Iasonas Kokkinos

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Learning monocular 3D reconstruction of articulated categories from motion

Monocular 3D reconstruction of articulated object categories is challeng...
03/30/2021 ∙ by Filippos Kokkinos, et al. ∙ 6 ∙ share

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Harnessing Uncertainty in Domain Adaptation for MRI Prostate Lesion Segmentation

The need for training data can impede the adoption of novel imaging moda...
10/14/2020 ∙ by Eleni Chiou, et al. ∙ 0 ∙ share

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Holistic Multi-View Building Analysis in the Wild with Projection Pooling

We address six different classification tasks related to fine-grained bu...
08/23/2020 ∙ by Zbigniew Wojna, et al. ∙ 5 ∙ 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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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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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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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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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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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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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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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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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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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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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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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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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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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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Iasonas Kokkinos

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