{"list":"other","total":52,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1505.04597","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","created":"2015-05-18","cited_by_count":19713},{"id":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"1506.01497","title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","created":"2015-06-04","cited_by_count":9639},{"id":"1703.06870","title":"Mask R-CNN","created":"2017-03-20","cited_by_count":7277},{"id":"1512.00567","title":"Rethinking the Inception Architecture for Computer Vision","created":"2015-12-02","cited_by_count":5475},{"id":"1411.4038","title":"Fully Convolutional Networks for Semantic Segmentation","created":"2014-11-14","cited_by_count":4859},{"id":"1506.02640","title":"You Only Look Once: Unified, Real-Time Object Detection","created":"2015-06-08","cited_by_count":4852},{"id":"1801.04381","title":"MobileNetV2: Inverted Residuals and Linear Bottlenecks","created":"2018-01-13","cited_by_count":4795},{"id":"1709.01507","title":"Squeeze-and-Excitation Networks","created":"2017-09-05","cited_by_count":4789},{"id":"1409.4842","title":"Going Deeper with Convolutions","created":"2014-09-17","cited_by_count":4241},{"id":"1802.02611","title":"Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation","created":"2018-02-07","cited_by_count":3223},{"id":"1504.08083","title":"Fast R-CNN","created":"2015-04-30","cited_by_count":2933},{"id":"1804.02767","title":"YOLOv3: An Incremental Improvement","created":"2018-04-08","cited_by_count":2659},{"id":"1612.01105","title":"Pyramid Scene Parsing Network","created":"2016-12-04","cited_by_count":2479},{"id":"1311.2524","title":"Rich feature hierarchies for accurate object detection and semantic segmentation","created":"2013-11-11","cited_by_count":2162},{"id":"1706.05587","title":"Rethinking Atrous Convolution for Semantic Image Segmentation","created":"2017-06-17","cited_by_count":2161},{"id":"2004.10934","title":"YOLOv4: Optimal Speed and Accuracy of Object Detection","created":"2020-04-23","cited_by_count":1630},{"id":"1511.00561","title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","created":"2015-11-02","cited_by_count":1487},{"id":"1612.08242","title":"YOLO9000: Better, Faster, Stronger","created":"2016-12-25","cited_by_count":1431},{"id":"1807.11164","title":"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design","created":"2018-07-30","cited_by_count":1136},{"id":"1711.03213","title":"CyCADA: Cycle-Consistent Adversarial Domain Adaptation","created":"2017-11-08","cited_by_count":1082},{"id":"1807.11626","title":"MnasNet: Platform-Aware Neural Architecture Search for Mobile","created":"2018-07-31","cited_by_count":943},{"id":"1710.11063","title":"Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks","created":"2017-10-30","cited_by_count":939},{"id":"1404.5997","title":"One weird trick for parallelizing convolutional neural networks","created":"2014-04-23","cited_by_count":264},{"id":"1907.05740","title":"Gated-SCNN: Gated Shape CNNs for Semantic Segmentation","created":"2019-07-12","cited_by_count":166},{"id":"1803.06791","title":"Depth-aware CNN for RGB-D Segmentation","created":"2018-03-19","cited_by_count":74},{"id":"2008.11351","title":"SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection","created":"2020-08-26","cited_by_count":66},{"id":"1908.00894","title":"Pothole Detection Based on Disparity Transformation and Road Surface Modeling","created":"2019-08-02","cited_by_count":38},{"id":"1807.01874","title":"Road surface 3d reconstruction based on dense subpixel disparity map estimation","created":"2018-07-05","cited_by_count":34},{"id":"1904.08582","title":"Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding","created":"2019-04-18","cited_by_count":33},{"id":"2103.07094","title":"PVStereo: Pyramid Voting Module for End-to-End Self-Supervised Stereo Matching","created":"2021-03-12","cited_by_count":28},{"id":"2012.10802","title":"Rethinking Road Surface 3D Reconstruction and Pothole Detection: From Perspective Transformation to Disparity Map Segmentation","created":"2020-12-19","cited_by_count":27},{"id":"1910.04988","title":"Road Damage Detection Based on Unsupervised Disparity Map Segmentation","created":"2019-10-11","cited_by_count":24},{"id":"2103.02433","title":"Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and Algorithms","created":"2021-03-03","cited_by_count":23},{"id":"2107.14599","title":"SNE-RoadSeg+: Rethinking Depth-Normal Translation and Deep Supervision for Freespace Detection","created":"2021-07-30","cited_by_count":23},{"id":"2008.11383","title":"Applying Surface Normal Information in Drivable Area and Road Anomaly Detection for Ground Mobile Robots","created":"2020-08-26","cited_by_count":20},{"id":"2012.07890","title":"Learning Collision-Free Space Detection from Stereo Images: Homography Matrix Brings Better Data Augmentation","created":"2020-12-14","cited_by_count":19},{"id":"2104.08862","title":"End-to-End Interactive Prediction and Planning with Optical Flow Distillation for Autonomous Driving","created":"2021-04-18","cited_by_count":17},{"id":"2012.03194","title":"Computer Stereo Vision for Autonomous Driving","created":"2020-12-06","cited_by_count":15},{"id":"2109.02711","title":"Graph Attention Layer Evolves Semantic Segmentation for Road Pothole Detection: A Benchmark and Algorithms","created":"2021-09-06","cited_by_count":15},{"id":"2005.08165","title":"Three-Filters-to-Normal: An Accurate and Ultrafast Surface Normal Estimator","created":"2020-05-17","cited_by_count":14},{"id":"2008.06840","title":"We Learn Better Road Pothole Detection: from Attention Aggregation to Adversarial Domain Adaptation","created":"2020-08-16","cited_by_count":12},{"id":"2011.02156","title":"CoT-AMFlow: Adaptive Modulation Network with Co-Teaching Strategy for Unsupervised Optical Flow Estimation","created":"2020-11-04","cited_by_count":10},{"id":"1904.06017","title":"Real-Time Dense Stereo Embedded in A UAV for Road Inspection","created":"2019-04-12","cited_by_count":7},{"id":"1807.07433","title":"Real-Time Stereo Vision for Road Surface 3-D Reconstruction","created":"2018-07-18","cited_by_count":5},{"id":"2009.13851","title":"Loop-box: Multi-Agent Direct SLAM Triggered by Single Loop Closure for Large-Scale Mapping","created":"2020-09-29","cited_by_count":4},{"id":"1807.02752","title":"Real-time stereo vision-based lane detection system","created":"2018-07-08","cited_by_count":3},{"id":"2112.13082","title":"Multi-Scale Feature Fusion: Learning Better Semantic Segmentation for Road Pothole Detection","created":"2021-12-24","cited_by_count":3},{"id":"1808.02837","title":"A Novel Disparity Transformation Algorithm for Road Segmentation","created":"2018-08-08","cited_by_count":2},{"id":"1807.02044","title":"Real-Time Subpixel Fast Bilateral Stereo","created":"2018-07-05","cited_by_count":1},{"id":"1906.01894","title":"A Robust Roll Angle Estimation Algorithm Based on Gradient Descent","created":"2019-06-05","cited_by_count":1}],"next_cursor":null}