{"list":"other","total":51,"rows":[{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"1709.01507","title":"Squeeze-and-Excitation Networks","created":"2017-09-05","cited_by_count":4789},{"id":"1711.07971","title":"Non-local Neural Networks","created":"2017-11-21","cited_by_count":2293},{"id":"2103.13413","title":"Vision Transformers for Dense Prediction","created":"2021-03-24","cited_by_count":1803},{"id":"1502.03044","title":"Show, Attend and Tell: Neural Image Caption Generation with Visual Attention","created":"2015-02-10","cited_by_count":1423},{"id":"1406.2283","title":"Depth Map Prediction from a Single Image using a Multi-Scale Deep Network","created":"2014-06-09","cited_by_count":1274},{"id":"1807.11626","title":"MnasNet: Platform-Aware Neural Architecture Search for Mobile","created":"2018-07-31","cited_by_count":943},{"id":"1806.02446","title":"Deep Ordinal Regression Network for Monocular Depth Estimation","created":"2018-06-06","cited_by_count":723},{"id":"1411.4734","title":"Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture","created":"2014-11-18","cited_by_count":483},{"id":"1606.00373","title":"Deeper Depth Prediction with Fully Convolutional Residual Networks","created":"2016-06-01","cited_by_count":403},{"id":"1704.06904","title":"Residual Attention Network for Image Classification","created":"2017-04-23","cited_by_count":386},{"id":"1907.10326","title":"From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation","created":"2019-07-24","cited_by_count":382},{"id":"1904.09925","title":"Attention Augmented Convolutional Networks","created":"2019-04-22","cited_by_count":363},{"id":"1708.06500","title":"Sparsity Invariant CNNs","created":"2017-08-22","cited_by_count":326},{"id":"1907.12209","title":"Enforcing geometric constraints of virtual normal for depth prediction","created":"2019-07-29","cited_by_count":234},{"id":"2007.10042","title":"Non-Local Spatial Propagation Network for Depth Completion","created":"2020-07-20","cited_by_count":195},{"id":"1709.07492","title":"Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image","created":"2017-09-21","cited_by_count":161},{"id":"2103.00783","title":"PENet: Towards Precise and Efficient Image Guided Depth Completion","created":"2021-03-01","cited_by_count":146},{"id":"1803.08673","title":"Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object Boundaries","created":"2018-03-23","cited_by_count":145},{"id":"1906.03525","title":"Pattern-Affinitive Propagation across Depth, Surface Normal and Semantic Segmentation","created":"2019-06-08","cited_by_count":142},{"id":"1810.02695","title":"Learning Depth with Convolutional Spatial Propagation Network","created":"2018-10-04","cited_by_count":136},{"id":"1812.00488","title":"DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Image","created":"2018-12-02","cited_by_count":129},{"id":"1803.09326","title":"Deep Depth Completion of a Single RGB-D Image","created":"2018-03-25","cited_by_count":110},{"id":"1808.00150","title":"Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network","created":"2018-08-01","cited_by_count":110},{"id":"1910.06727","title":"Depth Completion from Sparse LiDAR Data with Depth-Normal Constraints","created":"2019-10-15","cited_by_count":108},{"id":"1812.04072","title":"PlaneRCNN: 3D Plane Detection and Reconstruction from a Single Image","created":"2018-12-10","cited_by_count":105},{"id":"1903.03273","title":"FastDepth: Fast Monocular Depth Estimation on Embedded Systems","created":"2019-03-08","cited_by_count":94},{"id":"2004.02760","title":"Guiding Monocular Depth Estimation Using Depth-Attention Volume","created":"2020-04-06","cited_by_count":82},{"id":"1803.11029","title":"Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation","created":"2018-03-29","cited_by_count":78},{"id":"1804.06278","title":"PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image","created":"2018-04-17","cited_by_count":77},{"id":"1808.00769","title":"Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentation","created":"2018-08-02","cited_by_count":76},{"id":"1904.02028","title":"CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth","created":"2019-04-03","cited_by_count":75},{"id":"2012.12402","title":"Learning Joint 2D-3D Representations for Depth Completion","created":"2020-12-22","cited_by_count":71},{"id":"1905.08598","title":"SharpNet: Fast and Accurate Recovery of Occluding Contours in Monocular Depth Estimation","created":"2019-05-21","cited_by_count":59},{"id":"2006.03349","title":"Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End","created":"2020-06-05","cited_by_count":55},{"id":"1811.01791","title":"Confidence Propagation through CNNs for Guided Sparse Depth Regression","created":"2018-11-05","cited_by_count":52},{"id":"2008.03633","title":"Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes","created":"2020-08-09","cited_by_count":50},{"id":"1907.06023","title":"Structure-Aware Residual Pyramid Network for Monocular Depth Estimation","created":"2019-07-13","cited_by_count":49},{"id":"1903.05421","title":"Depth Coefficients for Depth Completion","created":"2019-03-13","cited_by_count":41},{"id":"1805.11913","title":"Propagating Confidences through CNNs for Sparse Data Regression","created":"2018-05-30","cited_by_count":36},{"id":"1807.03959","title":"Deep attention-based classification network for robust depth prediction","created":"2018-07-11","cited_by_count":36},{"id":"1612.07429","title":"Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks","created":"2016-12-22","cited_by_count":35},{"id":"1809.00646","title":"Detail Preserving Depth Estimation from a Single Image Using Attention Guided Networks","created":"2018-09-03","cited_by_count":31},{"id":"1806.11430","title":"Towards real-time unsupervised monocular depth estimation on CPU","created":"2018-06-29","cited_by_count":26},{"id":"1707.05847","title":"The Devil is in the Decoder: Classification, Regression and GANs","created":"2017-07-18","cited_by_count":22},{"id":"2105.08630","title":"Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report","created":"2021-05-17","cited_by_count":19},{"id":"1906.03279","title":"Deep Robust Single Image Depth Estimation Neural Network Using Scene Understanding","created":"2019-06-07","cited_by_count":15},{"id":"2012.10296","title":"Boosting Monocular Depth Estimation with Lightweight 3D Point Fusion","created":"2020-12-18","cited_by_count":14},{"id":"1805.01556","title":"Pixel-wise Attentional Gating for Parsimonious Pixel Labeling","created":"2018-05-03","cited_by_count":12},{"id":"2101.02843","title":"Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction","created":"2021-01-08","cited_by_count":8},{"id":"2006.10841","title":"Learning non-rigid surface reconstruction from spatio-temporal image patches","created":"2020-06-18","cited_by_count":2}],"next_cursor":null}