Search Results for author: George Papandreou

Found 20 papers, 8 papers with code

BLSM: A Bone-Level Skinned Model of the Human Mesh

no code implementations ECCV 2020 Haoyang Wang, Riza Alp Güler, Iasonas Kokkinos, George Papandreou, Stefanos Zafeiriou

We introduce BLSM, a bone-level skinned model of the human body mesh where bone scales are set prior to template synthesis, rather than the common, inverse practice.

Unity

Volumetric Capture of Humans with a Single RGBD Camera via Semi-Parametric Learning

no code implementations CVPR 2019 Rohit Pandey, Anastasia Tkach, Shuoran Yang, Pavel Pidlypenskyi, Jonathan Taylor, Ricardo Martin-Brualla, Andrea Tagliasacchi, George Papandreou, Philip Davidson, Cem Keskin, Shahram Izadi, Sean Fanello

The key insight is to leverage previously seen "calibration" images of a given user to extrapolate what should be rendered in a novel viewpoint from the data available in the sensor.

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

70 code implementations ECCV 2018 Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, Hartwig Adam

The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling operations at multiple rates and multiple effective fields-of-view, while the latter networks can capture sharper object boundaries by gradually recovering the spatial information.

Image Classification Lesion Segmentation +1

Rethinking Atrous Convolution for Semantic Image Segmentation

60 code implementations17 Jun 2017 Liang-Chieh Chen, George Papandreou, Florian Schroff, Hartwig Adam

To handle the problem of segmenting objects at multiple scales, we design modules which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates.

Ranked #5 on Semantic Segmentation on PASCAL VOC 2012 val (using extra training data)

Semantic Segmentation Thermal Image Segmentation

Towards Accurate Multi-person Pose Estimation in the Wild

no code implementations CVPR 2017 George Papandreou, Tyler Zhu, Nori Kanazawa, Alexander Toshev, Jonathan Tompson, Chris Bregler, Kevin Murphy

Trained on COCO data alone, our final system achieves average precision of 0. 649 on the COCO test-dev set and the 0. 643 test-standard sets, outperforming the winner of the 2016 COCO keypoints challenge and other recent state-of-art.

Human Detection Keypoint Detection +1

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

47 code implementations2 Jun 2016 Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille

ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales.

Semantic Segmentation

Weakly- and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation

1 code implementation ICCV 2015 George Papandreou, Liang-Chieh Chen, Kevin P. Murphy, Alan L. Yuille

Deep convolutional neural networks (DCNNs) trained on a large number of images with strong pixel-level annotations have recently significantly pushed the state-of-art in semantic image segmentation.

Semantic Segmentation

Im2Calories: Towards an Automated Mobile Vision Food Diary

no code implementations ICCV 2015 Austin Meyers, Nick Johnston, Vivek Rathod, Anoop Korattikara, Alex Gorban, Nathan Silberman, Sergio Guadarrama, George Papandreou, Jonathan Huang, Kevin P. Murphy

We present a system which can recognize the contents of your meal from a single image, and then predict its nutritional contents, such as calories.

Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation

3 code implementations9 Feb 2015 George Papandreou, Liang-Chieh Chen, Kevin Murphy, Alan L. Yuille

Deep convolutional neural networks (DCNNs) trained on a large number of images with strong pixel-level annotations have recently significantly pushed the state-of-art in semantic image segmentation.

Semi-Supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation

Untangling Local and Global Deformations in Deep Convolutional Networks for Image Classification and Sliding Window Detection

no code implementations30 Nov 2014 George Papandreou, Iasonas Kokkinos, Pierre-André Savalle

Deep Convolutional Neural Networks (DCNNs) commonly use generic `max-pooling' (MP) layers to extract deformation-invariant features, but we argue in favor of a more refined treatment.

General Classification Image Classification +5

Deep Epitomic Convolutional Neural Networks

no code implementations10 Jun 2014 George Papandreou

An epitomic convolution layer replaces a pair of consecutive convolution and max-pooling layers found in standard deep convolutional neural networks.

General Classification Image Classification

Modeling Image Patches with a Generic Dictionary of Mini-Epitomes

no code implementations CVPR 2014 George Papandreou, Liang-Chieh Chen, Alan L. Yuille

As an alternative, we develop a generative model for the raw intensity of image patches and show that it can support image classification performance on par with optimized SIFT-based techniques in a bag-of-visual-words setting.

Classification General Classification +1

Gaussian sampling by local perturbations

no code implementations NeurIPS 2010 George Papandreou, Alan L. Yuille

We present a technique for exact simulation of Gaussian Markov random fields (GMRFs), which can be interpreted as locally injecting noise to each Gaussian factor independently, followed by computing the mean/mode of the perturbed GMRF.

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