└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # CVPR2018_attention 2 | Context Encoding for Semantic Segmentation 3 | MegaDepth: Learning Single-View Depth Prediction from Internet Photos 4 | LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation 5 | PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume 6 | On the Robustness of Semantic Segmentation Models to Adversarial Attacks 7 | SPLATNet: Sparse Lattice Networks for Point Cloud Processing 8 | Left-Right Comparative Recurrent Model for Stereo Matching 9 | Enhancing the Spatial Resolution of Stereo Images using a Parallax Prior 10 | Unsupervised CCA 11 | Discovering Point Lights with Intensity Distance Fields 12 | CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation 13 | Learning a Discriminative Feature Network for Semantic Segmentation 14 | Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation 15 | Unsupervised Deep Generative Adversarial Hashing Network 16 | Monocular Relative Depth Perception with Web Stereo Data Supervision 17 | Single Image Reflection Separation with Perceptual Losses 18 | Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains 19 | EPINET: A Fully-Convolutional Neural Network for Light Field Depth Estimation by Using Epipolar Geometry 20 | FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds 21 | Decorrelated Batch Normalization 22 | Unsupervised Learning of Depth and Egomotion from Monocular Video Using 3D Geometric Constraints 23 | PU-Net: Point Cloud Upsampling Network 24 | Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer 25 | Tell Me Where To Look: Guided Attention Inference Network 26 | Residual Dense Network for Image Super-Resolution 27 | Reflection Removal for Large-Scale 3D Point Clouds 28 | PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image 29 | Fully Convolutional Adaptation Networks for Semantic Segmentation 30 | CRRN: Multi-Scale Guided Concurrent Reflection Removal Network 31 | DenseASPP: Densely Connected Networks for Semantic Segmentation 32 | SGAN: An Alternative Training of Generative Adversarial Networks 33 | Multi-Agent Diverse Generative Adversarial Networks 34 | Robust Depth Estimation from Auto Bracketed Images 35 | AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation 36 | DeepMVS: Learning Multi-View Stereopsis 37 | GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose 38 | GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation 39 | Single-Image Depth Estimation Based on Fourier Domain Analysis 40 | Single View Stereo Matching 41 | Pyramid Stereo Matching Network 42 | A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation 43 | Image Correction via Deep Reciprocating HDR Transformation 44 | Occlusion Aware Unsupervised Learning of Optical Flow 45 | PAD-Net: Multi-Tasks Guided Prediciton-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing 46 | Surface Networks 47 | Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation 48 | TextureGAN: Controlling Deep Image Synthesis with Texture Patches 49 | Aperture Supervision for Monocular Depth Estimation 50 | Two-Stream Convolutional Networks for Dynamic Texture Synthesis 51 | Unsupervised Learning of Single View Depth Estimation and Visual Odometry with Deep Feature Reconstruction 52 | Left/Right Asymmetric Layer Skippable Networks 53 | Learning to See in the Dark 54 | --------------------------------------------------------------------------------