{"list":"other","total":53,"rows":[{"id":"1610.02391","title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization","created":"2016-10-07","cited_by_count":6733},{"id":"1612.00593","title":"PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation","created":"2016-12-02","cited_by_count":5248},{"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":"1801.07829","title":"Dynamic Graph CNN for Learning on Point Clouds","created":"2018-01-24","cited_by_count":2223},{"id":"2004.07780","title":"Shortcut Learning in Deep Neural Networks","created":"2020-04-16","cited_by_count":1969},{"id":"1907.07174","title":"Natural Adversarial Examples","created":"2019-07-16","cited_by_count":1408},{"id":"1705.07204","title":"Ensemble Adversarial Training: Attacks and Defenses","created":"2017-05-19","cited_by_count":840},{"id":"2012.09688","title":"PCT: Point cloud transformer","created":"2020-12-17","cited_by_count":732},{"id":"1611.07759","title":"Multi-View 3D Object Detection Network for Autonomous Driving","created":"2016-11-23","cited_by_count":724},{"id":"2111.14819","title":"Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling","created":"2021-11-29","cited_by_count":653},{"id":"2012.09838","title":"Transformer Interpretability Beyond Attention Visualization","created":"2020-12-17","cited_by_count":627},{"id":"2202.07123","title":"Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework","created":"2022-02-15","cited_by_count":379},{"id":"1911.03584","title":"On the Relationship between Self-Attention and Convolutional Layers","created":"2019-11-08","cited_by_count":300},{"id":"1906.04284","title":"Analyzing the Structure of Attention in a Transformer Language Model","created":"2019-06-07","cited_by_count":243},{"id":"2003.00492","title":"PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling","created":"2020-03-01","cited_by_count":239},{"id":"2103.14635","title":"PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds","created":"2021-03-26","cited_by_count":211},{"id":"2105.01288","title":"Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis","created":"2021-05-04","cited_by_count":172},{"id":"1809.07016","title":"Generating 3D Adversarial Point Clouds","created":"2018-09-19","cited_by_count":144},{"id":"1812.01687","title":"PointCloud Saliency Maps","created":"2018-11-28","cited_by_count":112},{"id":"1901.01060","title":"PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds","created":"2019-01-04","cited_by_count":99},{"id":"1901.03006","title":"Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers","created":"2019-01-10","cited_by_count":92},{"id":"1912.00461","title":"AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds","created":"2019-12-01","cited_by_count":84},{"id":"1609.08546","title":"Shape Completion Enabled Robotic Grasping","created":"2016-09-27","cited_by_count":79},{"id":"1912.03663","title":"SampleNet: Differentiable Point Cloud Sampling","created":"2019-12-08","cited_by_count":74},{"id":"2012.10921","title":"Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud","created":"2020-12-20","cited_by_count":71},{"id":"2202.03377","title":"Benchmarking and Analyzing Point Cloud Classification under Corruptions","created":"2022-02-07","cited_by_count":70},{"id":"2101.01461","title":"PointCutMix: Regularization Strategy for Point Cloud Classification","created":"2021-01-05","cited_by_count":69},{"id":"1902.10899","title":"Adversarial Attack and Defense on Point Sets","created":"2019-02-28","cited_by_count":63},{"id":"2102.01929","title":"Regularization Strategy for Point Cloud via Rigidly Mixed Sample","created":"2021-02-03","cited_by_count":59},{"id":"1904.00923","title":"Robustness of 3D Deep Learning in an Adversarial Setting","created":"2019-04-01","cited_by_count":55},{"id":"2011.00566","title":"LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud-based Deep Networks","created":"2020-11-01","cited_by_count":55},{"id":"1804.00103","title":"A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving","created":"2018-03-31","cited_by_count":54},{"id":"2105.14217","title":"Less is More: Pay Less Attention in Vision Transformers","created":"2021-05-29","cited_by_count":53},{"id":"2203.04041","title":"Shape-invariant 3D Adversarial Point Clouds","created":"2022-03-08","cited_by_count":50},{"id":"2103.03046","title":"PointGuard: Provably Robust 3D Point Cloud Classification","created":"2021-03-04","cited_by_count":49},{"id":"1907.12766","title":"PointHop: An Explainable Machine Learning Method for Point Cloud Classification","created":"2019-07-30","cited_by_count":46},{"id":"2002.12222","title":"On Isometry Robustness of Deep 3D Point Cloud Models under Adversarial Attacks","created":"2020-02-27","cited_by_count":46},{"id":"2010.05272","title":"IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration","created":"2020-10-11","cited_by_count":46},{"id":"2008.12066","title":"Minimal Adversarial Examples for Deep Learning on 3D Point Clouds","created":"2020-08-27","cited_by_count":41},{"id":"2110.05379","title":"Point Cloud Augmentation with Weighted Local Transformations","created":"2021-10-11","cited_by_count":39},{"id":"1710.03348","title":"What does Attention in Neural Machine Translation Pay Attention to?","created":"2017-10-09","cited_by_count":30},{"id":"2005.11626","title":"ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds","created":"2020-05-24","cited_by_count":20},{"id":"2303.11126","title":"Robustifying Token Attention for Vision Transformers","created":"2023-03-20","cited_by_count":19},{"id":"2107.13459","title":"Surrogate Model-Based Explainability Methods for Point Cloud NNs","created":"2021-07-28","cited_by_count":18},{"id":"2006.05548","title":"3D Point Cloud Feature Explanations Using Gradient-Based Methods","created":"2020-06-09","cited_by_count":14},{"id":"2202.11287","title":"LPF-Defense: 3D Adversarial Defense based on Frequency Analysis","created":"2022-02-23","cited_by_count":10},{"id":"2110.04158","title":"Explainability-Aware One Point Attack for Point Cloud Neural Networks","created":"2021-10-08","cited_by_count":7},{"id":"2303.01959","title":"PointCert: Point Cloud Classification with Deterministic Certified Robustness Guarantees","created":"2023-03-03","cited_by_count":6},{"id":"2303.11419","title":"EPiC: Ensemble of Partial Point Clouds for Robust Classification","created":"2023-03-20","cited_by_count":5},{"id":"2112.01050","title":"CloudWalker: Random walks for 3D point cloud shape analysis","created":"2021-12-02","cited_by_count":4},{"id":"2304.00961","title":"Self-Ordering Point Clouds","created":"2023-04-03","cited_by_count":2},{"id":"2209.06971","title":"PointACL:Adversarial Contrastive Learning for Robust Point Clouds Representation under Adversarial Attack","created":"2022-09-14","cited_by_count":1},{"id":"2211.15428","title":"Explanation on Pretraining Bias of Finetuned Vision Transformer","created":"2022-11-18","cited_by_count":1}],"next_cursor":null}