{"list":"other","total":62,"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":"1903.11027","title":"nuScenes: A multimodal dataset for autonomous driving","created":"2019-03-26","cited_by_count":4523},{"id":"1706.02413","title":"PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space","created":"2017-06-07","cited_by_count":4401},{"id":"1904.08755","title":"4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks","created":"2019-04-18","cited_by_count":1208},{"id":"1904.08889","title":"KPConv: Flexible and Deformable Convolution for Point Clouds","created":"2019-04-18","cited_by_count":1205},{"id":"1711.03213","title":"CyCADA: Cycle-Consistent Adversarial Domain Adaptation","created":"2017-11-08","cited_by_count":1082},{"id":"1711.10275","title":"3D Semantic Segmentation with Submanifold Sparse Convolutional Networks","created":"2017-11-28","cited_by_count":748},{"id":"1802.03601","title":"Deep Visual Domain Adaptation: A Survey","created":"2018-02-10","cited_by_count":635},{"id":"1811.12833","title":"ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation","created":"2018-11-30","cited_by_count":623},{"id":"1911.11236","title":"RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds","created":"2019-11-25","cited_by_count":585},{"id":"1802.10349","title":"Learning to Adapt Structured Output Space for Semantic Segmentation","created":"2018-02-28","cited_by_count":584},{"id":"1912.12033","title":"Deep Learning for 3D Point Clouds: A Survey","created":"2019-12-27","cited_by_count":558},{"id":"1701.04128","title":"Understanding the Effective Receptive Field in Deep Convolutional Neural Networks","created":"2017-01-15","cited_by_count":553},{"id":"2007.16100","title":"Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution","created":"2020-07-31","cited_by_count":423},{"id":"2011.10033","title":"Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation","created":"2020-11-19","cited_by_count":389},{"id":"1611.05009","title":"OctNet: Learning Deep 3D Representations at High Resolutions","created":"2016-11-15","cited_by_count":342},{"id":"1605.06457","title":"Virtual Worlds as Proxy for Multi-Object Tracking Analysis","created":"2016-05-20","cited_by_count":341},{"id":"1904.10620","title":"Bidirectional Learning for Domain Adaptation of Semantic Segmentation","created":"2019-04-24","cited_by_count":308},{"id":"2111.14887","title":"DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation","created":"2021-11-29","cited_by_count":300},{"id":"2003.14032","title":"PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation","created":"2020-03-31","cited_by_count":275},{"id":"2101.10979","title":"Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation","created":"2021-01-26","cited_by_count":268},{"id":"2301.04856","title":"Multimodal Deep Learning","created":"2023-01-12","cited_by_count":258},{"id":"1612.02649","title":"FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation","created":"2016-12-08","cited_by_count":243},{"id":"1809.08495","title":"SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud","created":"2018-09-22","cited_by_count":228},{"id":"2004.06320","title":"A2D2: Audi Autonomous Driving Dataset","created":"2020-04-14","cited_by_count":190},{"id":"1703.09438","title":"Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs","created":"2017-03-28","cited_by_count":189},{"id":"1712.00479","title":"Image to Image Translation for Domain Adaptation","created":"2017-12-01","cited_by_count":171},{"id":"2204.13132","title":"HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation","created":"2022-04-27","cited_by_count":164},{"id":"2207.04397","title":"2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds","created":"2022-07-10","cited_by_count":158},{"id":"2003.00867","title":"Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation","created":"2020-03-02","cited_by_count":153},{"id":"1901.05427","title":"Domain Adaptation for Structured Output via Discriminative Patch Representations","created":"2019-01-16","cited_by_count":150},{"id":"2007.09222","title":"Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation","created":"2020-07-17","cited_by_count":142},{"id":"1911.12676","title":"xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation","created":"2019-11-28","cited_by_count":129},{"id":"1909.00589","title":"Self-Ensembling with GAN-based Data Augmentation for Domain Adaptation in Semantic Segmentation","created":"2019-09-02","cited_by_count":126},{"id":"2003.08040","title":"Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation","created":"2020-03-18","cited_by_count":126},{"id":"1812.05040","title":"Learning Semantic Segmentation from Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach","created":"2018-12-12","cited_by_count":119},{"id":"2007.08488","title":"Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds","created":"2020-07-16","cited_by_count":116},{"id":"1903.12212","title":"All about Structure: Adapting Structural Information across Domains for Boosting Semantic Segmentation","created":"2019-03-26","cited_by_count":104},{"id":"1804.05827","title":"DCAN: Dual Channel-wise Alignment Networks for Unsupervised Scene Adaptation","created":"2018-04-16","cited_by_count":101},{"id":"1904.01886","title":"DADA: Depth-aware Domain Adaptation in Semantic Segmentation","created":"2019-04-03","cited_by_count":97},{"id":"2204.12667","title":"MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation","created":"2022-04-27","cited_by_count":76},{"id":"1804.08286","title":"Fully Convolutional Adaptation Networks for Semantic Segmentation","created":"2018-04-23","cited_by_count":75},{"id":"1808.03833","title":"Self-Supervised Model Adaptation for Multimodal Semantic Segmentation","created":"2018-08-11","cited_by_count":73},{"id":"1711.10288","title":"Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation","created":"2017-11-28","cited_by_count":71},{"id":"2009.03456","title":"ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation","created":"2020-09-07","cited_by_count":56},{"id":"2207.09778","title":"CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation","created":"2022-07-20","cited_by_count":54},{"id":"1912.08954","title":"An Adversarial Perturbation Oriented Domain Adaptation Approach for Semantic Segmentation","created":"2019-12-18","cited_by_count":48},{"id":"2107.14724","title":"Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation","created":"2021-07-30","cited_by_count":46},{"id":"2101.07253","title":"Cross-modal Learning for Domain Adaptation in 3D Semantic Segmentation","created":"2021-01-18","cited_by_count":40},{"id":"1905.08955","title":"Domain Adaptation for Vehicle Detection from Bird's Eye View LiDAR Point Cloud Data","created":"2019-05-22","cited_by_count":35},{"id":"1912.08487","title":"FuseSeg: LiDAR Point Cloud Segmentation Fusing Multi-Modal Data","created":"2019-12-18","cited_by_count":33},{"id":"1910.10563","title":"Domain Bridge for Unpaired Image-to-Image Translation and Unsupervised Domain Adaptation","created":"2019-10-23","cited_by_count":32},{"id":"2105.07830","title":"Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation","created":"2021-05-17","cited_by_count":32},{"id":"2010.12239","title":"Domain Adaptation in LiDAR Semantic Segmentation by Aligning Class Distributions","created":"2020-10-23","cited_by_count":17},{"id":"1904.04744","title":"Learning Across Tasks and Domains","created":"2019-04-09","cited_by_count":16},{"id":"2110.06685","title":"Plugging Self-Supervised Monocular Depth into Unsupervised Domain Adaptation for Semantic Segmentation","created":"2021-10-13","cited_by_count":13},{"id":"2110.02833","title":"Shallow Features Guide Unsupervised Domain Adaptation for Semantic Segmentation at Class Boundaries","created":"2021-10-06","cited_by_count":10},{"id":"1810.05852","title":"Exploiting Semantics in Adversarial Training for Image-Level Domain Adaptation","created":"2018-10-13","cited_by_count":9},{"id":"1812.04351","title":"Multichannel Semantic Segmentation with Unsupervised Domain Adaptation","created":"2018-12-11","cited_by_count":4},{"id":"1907.02149","title":"Analyzing the Cross-Sensor Portability of Neural Network Architectures for LiDAR-based Semantic Labeling","created":"2019-07-03","cited_by_count":4},{"id":"2301.11310","title":"Learning Good Features to Transfer Across Tasks and Domains","created":"2023-01-26","cited_by_count":2}],"next_cursor":null}