/CVPR2018_attention

Context Encoding for Semantic Segmentation MegaDepth: Learning Single-View Depth Prediction from Internet Photos LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume On the Robustness of Semantic Segmentation Models to Adversarial Attacks SPLATNet: Sparse Lattice Networks for Point Cloud Processing Left-Right Comparative Recurrent Model for Stereo Matching Enhancing the Spatial Resolution of Stereo Images using a Parallax Prior Unsupervised CCA Discovering Point Lights with Intensity Distance Fields CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation Learning a Discriminative Feature Network for Semantic Segmentation Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation Unsupervised Deep Generative Adversarial Hashing Network Monocular Relative Depth Perception with Web Stereo Data Supervision Single Image Reflection Separation with Perceptual Losses Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains EPINET: A Fully-Convolutional Neural Network for Light Field Depth Estimation by Using Epipolar Geometry FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds Decorrelated Batch Normalization Unsupervised Learning of Depth and Egomotion from Monocular Video Using 3D Geometric Constraints PU-Net: Point Cloud Upsampling Network Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer Tell Me Where To Look: Guided Attention Inference Network Residual Dense Network for Image Super-Resolution Reflection Removal for Large-Scale 3D Point Clouds PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image Fully Convolutional Adaptation Networks for Semantic Segmentation CRRN: Multi-Scale Guided Concurrent Reflection Removal Network DenseASPP: Densely Connected Networks for Semantic Segmentation SGAN: An Alternative Training of Generative Adversarial Networks Multi-Agent Diverse Generative Adversarial Networks Robust Depth Estimation from Auto Bracketed Images AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation DeepMVS: Learning Multi-View Stereopsis GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation Single-Image Depth Estimation Based on Fourier Domain Analysis Single View Stereo Matching Pyramid Stereo Matching Network A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation Image Correction via Deep Reciprocating HDR Transformation Occlusion Aware Unsupervised Learning of Optical Flow PAD-Net: Multi-Tasks Guided Prediciton-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing Surface Networks Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation TextureGAN: Controlling Deep Image Synthesis with Texture Patches Aperture Supervision for Monocular Depth Estimation Two-Stream Convolutional Networks for Dynamic Texture Synthesis Unsupervised Learning of Single View Depth Estimation and Visual Odometry with Deep Feature Reconstruction Left/Right Asymmetric Layer Skippable Networks Learning to See in the Dark

CVPR2018_attention

Context Encoding for Semantic Segmentation
MegaDepth: Learning Single-View Depth Prediction from Internet Photos
LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
On the Robustness of Semantic Segmentation Models to Adversarial Attacks
SPLATNet: Sparse Lattice Networks for Point Cloud Processing
Left-Right Comparative Recurrent Model for Stereo Matching
Enhancing the Spatial Resolution of Stereo Images using a Parallax Prior
Unsupervised CCA
Discovering Point Lights with Intensity Distance Fields
CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation
Learning a Discriminative Feature Network for Semantic Segmentation
Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation
Unsupervised Deep Generative Adversarial Hashing Network
Monocular Relative Depth Perception with Web Stereo Data Supervision
Single Image Reflection Separation with Perceptual Losses
Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains
EPINET: A Fully-Convolutional Neural Network for Light Field Depth Estimation by Using Epipolar Geometry
FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds
Decorrelated Batch Normalization
Unsupervised Learning of Depth and Egomotion from Monocular Video Using 3D Geometric Constraints
PU-Net: Point Cloud Upsampling Network
Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer
Tell Me Where To Look: Guided Attention Inference Network
Residual Dense Network for Image Super-Resolution
Reflection Removal for Large-Scale 3D Point Clouds
PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image
Fully Convolutional Adaptation Networks for Semantic Segmentation
CRRN: Multi-Scale Guided Concurrent Reflection Removal Network
DenseASPP: Densely Connected Networks for Semantic Segmentation
SGAN: An Alternative Training of Generative Adversarial Networks
Multi-Agent Diverse Generative Adversarial Networks
Robust Depth Estimation from Auto Bracketed Images
AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation
DeepMVS: Learning Multi-View Stereopsis
GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation
Single-Image Depth Estimation Based on Fourier Domain Analysis
Single View Stereo Matching
Pyramid Stereo Matching Network A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation
Image Correction via Deep Reciprocating HDR Transformation Occlusion Aware Unsupervised Learning of Optical Flow PAD-Net: Multi-Tasks Guided Prediciton-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing Surface Networks Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation
TextureGAN: Controlling Deep Image Synthesis with Texture Patches
Aperture Supervision for Monocular Depth Estimation Two-Stream Convolutional Networks for Dynamic Texture Synthesis
Unsupervised Learning of Single View Depth Estimation and Visual Odometry with Deep Feature Reconstruction
Left/Right Asymmetric Layer Skippable Networks
Learning to See in the Dark