Search Results for author: Arjun Jain

Found 18 papers, 8 papers with code

ProtoGAN: Towards Few Shot Learning for Action Recognition

no code implementations17 Sep 2019 Sai Kumar Dwivedi, Vikram Gupta, Rahul Mitra, Shuaib Ahmed, Arjun Jain

To the best of our knowledge, we are the first to report the results for G-FSL and provide a strong benchmark for future research.

Action Recognition Few-Shot Learning

Progression Modelling for Online and Early Gesture Detection

1 code implementation14 Sep 2019 Vikram Gupta, Sai Kumar Dwivedi, Rishabh Dabral, Arjun Jain

Online and Early detection of gestures is crucial for building touchless gesture based interfaces.

Multi-Task Learning

On the Robustness of Human Pose Estimation

no code implementations18 Aug 2019 Sahil Shah, Naman jain, Abhishek Sharma, Arjun Jain

This paper provides a comprehensive and exhaustive study of adversarial attacks on human pose estimation models and the evaluation of their robustness.

General Classification Pose Estimation +2

Multiview-Consistent Semi-Supervised Learning for 3D Human Pose Estimation

no code implementations CVPR 2020 Rahul Mitra, Nitesh B. Gundavarapu, Abhishek Sharma, Arjun Jain

The best performing methods for 3D human pose estimation from monocular images require large amounts of in-the-wild 2D and controlled 3D pose annotated datasets which are costly and require sophisticated systems to acquire.

3D Human Pose Estimation Metric Learning +2

An Improved Learning Framework for Covariant Local Feature Detection

no code implementations1 Nov 2018 Nehal Doiphode, Rahul Mitra, Shuaib Ahmed, Arjun Jain

However, just learning from covariant constraint can lead to detection of unstable features.

A Large Dataset for Improving Patch Matching

1 code implementation4 Jan 2018 Rahul Mitra, Nehal Doiphode, Utkarsh Gautam, Sanath Narayan, Shuaib Ahmed, Sharat Chandran, Arjun Jain

Similarly on the Strecha dataset, we see an improvement of 3-5% for the matching task in non-planar scenes.

Patch Matching Retrieval

Improved Descriptors for Patch Matching and Reconstruction

no code implementations24 Jan 2017 Rahul Mitra, Jiakai Zhang, Sanath Narayan, Shuaib Ahmed, Sharat Chandran, Arjun Jain

Scenes from the Oxford ACRD, MVS and Synthetic datasets are used for evaluating the patch matching performance of the learnt descriptors while the Strecha dataset is used to evaluate the 3D reconstruction task.

3D Reconstruction Patch Matching

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

Efficient ConvNet-Based Marker-Less Motion Capture in General Scenes With a Low Number of Cameras

no code implementations CVPR 2015 Ahmed Elhayek, Edilson de Aguiar, Arjun Jain, Jonathan Tompson, Leonid Pishchulin, Micha Andriluka, Chris Bregler, Bernt Schiele, Christian Theobalt

Our approach unites a discriminative image-based joint detection method with a model-based generative motion tracking algorithm through a combined pose optimization energy.

Pose Estimation

MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation

no code implementations28 Sep 2014 Arjun Jain, Jonathan Tompson, Yann Lecun, Christoph Bregler

In this work, we propose a novel and efficient method for articulated human pose estimation in videos using a convolutional network architecture, which incorporates both color and motion features.

2D Human Pose Estimation Pose Estimation

Learning Human Pose Estimation Features with Convolutional Networks

1 code implementation27 Dec 2013 Arjun Jain, Jonathan Tompson, Mykhaylo Andriluka, Graham W. Taylor, Christoph Bregler

This paper introduces a new architecture for human pose estimation using a multi- layer convolutional network architecture and a modified learning technique that learns low-level features and higher-level weak spatial models.

Object Recognition Pose Estimation +2

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