Search Results for author: Sina Honari

Found 24 papers, 13 papers with code

Unsupervised 3D Keypoint Estimation with Multi-View Geometry

no code implementations23 Nov 2022 Sina Honari, Pascal Fua

Given enough annotated training data, 3D human pose estimation models can achieve high accuracy.

3D Human Pose Estimation Keypoint Estimation +1

Perspective Aware Road Obstacle Detection

1 code implementation4 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.

Adversarial Parametric Pose Prior

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.

3D Reconstruction

Overcoming the Domain Gap in Neural Action Representations

no code implementations2 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.

Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness Metrics

1 code implementation8 Jun 2021 Charan Reddy, Soroush Mehri, Deepak Sharma, Samira Shabanian, Sina Honari

With the recent expanding attention of machine learning researchers and practitioners to fairness, there is a void of a common framework to analyze and compare the capabilities of proposed models in deep representation learning.

Age And Gender Classification Benchmarking +6

SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation

2 code implementations30 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.

Instance Segmentation Segmentation +1

Detecting Road Obstacles by Erasing Them

no code implementations25 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.

Temporal Representation Learning on Monocular Videos for 3D Human Pose Estimation

no code implementations2 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.

3D Human Pose Estimation 3D Pose Estimation +1

U-Net Fixed-Point Quantization for Medical Image Segmentation

2 code implementations2 Aug 2019 MohammadHossein AskariHemmat, Sina Honari, Lucas Rouhier, Christian S. Perone, Julien Cohen-Adad, Yvon Savaria, Jean-Pierre David

We then apply our quantization algorithm to three datasets: (1) the Spinal Cord Gray Matter Segmentation (GM), (2) the ISBI challenge for segmentation of neuronal structures in Electron Microscopic (EM), and (3) the public National Institute of Health (NIH) dataset for pancreas segmentation in abdominal CT scans.

Image Segmentation Pancreas Segmentation +3

Adversarial Mixup Resynthesizers

1 code implementation ICLR Workshop DeepGenStruct 2019 Christopher Beckham, Sina Honari, Alex Lamb, Vikas Verma, Farnoosh Ghadiri, R Devon Hjelm, Christopher Pal

In this paper, we explore new approaches to combining information encoded within the learned representations of autoencoders.

On Adversarial Mixup Resynthesis

1 code implementation NeurIPS 2019 Christopher Beckham, Sina Honari, Vikas Verma, Alex Lamb, Farnoosh Ghadiri, R. Devon Hjelm, Yoshua Bengio, Christopher Pal

In this paper, we explore new approaches to combining information encoded within the learned representations of auto-encoders.


Distribution Matching Losses Can Hallucinate Features in Medical Image Translation

1 code implementation22 May 2018 Joseph Paul Cohen, Margaux Luck, Sina Honari

When the output of an algorithm is a transformed image there are uncertainties whether all known and unknown class labels have been preserved or changed.

Image Generation Translation

Unsupervised Depth Estimation, 3D Face Rotation and Replacement

1 code implementation NeurIPS 2018 Joel Ruben Antony Moniz, Christopher Beckham, Simon Rajotte, Sina Honari, Christopher Pal

We present an unsupervised approach for learning to estimate three dimensional (3D) facial structure from a single image while also predicting 3D viewpoint transformations that match a desired pose and facial geometry.

Depth Estimation Translation

Improving Landmark Localization with Semi-Supervised Learning

no code implementations CVPR 2018 Sina Honari, Pavlo Molchanov, Stephen Tyree, Pascal Vincent, Christopher Pal, Jan Kautz

First, we propose the framework of sequential multitasking and explore it here through an architecture for landmark localization where training with class labels acts as an auxiliary signal to guide the landmark localization on unlabeled data.

Face Alignment Small Data Image Classification

Learning to Generate Samples from Noise through Infusion Training

1 code implementation20 Mar 2017 Florian Bordes, Sina Honari, Pascal Vincent

In this work, we investigate a novel training procedure to learn a generative model as the transition operator of a Markov chain, such that, when applied repeatedly on an unstructured random noise sample, it will denoise it into a sample that matches the target distribution from the training set.


Theano: A Python framework for fast computation of mathematical expressions

1 code implementation9 May 2016 The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre-Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Mélanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian Goodfellow, Matt Graham, Caglar Gulcehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrancois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert T. McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang

Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.

BIG-bench Machine Learning Clustering +2

Improving Facial Analysis and Performance Driven Animation through Disentangling Identity and Expression

no code implementations27 Dec 2015 David Rim, Sina Honari, Md. Kamrul Hasan, Chris Pal

We use a weakly-supervised approach in which identity labels are used to learn the different factors of variation linked to identity separately from factors related to expression.

Emotion Recognition Point Tracking

Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation

1 code implementation CVPR 2016 Sina Honari, Jason Yosinski, Pascal Vincent, Christopher Pal

Deep neural networks with alternating convolutional, max-pooling and decimation layers are widely used in state of the art architectures for computer vision.

Image Classification

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