Iasonas Kokkinos

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Attentive Single-Tasking of Multiple Tasks

In this work we address task interference in universal networks by consi...
04/18/2019 ∙ by Kevis-Kokitsi Maninis, et al. ∙ 24 ∙ share

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Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild

We introduce a simple and effective network architecture for monocular 3...
04/04/2020 ∙ by Dominik Kulon, et al. ∙ 8 ∙ share

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Lifting AutoEncoders: Unsupervised Learning of a Fully-Disentangled 3D Morphable Model using Deep Non-Rigid Structure from Motion

In this work we introduce Lifting Autoencoders, a generative 3D surface-...
04/26/2019 ∙ by Mihir Sahasrabudhe, et al. ∙ 6 ∙ share

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Going Deeper with Point Networks

In this work, we introduce three generic point cloud processing blocks t...
07/01/2019 ∙ by Eric-Tuan Le, et al. ∙ 4 ∙ share

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DensePose: Dense Human Pose Estimation In The Wild

In this work, we establish dense correspondences between RGB image and a...
02/01/2018 ∙ by Rıza Alp Güler, et al. ∙ 2 ∙ share

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Slim DensePose: Thrifty Learning from Sparse Annotations and Motion Cues

DensePose supersedes traditional landmark detectors by densely mapping i...
06/13/2019 ∙ by Natalia Neverova, et al. ∙ 2 ∙ share

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Deep Learning Enhanced Extended Depth-of-Field for Thick Blood-Film Malaria High-Throughput Microscopy

Fast accurate diagnosis of malaria is still a global health challenge fo...
06/18/2019 ∙ by Petru Manescu, et al. ∙ 1 ∙ share

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Mass Displacement Networks

Despite the large improvements in performance attained by using deep lea...
08/12/2017 ∙ by Natalia Neverova, et al. ∙ 0 ∙ share

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DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild

In this paper we propose to learn a mapping from image pixels into a den...
12/04/2016 ∙ by Rıza Alp Güler, et al. ∙ 0 ∙ share

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Deep, Dense, and Low-Rank Gaussian Conditional Random Fields

In this work we introduce a fully-connected graph structure in the Deep ...
11/28/2016 ∙ by Siddhartha Chandra, et al. ∙ 0 ∙ share

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UberNet: Training a `Universal' Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory

In this work we introduce a convolutional neural network (CNN) that join...
09/07/2016 ∙ by Iasonas Kokkinos, et al. ∙ 0 ∙ share

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

In this work we address the task of semantic image segmentation with Dee...
06/02/2016 ∙ by Liang-Chieh Chen, et al. ∙ 0 ∙ share

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Fast, Exact and Multi-Scale Inference for Semantic Image Segmentation with Deep Gaussian CRFs

In this work we propose a structured prediction technique that combines ...
03/28/2016 ∙ by Siddhartha Chandra, et al. ∙ 0 ∙ share

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Sub-cortical brain structure segmentation using F-CNN's

In this paper we propose a deep learning approach for segmenting sub-cor...
02/05/2016 ∙ by Mahsa Shakeri, et al. ∙ 0 ∙ share

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Learning Dense Convolutional Embeddings for Semantic Segmentation

This paper proposes a new deep convolutional neural network (DCNN) archi...
11/13/2015 ∙ by Adam W. Harley, et al. ∙ 0 ∙ share

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Deep filter banks for texture recognition, description, and segmentation

Visual textures have played a key role in image understanding because th...
07/09/2015 ∙ by Mircea Cimpoi, et al. ∙ 0 ∙ share

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Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Deep Convolutional Neural Networks (DCNNs) have recently shown state of ...
12/22/2014 ∙ by Liang-Chieh Chen, et al. ∙ 0 ∙ share

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Fracking Deep Convolutional Image Descriptors

In this paper we propose a novel framework for learning local image desc...
12/19/2014 ∙ by Edgar Simo-Serra, et al. ∙ 0 ∙ share

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

Deep Convolutional Neural Networks (DCNNs) commonly use generic `max-poo...
11/30/2014 ∙ by George Papandreou, et al. ∙ 0 ∙ share

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Describing Textures in the Wild

Patterns and textures are defining characteristics of many natural objec...
11/14/2013 ∙ by Mircea Cimpoi, et al. ∙ 0 ∙ share

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Learning Filterbanks from Raw Speech for Phone Recognition

We train a bank of complex filters that operates on the raw waveform and...
11/03/2017 ∙ by Neil Zeghidour, et al. ∙ 0 ∙ share

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Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance

In this work we introduce Deforming Autoencoders, a generative model for...
06/18/2018 ∙ by Zhixin Shu, et al. ∙ 0 ∙ share

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Deep Spatio-Temporal Random Fields for Efficient Video Segmentation

In this work we introduce a time- and memory-efficient method for struct...
07/03/2018 ∙ by Siddhartha Chandra, et al. ∙ 0 ∙ share

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Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Images

In this paper we address the memory demands that come with the processin...
08/16/2018 ∙ by Stefano B. Blumberg, et al. ∙ 0 ∙ share

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Dense Pose Transfer

In this work we integrate ideas from surface-based modeling with neural ...
09/06/2018 ∙ by Natalia Neverova, et al. ∙ 0 ∙ share

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MultiGrain: a unified image embedding for classes and instances

MultiGrain is a network architecture producing compact vector representa...
02/14/2019 ∙ by Maxim Berman, et al. ∙ 0 ∙ share

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Iasonas Kokkinos

Featured Co-authors

Andrea Vedaldi

Stefanos Zafeiriou

Kevin Murphy

Alan L. Yuille

Subhransu Maji

Hervé Jégou

Gabriel Synnaeve

John Shawe-Taylor

Dimitris Samaras

Liang-Chieh Chen

Emmanuel Dupoux

Attentive Single-Tasking of Multiple Tasks

Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild

Lifting AutoEncoders: Unsupervised Learning of a Fully-Disentangled 3D Morphable Model using Deep Non-Rigid Structure from Motion

Going Deeper with Point Networks

DensePose: Dense Human Pose Estimation In The Wild

Slim DensePose: Thrifty Learning from Sparse Annotations and Motion Cues

Deep Learning Enhanced Extended Depth-of-Field for Thick Blood-Film Malaria High-Throughput Microscopy

Mass Displacement Networks

DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild

Deep, Dense, and Low-Rank Gaussian Conditional Random Fields

UberNet: Training a `Universal' Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory

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

Fast, Exact and Multi-Scale Inference for Semantic Image Segmentation with Deep Gaussian CRFs

Sub-cortical brain structure segmentation using F-CNN's

Learning Dense Convolutional Embeddings for Semantic Segmentation

Deep filter banks for texture recognition, description, and segmentation

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Fracking Deep Convolutional Image Descriptors

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

Describing Textures in the Wild

Learning Filterbanks from Raw Speech for Phone Recognition

Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance

Deep Spatio-Temporal Random Fields for Efficient Video Segmentation

Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Images

Dense Pose Transfer

MultiGrain: a unified image embedding for classes and instances