{"list":"other","total":53,"rows":[{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"1405.0312","title":"Microsoft COCO: Common Objects in Context","created":"2014-05-01","cited_by_count":17608},{"id":"1912.04838","title":"Scalability in Perception for Autonomous Driving: Waymo Open Dataset","created":"2019-12-10","cited_by_count":2451},{"id":"1812.05784","title":"PointPillars: Fast Encoders for Object Detection from Point Clouds","created":"2018-12-14","cited_by_count":1715},{"id":"1711.06396","title":"VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection","created":"2017-11-17","cited_by_count":1323},{"id":"2006.11275","title":"Center-based 3D Object Detection and Tracking","created":"2020-06-19","cited_by_count":1144},{"id":"1812.04244","title":"PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud","created":"2018-12-11","cited_by_count":941},{"id":"1912.13192","title":"PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection","created":"2019-12-31","cited_by_count":891},{"id":"1803.09050","title":"Learning to Reweight Examples for Robust Deep Learning","created":"2018-03-24","cited_by_count":675},{"id":"1904.08189","title":"CenterNet: Keypoint Triplets for Object Detection","created":"2019-04-17","cited_by_count":654},{"id":"1712.05055","title":"MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels","created":"2017-12-14","cited_by_count":653},{"id":"1904.09664","title":"Deep Hough Voting for 3D Object Detection in Point Clouds","created":"2019-04-21","cited_by_count":648},{"id":"1711.08488","title":"Frustum PointNets for 3D Object Detection from RGB-D Data","created":"2017-11-22","cited_by_count":640},{"id":"1604.03540","title":"Training Region-based Object Detectors with Online Hard Example Mining","created":"2016-04-12","cited_by_count":579},{"id":"2002.10187","title":"3DSSD: Point-based 3D Single Stage Object Detector","created":"2020-02-24","cited_by_count":440},{"id":"2012.15712","title":"Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection","created":"2020-12-31","cited_by_count":410},{"id":"2006.04388","title":"Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection","created":"2020-06-08","cited_by_count":409},{"id":"1907.03670","title":"From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network","created":"2019-07-08","cited_by_count":368},{"id":"2003.01251","title":"Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud","created":"2020-03-02","cited_by_count":335},{"id":"2103.06627","title":"MagFace: A Universal Representation for Face Recognition and Quality Assessment","created":"2021-03-11","cited_by_count":334},{"id":"1712.02294","title":"Joint 3D Proposal Generation and Object Detection from View Aggregation","created":"2017-12-06","cited_by_count":326},{"id":"2004.00288","title":"CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition","created":"2020-04-01","cited_by_count":321},{"id":"1904.03626","title":"On The Power of Curriculum Learning in Training Deep Networks","created":"2019-04-07","cited_by_count":286},{"id":"1907.10471","title":"STD: Sparse-to-Dense 3D Object Detector for Point Cloud","created":"2019-07-22","cited_by_count":282},{"id":"2101.10382","title":"Curriculum Learning: A Survey","created":"2021-01-25","cited_by_count":259},{"id":"2109.02497","title":"Voxel Transformer for 3D Object Detection","created":"2021-09-06","cited_by_count":247},{"id":"2102.00463","title":"PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection","created":"2021-01-31","cited_by_count":232},{"id":"2008.13367","title":"VarifocalNet: An IoU-aware Dense Object Detector","created":"2020-08-31","cited_by_count":231},{"id":"2112.06375","title":"Embracing Single Stride 3D Object Detector with Sparse Transformer","created":"2021-12-13","cited_by_count":193},{"id":"2103.05346","title":"ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection","created":"2021-03-09","cited_by_count":159},{"id":"1704.07433","title":"Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples","created":"2017-04-24","cited_by_count":147},{"id":"2103.10039","title":"RangeDet:In Defense of Range View for LiDAR-based 3D Object Detection","created":"2021-03-18","cited_by_count":140},{"id":"1903.08701","title":"LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving","created":"2019-03-20","cited_by_count":133},{"id":"2203.11139","title":"Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds","created":"2022-03-21","cited_by_count":131},{"id":"2103.15297","title":"LiDAR R-CNN: An Efficient and Universal 3D Object Detector","created":"2021-03-29","cited_by_count":129},{"id":"2110.02210","title":"Mix3D: Out-of-Context Data Augmentation for 3D Scenes","created":"2021-10-05","cited_by_count":126},{"id":"1908.02990","title":"Fast Point R-CNN","created":"2019-08-08","cited_by_count":119},{"id":"2106.13365","title":"RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection","created":"2021-06-25","cited_by_count":115},{"id":"2211.16231","title":"Curriculum Temperature for Knowledge Distillation","created":"2022-11-29","cited_by_count":112},{"id":"2005.09927","title":"Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection","created":"2020-05-20","cited_by_count":78},{"id":"2208.00223","title":"PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds","created":"2022-07-30","cited_by_count":74},{"id":"1707.09533","title":"Curriculum Learning and Minibatch Bucketing in Neural Machine Translation","created":"2017-07-29","cited_by_count":63},{"id":"1905.05816","title":"Curriculum Learning for Domain Adaptation in Neural Machine Translation","created":"2019-05-14","cited_by_count":59},{"id":"1811.00739","title":"An Empirical Exploration of Curriculum Learning for Neural Machine Translation","created":"2018-11-02","cited_by_count":58},{"id":"2112.02205","title":"Behind the Curtain: Learning Occluded Shapes for 3D Object Detection","created":"2021-12-04","cited_by_count":58},{"id":"1812.05276","title":"IPOD: Intensive Point-based Object Detector for Point Cloud","created":"2018-12-13","cited_by_count":46},{"id":"2208.03624","title":"Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph","created":"2022-08-07","cited_by_count":42},{"id":"1911.06849","title":"Curriculum Self-Paced Learning for Cross-Domain Object Detection","created":"2019-11-15","cited_by_count":39},{"id":"2106.13381","title":"To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernels","created":"2021-06-25","cited_by_count":39},{"id":"2110.05379","title":"Point Cloud Augmentation with Weighted Local Transformations","created":"2021-10-11","cited_by_count":39},{"id":"2103.07552","title":"Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning","created":"2021-03-12","cited_by_count":35},{"id":"2208.08110","title":"PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation","created":"2022-08-17","cited_by_count":14},{"id":"2010.15434","title":"Self-paced Data Augmentation for Training Neural Networks","created":"2020-10-29","cited_by_count":5}],"next_cursor":null}