Iasonas Kokkinos

research

∙ 02/15/2022

Beyond Deterministic Translation for Unsupervised Domain Adaptation

In this work we challenge the common approach of using a one-to-one mapp...

Eleni Chiou, et al. ∙

research

∙ 09/19/2021

Unsupervised Domain Adaptation with Semantic Consistency across Heterogeneous Modalities for MRI Prostate Lesion Segmentation

Any novel medical imaging modality that differs from previous protocols ...

Eleni Chiou, et al. ∙

research

∙ 06/10/2021

To The Point: Correspondence-driven monocular 3D category reconstruction

We present To The Point (TTP), a method for reconstructing 3D objects fr...

Filippos Kokkinos, et al. ∙

research

∙ 03/30/2021

Learning monocular 3D reconstruction of articulated categories from motion

Monocular 3D reconstruction of articulated object categories is challeng...

Filippos Kokkinos, et al. ∙

research

∙ 10/14/2020

Harnessing Uncertainty in Domain Adaptation for MRI Prostate Lesion Segmentation

The need for training data can impede the adoption of novel imaging moda...

Eleni Chiou, et al. ∙

research

∙ 08/23/2020

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

We address six different classification tasks related to fine-grained bu...

Zbigniew Wojna, et al. ∙

research

∙ 04/04/2020

Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild

We introduce a simple and effective network architecture for monocular 3...

Dominik Kulon, et al. ∙

research

∙ 07/01/2019

Going Deeper with Point Networks

In this work, we introduce three generic point cloud processing blocks t...

Eric-Tuan Le, et al. ∙

research

∙ 06/18/2019

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...

Petru Manescu, et al. ∙

research

∙ 06/13/2019

Slim DensePose: Thrifty Learning from Sparse Annotations and Motion Cues

DensePose supersedes traditional landmark detectors by densely mapping i...

Natalia Neverova, et al. ∙

research

∙ 04/26/2019

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-...

Mihir Sahasrabudhe, et al. ∙

research

∙ 04/18/2019

Attentive Single-Tasking of Multiple Tasks

In this work we address task interference in universal networks by consi...

Kevis-Kokitsi Maninis, et al. ∙

research

∙ 02/14/2019

MultiGrain: a unified image embedding for classes and instances

MultiGrain is a network architecture producing compact vector representa...

Maxim Berman, et al. ∙

research

∙ 09/06/2018

Dense Pose Transfer

In this work we integrate ideas from surface-based modeling with neural ...

Natalia Neverova, et al. ∙

research

∙ 08/16/2018

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...

Stefano B. Blumberg, et al. ∙

research

∙ 07/03/2018

Deep Spatio-Temporal Random Fields for Efficient Video Segmentation

In this work we introduce a time- and memory-efficient method for struct...

Siddhartha Chandra, et al. ∙

research

∙ 06/18/2018

Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance

In this work we introduce Deforming Autoencoders, a generative model for...

Zhixin Shu, et al. ∙

research

∙ 02/01/2018

DensePose: Dense Human Pose Estimation In The Wild

In this work, we establish dense correspondences between RGB image and a...

Rıza Alp Güler, et al. ∙

research

∙ 11/03/2017

Learning Filterbanks from Raw Speech for Phone Recognition

We train a bank of complex filters that operates on the raw waveform and...

Neil Zeghidour, et al. ∙

research

∙ 08/12/2017

Mass Displacement Networks

Despite the large improvements in performance attained by using deep lea...

Natalia Neverova, et al. ∙

research

∙ 12/04/2016

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

In this paper we propose to learn a mapping from image pixels into a den...

Rıza Alp Güler, et al. ∙

research

∙ 11/28/2016

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

In this work we introduce a fully-connected graph structure in the Deep ...

Siddhartha Chandra, et al. ∙

research

∙ 09/07/2016

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...

Iasonas Kokkinos, et al. ∙

research

∙ 06/02/2016

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...

Liang-Chieh Chen, et al. ∙

research

∙ 03/28/2016

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 ...

Siddhartha Chandra, et al. ∙

research

∙ 02/05/2016

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

In this paper we propose a deep learning approach for segmenting sub-cor...

Mahsa Shakeri, et al. ∙

research

∙ 11/13/2015

Learning Dense Convolutional Embeddings for Semantic Segmentation

This paper proposes a new deep convolutional neural network (DCNN) archi...

Adam W. Harley, et al. ∙

research

∙ 07/09/2015

Deep filter banks for texture recognition, description, and segmentation

Visual textures have played a key role in image understanding because th...

Mircea Cimpoi, et al. ∙

research

∙ 12/22/2014

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Deep Convolutional Neural Networks (DCNNs) have recently shown state of ...

Liang-Chieh Chen, et al. ∙

research

∙ 12/19/2014

Fracking Deep Convolutional Image Descriptors

In this paper we propose a novel framework for learning local image desc...

Edgar Simo-Serra, et al. ∙

research

∙ 11/30/2014

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...

George Papandreou, et al. ∙

research

∙ 11/14/2013

Describing Textures in the Wild

Patterns and textures are defining characteristics of many natural objec...

Mircea Cimpoi, et al. ∙

Iasonas Kokkinos

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