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10/17/2022
Deformably-Scaled Transposed Convolution
Transposed convolution is crucial for generating high-resolution outputs...
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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...
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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 ...
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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...
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03/30/2021
Learning monocular 3D reconstruction of articulated categories from motion
Monocular 3D reconstruction of articulated object categories is challeng...
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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...
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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...
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04/04/2020
Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild
We introduce a simple and effective network architecture for monocular 3...
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07/01/2019
Going Deeper with Point Networks
In this work, we introduce three generic point cloud processing blocks t...
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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...
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06/13/2019
Slim DensePose: Thrifty Learning from Sparse Annotations and Motion Cues
DensePose supersedes traditional landmark detectors by densely mapping i...
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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-...
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04/18/2019
Attentive Single-Tasking of Multiple Tasks
In this work we address task interference in universal networks by consi...
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02/14/2019
MultiGrain: a unified image embedding for classes and instances
MultiGrain is a network architecture producing compact vector representa...
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09/06/2018
Dense Pose Transfer
In this work we integrate ideas from surface-based modeling with neural ...
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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...
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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...
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06/18/2018
Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance
In this work we introduce Deforming Autoencoders, a generative model for...
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02/01/2018
DensePose: Dense Human Pose Estimation In The Wild
In this work, we establish dense correspondences between RGB image and a...
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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...
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08/12/2017
Mass Displacement Networks
Despite the large improvements in performance attained by using deep lea...
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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...
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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 ...
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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...
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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...
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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 ...
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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...
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11/13/2015
Learning Dense Convolutional Embeddings for Semantic Segmentation
This paper proposes a new deep convolutional neural network (DCNN) archi...
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07/09/2015
Deep filter banks for texture recognition, description, and segmentation
Visual textures have played a key role in image understanding because th...
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12/22/2014
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
Deep Convolutional Neural Networks (DCNNs) have recently shown state of ...
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12/19/2014
Fracking Deep Convolutional Image Descriptors
In this paper we propose a novel framework for learning local image desc...
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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...
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11/14/2013
