuk, Jiri Matas, Pascal Fua, Kwang Moo Yi, Eduard Trulls
We introduce a comprehensive benchmark for local features and robust estimation algorithms, focusing on the downstream task -- the accuracy of the reconstructed camera pose -- as our primary metric.
no code implementations • CVPR 2020 • Siyuan Li, Semih Günel, Mirela Ostrek, Pavan Ramdya, Pascal Fua, Helge Rhodin
We compare our approach with existing domain transfer methods and demonstrate improved pose estimation accuracy on Drosophila melanogaster (fruit fly), Caenorhabditis elegans (worm) and Danio rerio (zebrafish), without requiring any manual annotation on the target domain and despite using simplistic off-the-shelf animal characters for simulation, or simple geometric shapes as models.
1 code implementation • CVPR 2020 • Sena Kiciroglu, Helge Rhodin, Sudipta N. Sinha, Mathieu Salzmann, Pascal Fua
The accuracy of monocular 3D human pose estimation depends on the viewpoint from which the image is captured.
1 code implementation • 8 Dec 2019 • Udaranga Wickramasinghe, Edoardo Remelli, Graham Knott, Pascal Fua
CNN-based volumetric methods that label individual voxels now dominate the field of biomedical segmentation.
no code implementations • 28 Nov 2019 • Leonardo Citraro, Mateusz Koziński, Pascal Fua
Existing performance measures rank delineation algorithms inconsistently, which makes it difficult to decide which one is best in any given situation.
no code implementations • 26 Nov 2019 • Weizhe Liu, Mathieu Salzmann, Pascal Fua
State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density.
1 code implementation • CVPR 2020 • Jan Bednarik, Shaifali Parashar, Erhan Gundogdu, Mathieu Salzmann, Pascal Fua
Generative models that produce point clouds have emerged as a powerful tool to represent 3D surfaces, and the best current ones rely on learning an ensemble of parametric representations.
1 code implementation • ECCV 2020 • Weizhe Liu, Mathieu Salzmann, Pascal Fua
In this paper, we advocate estimating people flows across image locations between consecutive images and inferring the people densities from these flows instead of directly regressing.
1 code implementation • CVPR 2020 • Yinlin Hu, Pascal Fua, Wei Wang, Mathieu Salzmann
Second, training the deep network relies on a surrogate loss that does not directly reflect the final 6D pose estimation task.
1 code implementation • 18 Sep 2019 • Udaranga Wickramasinghe, Graham Knott, Pascal Fua
Probabilistic atlases (PAs) have long been used in standard segmentation approaches and, more recently, in conjunction with Convolutional Neural Networks (CNNs).
no code implementations • ICCV 2019 • Didier Bieler, Semih Günel, Pascal Fua, Helge Rhodin
We show theoretically and empirically that a simple motion trajectory analysis suffices to translate from pixel measurements to the person's metric height, reaching a MAE of up to 3. 9 cm on jumping motions, and that this works without camera and ground plane calibration.
no code implementations • 30 Aug 2019 • Roman Bachmann, Jörg Spörri, Pascal Fua, Helge Rhodin
We propose a method for estimating an athlete's global 3D position and articulated pose using multiple cameras.
1 code implementation • ICCV 2019 • Patrick Ebel, Anastasiia Mishchuk, Kwang Moo Yi, Pascal Fua, Eduard Trulls
We demonstrate that this representation is particularly amenable to learning descriptors with deep networks.
no code implementations • 18 Jul 2019 • Isinsu Katircioglu, Helge Rhodin, Victor Constantin, Jörg Spörri, Mathieu Salzmann, Pascal Fua
While supervised object detection methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on.
4 code implementations • 1 Jul 2019 • Dushyant Mehta, Oleksandr Sotnychenko, Franziska Mueller, Weipeng Xu, Mohamed Elgharib, Pascal Fua, Hans-Peter Seidel, Helge Rhodin, Gerard Pons-Moll, Christian Theobalt
The first stage is a convolutional neural network (CNN) that estimates 2D and 3D pose features along with identity assignments for all visible joints of all individuals. We contribute a new architecture for this CNN, called SelecSLS Net, that uses novel selective long and short range skip connections to improve the information flow allowing for a drastically faster network without compromising accuracy.
Ranked #7 on 3D Multi-Person Pose Estimation on MuPoTS-3D
3D Multi-Person Human Pose Estimation 3D Multi-Person Pose Estimation +1
2 code implementations • NeurIPS 2019 • Wei Wang, Zheng Dang, Yinlin Hu, Pascal Fua, Mathieu Salzmann
Eigendecomposition (ED) is widely used in deep networks.
no code implementations • ICCV 2019 • Wei Wang, Kaicheng Yu, Joachim Hugonot, Pascal Fua, Mathieu Salzmann
State-of-the-art segmentation methods rely on very deep networks that are not always easy to train without very large training datasets and tend to be relatively slow to run on standard GPUs.
no code implementations • 9 May 2019 • Agata Mosinska, Mateusz Kozinski, Pascal Fua
Detection of curvilinear structures in images has long been of interest.
3 code implementations • ICCV 2019 • Krzysztof Lis, Krishna Nakka, Pascal Fua, Mathieu Salzmann
In this paper, we tackle the more realistic scenario where unexpected objects of unknown classes can appear at test time.
1 code implementation • CVPR 2019 • Helge Rhodin, Victor Constantin, Isinsu Katircioglu, Mathieu Salzmann, Pascal Fua
To this end, we introduce a self-supervised approach to learning what we call a neural scene decomposition (NSD) that can be exploited for 3D pose estimation.
no code implementations • 27 Jan 2019 • Edoardo Remelli, Pierre Baque, Pascal Fua
Most algorithms that rely on deep learning-based approaches to generate 3D point sets can only produce clouds containing fixed number of points.
5 code implementations • CVPR 2019 • Yinlin Hu, Joachim Hugonot, Pascal Fua, Mathieu Salzmann
The most recent trend in estimating the 6D pose of rigid objects has been to train deep networks to either directly regress the pose from the image or to predict the 2D locations of 3D keypoints, from which the pose can be obtained using a PnP algorithm.
Ranked #4 on 6D Pose Estimation using RGB on YCB-Video
no code implementations • ICCV 2019 • Erhan Gundogdu, Victor Constantin, Amrollah Seifoddini, Minh Dang, Mathieu Salzmann, Pascal Fua
We fuse these features with those extracted in parallel from the 3D body, so as to model the cloth-body interactions.
no code implementations • 27 Nov 2018 • Wei Wang, Kaicheng Yu, Joachim Hugonot, Pascal Fua, Mathieu Salzmann
As evidenced by our results on standard hand segmentation benchmarks and on our own dataset, our approach outperforms these other, simpler recurrent segmentation techniques, as well as the state-of-the-art hand segmentation one.
no code implementations • 27 Nov 2018 • Andrii Maksai, Pascal Fua
Identity Switching remains one of the main difficulties Multiple Object Tracking (MOT) algorithms have to deal with.
3 code implementations • CVPR 2019 • Weizhe Liu, Mathieu Salzmann, Pascal Fua
State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density.
Ranked #1 on Crowd Counting on Venice
1 code implementation • 26 Nov 2018 • Mateusz Koziński, Agata Mosinska, Mathieu Salzmann, Pascal Fua
The difficulty of obtaining annotations to build training databases still slows down the adoption of recent deep learning approaches for biomedical image analysis.
1 code implementation • ICLR 2019 • Ksenia Konyushkova, Raphael Sznitman, Pascal Fua
We propose a general-purpose approach to discovering active learning (AL) strategies from data.
no code implementations • CVPR 2018 • Tatjana Chavdarova, Pierre Baqué, Stéphane Bouquet, Andrii Maksai, Cijo Jose, Timur Bagautdinov, Louis Lettry, Pascal Fua, Luc van Gool, François Fleuret
People detection methods are highly sensitive to occlusions between pedestrians, which are extremely frequent in many situations where cameras have to be mounted at a limited height.
no code implementations • CVPR 2018 • Timur Bagautdinov, Chenglei Wu, Jason Saragih, Pascal Fua, Yaser Sheikh
We propose a method for learning non-linear face geometry representations using deep generative models.
no code implementations • 25 May 2018 • Semih Günel, Helge Rhodin, Pascal Fua
Recovering a person's height from a single image is important for virtual garment fitting, autonomous driving and surveillance, however, it is also very challenging due to the absence of absolute scale information.
4 code implementations • NeurIPS 2018 • Yuki Ono, Eduard Trulls, Pascal Fua, Kwang Moo Yi
We present a novel deep architecture and a training strategy to learn a local feature pipeline from scratch, using collections of images without the need for human supervision.
no code implementations • ECCV 2018 • Xiaoqing Yin, Xinchao Wang, Jun Yu, Maojun Zhang, Pascal Fua, DaCheng Tao
Images captured by fisheye lenses violate the pinhole camera assumption and suffer from distortions.
2 code implementations • ECCV 2018 • Helge Rhodin, Mathieu Salzmann, Pascal Fua
In this paper, we propose to overcome this problem by learning a geometry-aware body representation from multi-view images without annotations.
Ranked #29 on Weakly-supervised 3D Human Pose Estimation on Human3.6M
no code implementations • 23 Mar 2018 • Weizhe Liu, Krzysztof Lis, Mathieu Salzmann, Pascal Fua
In this paper, we explicitly model the scale changes and reason in terms of people per square-meter.
1 code implementation • 23 Mar 2018 • Jan Bednařík, Pascal Fua, Mathieu Salzmann
Recent years have seen the development of mature solutions for reconstructing deformable surfaces from a single image, provided that they are relatively well-textured.
no code implementations • ECCV 2018 • Zheng Dang, Kwang Moo Yi, Yinlin Hu, Fei Wang, Pascal Fua, Mathieu Salzmann
Many classical Computer Vision problems, such as essential matrix computation and pose estimation from 3D to 2D correspondences, can be solved by finding the eigenvector corresponding to the smallest, or zero, eigenvalue of a matrix representing a linear system.
no code implementations • 15 Mar 2018 • Weipeng Xu, Avishek Chatterjee, Michael Zollhoefer, Helge Rhodin, Pascal Fua, Hans-Peter Seidel, Christian Theobalt
We tackle these challenges based on a novel lightweight setup that converts a standard baseball cap to a device for high-quality pose estimation based on a single cap-mounted fisheye camera.
Ranked #6 on Egocentric Pose Estimation on GlobalEgoMocap Test Dataset (using extra training data)
no code implementations • CVPR 2018 • Helge Rhodin, Jörg Spörri, Isinsu Katircioglu, Victor Constantin, Frédéric Meyer, Erich Müller, Mathieu Salzmann, Pascal Fua
Accurate 3D human pose estimation from single images is possible with sophisticated deep-net architectures that have been trained on very large datasets.
no code implementations • CVPR 2018 • Wei Wang, Xavier Alameda-Pineda, Dan Xu, Pascal Fua, Elisa Ricci, Nicu Sebe
Finally, these landmark sequences are translated into face videos.
no code implementations • 14 Dec 2017 • Bin Fan, Qingqun Kong, Xinchao Wang, Zhiheng Wang, Shiming Xiang, Chunhong Pan, Pascal Fua
To obtain a comprehensive evaluation, we choose to include both float type features and binary ones.
no code implementations • CVPR 2018 • Agata Mosinska, Pablo Marquez-Neila, Mateusz Kozinski, Pascal Fua
We propose a new loss term that is aware of the higher-order topological features of linear structures.
5 code implementations • CVPR 2018 • Bugra Tekin, Sudipta N. Sinha, Pascal Fua
For single object and multiple object pose estimation on the LINEMOD and OCCLUSION datasets, our approach substantially outperforms other recent CNN-based approaches when they are all used without post-processing.
Ranked #1 on 6D Pose Estimation using RGB on OCCLUSION
no code implementations • CVPR 2018 • Artem Rozantsev, Mathieu Salzmann, Pascal Fua
The goal of Deep Domain Adaptation is to make it possible to use Deep Nets trained in one domain where there is enough annotated training data in another where there is little or none.
3 code implementations • CVPR 2018 • Kwang Moo Yi, Eduard Trulls, Yuki Ono, Vincent Lepetit, Mathieu Salzmann, Pascal Fua
We develop a deep architecture to learn to find good correspondences for wide-baseline stereo.
1 code implementation • 18 Oct 2017 • Thomas Kurmann, Pablo Marquez Neila, Xiaofei Du, Pascal Fua, Danail Stoyanov, Sebastian Wolf, Raphael Sznitman
An additional advantage of our approach is that instrument detection at test time is achieved while avoiding the need for scale-dependent sliding window evaluation.
no code implementations • ICCV 2017 • Andrii Maksai, Xinchao Wang, Francois Fleuret, Pascal Fua
Many state-of-the-art approaches to multi-object tracking rely on detecting them in each frame independently, grouping detections into short but reliable trajectory segments, and then further grouping them into full trajectories.
no code implementations • 28 Jul 2017 • Tatjana Chavdarova, Pierre Baqué, Stéphane Bouquet, Andrii Maksai, Cijo Jose, Louis Lettry, Pascal Fua, Luc van Gool, François Fleuret
People detection methods are highly sensitive to the perpetual occlusions among the targets.
no code implementations • 7 Jun 2017 • Pablo Márquez-Neila, Mathieu Salzmann, Pascal Fua
Imposing constraints on the output of a Deep Neural Net is one way to improve the quality of its predictions while loosening the requirements for labeled training data.
2 code implementations • ICCV 2017 • Pierre Baqué, François Fleuret, Pascal Fua
People detection in single 2D images has improved greatly in recent years.
Ranked #8 on Multiview Detection on MultiviewX
1 code implementation • NeurIPS 2017 • Ksenia Konyushkova, Raphael Sznitman, Pascal Fua
In this paper, we suggest a novel data-driven approach to active learning (AL).
no code implementations • 23 Dec 2016 • Agata Mosinska, Jakub Tarnawski, Pascal Fua
In a proofreading context, we similarly find regions of the resulting reconstruction that should be verified in priority to obtain a nearly-perfect result.
1 code implementation • 2 Dec 2016 • Andrii Maksai, Xinchao Wang, Francois Fleuret, Pascal Fua
Many state-of-the-art approaches to people tracking rely on detecting them in each frame independently, grouping detections into short but reliable trajectory segments, and then further grouping them into full trajectories.
no code implementations • CVPR 2017 • Artem Rozantsev, Sudipta N. Sinha, Debadeepta Dey, Pascal Fua
Our main contribution is a new bundle adjustment procedure which in addition to optimizing the camera poses, regularizes the point trajectory using a prior based on motion dynamics (or specifically flight dynamics).
no code implementations • 29 Nov 2016 • Dushyant Mehta, Helge Rhodin, Dan Casas, Pascal Fua, Oleksandr Sotnychenko, Weipeng Xu, Christian Theobalt
We propose a CNN-based approach for 3D human body pose estimation from single RGB images that addresses the issue of limited generalizability of models trained solely on the starkly limited publicly available 3D pose data.
Ranked #17 on Pose Estimation on Leeds Sports Poses
no code implementations • CVPR 2017 • Timur Bagautdinov, Alexandre Alahi, François Fleuret, Pascal Fua, Silvio Savarese
We present a unified framework for understanding human social behaviors in raw image sequences.
Ranked #2 on Action Recognition on Volleyball
no code implementations • 27 Nov 2016 • Radhakrishna Achanta, Pablo Márquez-Neila, Pascal Fua, Sabine Süsstrunk
Since information is a natural way of measuring image complexity, our proposed algorithm leads to image segments that are smaller and denser in areas of high complexity and larger in homogeneous regions, thus simplifying the image while preserving its details.
no code implementations • CVPR 2017 • Pierre Baqué, François Fleuret, Pascal Fua
Mean Field inference is central to statistical physics.
1 code implementation • ICCV 2017 • Bugra Tekin, Pablo Márquez-Neila, Mathieu Salzmann, Pascal Fua
Most recent approaches to monocular 3D human pose estimation rely on Deep Learning.
Ranked #277 on 3D Human Pose Estimation on Human3.6M
no code implementations • 29 Jun 2016 • Ksenia Konyushkova, Raphael Sznitman, Pascal Fua
Our approach combines geometric smoothness priors in the image space with more traditional uncertainty measures to estimate which pixels or voxels are the most informative, and thus should to be annotated next.
no code implementations • CVPR 2016 • Hani Altwaijry, Eduard Trulls, James Hays, Pascal Fua, Serge Belongie
We demonstrate that our models outperform the state-of-the-art on ultra-wide baseline matching and approach human accuracy.
no code implementations • 17 May 2016 • Bugra Tekin, Isinsu Katircioglu, Mathieu Salzmann, Vincent Lepetit, Pascal Fua
Most recent approaches to monocular 3D pose estimation rely on Deep Learning.
Ranked #313 on 3D Human Pose Estimation on Human3.6M
1 code implementation • 30 Mar 2016 • Kwang Moo Yi, Eduard Trulls, Vincent Lepetit, Pascal Fua
We introduce a novel Deep Network architecture that implements the full feature point handling pipeline, that is, detection, orientation estimation, and feature description.
no code implementations • 21 Mar 2016 • Artem Rozantsev, Mathieu Salzmann, Pascal Fua
To this end, we introduce a two-stream architecture, where one operates in the source domain and the other in the target domain.
no code implementations • 14 Feb 2016 • Bin Fan, Qingqun Kong, Wei Sui, Zhiheng Wang, Xinchao Wang, Shiming Xiang, Chunhong Pan, Pascal Fua
Binary features have been incrementally popular in the past few years due to their low memory footprints and the efficient computation of Hamming distance between binary descriptors.
no code implementations • CVPR 2016 • Agata Mosinska, Raphael Sznitman, Przemysław Głowacki, Pascal Fua
Many recent delineation techniques owe much of their increased effectiveness to path classification algorithms that make it possible to distinguish promising paths from others.
no code implementations • ICCV 2015 • Daniel Glasner, Pascal Fua, Todd Zickler, Lihi Zelnik-Manor
In this paper we explore interactions between the appearance of an outdoor scene and the ambient temperature.
no code implementations • NeurIPS 2015 • Mohammad E. Khan, Pierre Baque, François Fleuret, Pascal Fua
Secondly, we use the proximal framework to derive efficient variational algorithms for non-conjugate models.
no code implementations • ICCV 2015 • Alberto Crivellaro, Mahdi Rad, Yannick Verdie, Kwang Moo Yi, Pascal Fua, Vincent Lepetit
We present a method that estimates in real-time and under challenging conditions the 3D pose of a known object.
no code implementations • ICCV 2015 • Amos Sironi, Vincent Lepetit, Pascal Fua
Detection of elongated structures in 2D images and 3D image stacks is a critical prerequisite in many applications and Machine Learning-based approaches have recently been shown to deliver superior performance.
1 code implementation • ICCV 2015 • Edgar Simo-Serra, Eduard Trulls, Luis Ferraz, Iasonas Kokkinos, Pascal Fua, Francesc Moreno-Noguer
Deep learning has revolutionalized image-level tasks such as classification, but patch-level tasks, such as correspondence, still rely on hand-crafted features, e. g. SIFT.
Ranked #2 on Satellite Image Classification on SAT-4
no code implementations • CVPR 2016 • Bugra Tekin, Artem Rozantsev, Vincent Lepetit, Pascal Fua
We propose an efficient approach to exploiting motion information from consecutive frames of a video sequence to recover the 3D pose of people.
no code implementations • CVPR 2016 • Pierre Baqué, Timur Bagautdinov, François Fleuret, Pascal Fua
Mean-field variational inference is one of the most popular approaches to inference in discrete random fields.
no code implementations • CVPR 2016 • Andrii Maksai, Xinchao Wang, Pascal Fua
Tracking the ball is critical for video-based analysis of team sports.
no code implementations • CVPR 2016 • Kwang Moo Yi, Yannick Verdie, Pascal Fua, Vincent Lepetit
We show how to train a Convolutional Neural Network to assign a canonical orientation to feature points given an image patch centered on the feature point.
no code implementations • ICCV 2015 • Ksenia Konyushkova, Raphael Sznitman, Pascal Fua
We propose an Active Learning approach to training a segmentation classifier that exploits geometric priors to streamline the annotation process in 3D image volumes.
no code implementations • CVPR 2015 • Timur Bagautdinov, Francois Fleuret, Pascal Fua
We propose a novel approach to computing the probabilities of presence of multiple and potentially occluding objects in a scene from a single depth map.
no code implementations • arXiv:1504.08200 Search... Help | Advanced Search 2015 • Bugra Tekin, Xiaolu Sun, Xinchao Wang, Vincent Lepetit, Pascal Fua
We propose an efficient approach to exploiting motion information from consecutive frames of a video sequence to recover the 3D pose of people.
Ranked #314 on 3D Human Pose Estimation on Human3.6M
no code implementations • 30 Apr 2015 • Bugra Tekin, Xiaolu Sun, Xinchao Wang, Vincent Lepetit, Pascal Fua
We propose an efficient approach to exploiting motion information from consecutive frames of a video sequence to recover the 3D pose of people.
no code implementations • 16 Mar 2015 • Dat Tien Ngo, Jonas Ostlund, Pascal Fua
We show that by extending the Laplacian formalism, which was first introduced in the Graphics community to regularize 3D meshes, we can turn the monocular 3D shape reconstruction of a deformable surface given correspondences with a reference image into a much better-posed problem.
no code implementations • ICCV 2015 • Dat Tien Ngo, Sanghuyk Park, Anne Jorstad, Alberto Crivellaro, Chang Yoo, Pascal Fua
In this work, we explicitly address the problem of 3D reconstruction of poorly textured, occluded surfaces, proposing a framework based on a template-matching approach that scales dense robust features by a relevancy score.
no code implementations • 20 Feb 2015 • Pierre Baqué, Jean-Hubert Hours, François Fleuret, Pascal Fua
Mean-Field is an efficient way to approximate a posterior distribution in complex graphical models and constitutes the most popular class of Bayesian variational approximation methods.
no code implementations • 13 Feb 2015 • Pascal Fua, Graham Knott
If we are ever to unravel the mysteries of brain function at its most fundamental level, we will need a precise understanding of how its component neurons connect to each other.
no code implementations • 22 Jan 2015 • Engin Türetken, Xinchao Wang, Carlos Becker, Carsten Haubold, Pascal Fua
We propose a novel approach to automatically tracking cell populations in time-lapse images.
no code implementations • 28 Nov 2014 • Artem Rozantsev, Vincent Lepetit, Pascal Fua
We propose a novel approach to synthesizing images that are effective for training object detectors.
no code implementations • CVPR 2015 • Artem Rozantsev, Vincent Lepetit, Pascal Fua
We propose an approach to detect flying objects such as UAVs and aircrafts when they occupy a small portion of the field of view, possibly moving against complex backgrounds, and are filmed by a camera that itself moves.
no code implementations • CVPR 2015 • Yannick Verdie, Kwang Moo Yi, Pascal Fua, Vincent Lepetit
We introduce a learning-based approach to detect repeatable keypoints under drastic imaging changes of weather and lighting conditions to which state-of-the-art keypoint detectors are surprisingly sensitive.
no code implementations • 6 Sep 2014 • Vasileios Belagiannis, Xinchao Wang, Bernt Schiele, Pascal Fua, Slobodan Ilic, Nassir Navab
To address these challenges, we propose a temporally consistent 3D Pictorial Structures model (3DPS) for multiple human pose estimation from multiple camera views.
Ranked #16 on 3D Multi-Person Pose Estimation on Campus
no code implementations • 28 Jul 2014 • Roberto Rigamonti, Vincent Lepetit, Pascal Fua
In this Technical Report we propose a set of improvements with respect to the KernelBoost classifier presented in [Becker et al., MICCAI 2013].
no code implementations • CVPR 2014 • Przemyslaw Glowacki, Miguel Amavel Pinheiro, Engin Turetken, Raphael Sznitman, Daniel Lebrecht, Jan Kybic, Anthony Holtmaat, Pascal Fua
We propose an approach to reconstructing tree structures that evolve over time in 2D images and 3D image stacks such as neuronal axons or plant branches.
no code implementations • CVPR 2014 • Amos Sironi, Vincent Lepetit, Pascal Fua
We propose a robust and accurate method to extract the centerlines and scale of tubular structures in 2D images and 3D volumes.
no code implementations • NeurIPS 2013 • Carlos J. Becker, Christos M. Christoudias, Pascal Fua
This problem is accentuated with 3D data, for which annotation is very time-consuming, limiting the amount of data that can be labeled in new acquisitions for training.
no code implementations • CVPR 2013 • Roberto Rigamonti, Amos Sironi, Vincent Lepetit, Pascal Fua
Learning filters to produce sparse image representations in terms of overcomplete dictionaries has emerged as a powerful way to create image features for many different purposes.
no code implementations • CVPR 2013 • Aurelien Lucchi, Yunpeng Li, Pascal Fua
We propose a working set based approximate subgradient descent algorithm to minimize the margin-sensitive hinge loss arising from the soft constraints in max-margin learning frameworks, such as the structured SVM.
no code implementations • CVPR 2013 • Tomasz Trzcinski, Mario Christoudias, Pascal Fua, Vincent Lepetit
Binary keypoint descriptors provide an efficient alternative to their floating-point competitors as they enable faster processing while requiring less memory.
no code implementations • CVPR 2013 • Engin Turetken, Fethallah Benmansour, Bjoern Andres, Hanspeter Pfister, Pascal Fua
We propose a novel approach to automated delineation of linear structures that form complex and potentially loopy networks.
no code implementations • CVPR 2013 • Raphael Sznitman, Carlos Becker, Francois Fleuret, Pascal Fua
Cascade-style approaches to implementing ensemble classifiers can deliver significant speed-ups at test time.
no code implementations • 27 Mar 2013 • Pascal Fua
We discuss the relationship between Dempster's rule and our proposed rule for combining evidence over continuous frames.
no code implementations • NeurIPS 2012 • Tomasz Trzcinski, Mario Christoudias, Vincent Lepetit, Pascal Fua
The main goal of local feature descriptors is to distinctively represent a salient image region while remaining invariant to viewpoint and illumination changes.