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Learning monocular 3D reconstruction of articulated categories from motion
Monocular 3D reconstruction of articulated object categories is challeng...
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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...
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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...
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Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild
We introduce a simple and effective network architecture for monocular 3...
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Going Deeper with Point Networks
In this work, we introduce three generic point cloud processing blocks t...
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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...
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Slim DensePose: Thrifty Learning from Sparse Annotations and Motion Cues
DensePose supersedes traditional landmark detectors by densely mapping i...
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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-...
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Attentive Single-Tasking of Multiple Tasks
In this work we address task interference in universal networks by consi...
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MultiGrain: a unified image embedding for classes and instances
MultiGrain is a network architecture producing compact vector representa...
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Dense Pose Transfer
In this work we integrate ideas from surface-based modeling with neural ...
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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...
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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...
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Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance
In this work we introduce Deforming Autoencoders, a generative model for...
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DensePose: Dense Human Pose Estimation In The Wild
In this work, we establish dense correspondences between RGB image and a...
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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...
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Mass Displacement Networks
Despite the large improvements in performance attained by using deep lea...
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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...
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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 ...
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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...
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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...
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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 ...
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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...
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Learning Dense Convolutional Embeddings for Semantic Segmentation
This paper proposes a new deep convolutional neural network (DCNN) archi...
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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...
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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 ...
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Fracking Deep Convolutional Image Descriptors
In this paper we propose a novel framework for learning local image desc...
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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...
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Describing Textures in the Wild
Patterns and textures are defining characteristics of many natural objec...
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