{"list":"other","total":59,"rows":[{"id":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"id":"1911.05722","title":"Momentum Contrast for Unsupervised Visual Representation Learning","created":"2019-11-13","cited_by_count":7513},{"id":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"id":"1706.06083","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","created":"2017-06-19","cited_by_count":6415},{"id":"1312.6199","title":"Intriguing properties of neural networks","created":"2013-12-21","cited_by_count":5155},{"id":"1608.04644","title":"Towards Evaluating the Robustness of Neural Networks","created":"2016-08-16","cited_by_count":2998},{"id":"2011.10566","title":"Exploring Simple Siamese Representation Learning","created":"2020-11-20","cited_by_count":2866},{"id":"1805.01978","title":"Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination","created":"2018-05-05","cited_by_count":1845},{"id":"2003.01690","title":"Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks","created":"2020-03-03","cited_by_count":1451},{"id":"1511.04599","title":"DeepFool: a simple and accurate method to fool deep neural networks","created":"2015-11-14","cited_by_count":1318},{"id":"1906.05849","title":"Contrastive Multiview Coding","created":"2019-06-13","cited_by_count":1301},{"id":"1802.00420","title":"Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples","created":"2018-02-01","cited_by_count":1268},{"id":"1705.07204","title":"Ensemble Adversarial Training: Attacks and Defenses","created":"2017-05-19","cited_by_count":840},{"id":"1609.03677","title":"Unsupervised Monocular Depth Estimation with Left-Right Consistency","created":"2016-09-13","cited_by_count":820},{"id":"1912.01991","title":"Self-Supervised Learning of Pretext-Invariant Representations","created":"2019-12-04","cited_by_count":808},{"id":"1912.00049","title":"Square Attack: a query-efficient black-box adversarial attack via random search","created":"2019-11-29","cited_by_count":685},{"id":"1712.09665","title":"Adversarial Patch","created":"2017-12-27","cited_by_count":517},{"id":"1905.13736","title":"Unlabeled Data Improves Adversarial Robustness","created":"2019-05-31","cited_by_count":416},{"id":"1603.04992","title":"Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue","created":"2016-03-16","cited_by_count":368},{"id":"1803.02276","title":"GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose","created":"2018-03-06","cited_by_count":342},{"id":"1908.10553","title":"Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video","created":"2019-08-28","cited_by_count":244},{"id":"1904.03436","title":"Unsupervised Embedding Learning via Invariant and Spreading Instance Feature","created":"2019-04-06","cited_by_count":232},{"id":"2003.01060","title":"D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry","created":"2020-03-02","cited_by_count":206},{"id":"2104.14540","title":"The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth","created":"2021-04-29","cited_by_count":190},{"id":"1802.07934","title":"Adversarial Learning for Semi-Supervised Semantic Segmentation","created":"2018-02-22","cited_by_count":173},{"id":"1905.13725","title":"Are Labels Required for Improving Adversarial Robustness?","created":"2019-05-31","cited_by_count":163},{"id":"1910.14667","title":"Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors","created":"2019-10-31","cited_by_count":155},{"id":"1712.00175","title":"Learning Depth from Monocular Videos using Direct Methods","created":"2017-12-01","cited_by_count":154},{"id":"1907.06826","title":"Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving","created":"2019-07-16","cited_by_count":152},{"id":"1809.01649","title":"DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency","created":"2018-09-05","cited_by_count":148},{"id":"2006.07589","title":"Adversarial Self-Supervised Contrastive Learning","created":"2020-06-13","cited_by_count":139},{"id":"2004.00543","title":"Physically Realizable Adversarial Examples for LiDAR Object Detection","created":"2020-04-01","cited_by_count":137},{"id":"1909.09051","title":"Self-Supervised Monocular Depth Hints","created":"2019-09-19","cited_by_count":127},{"id":"1711.09856","title":"On the Robustness of Semantic Segmentation Models to Adversarial Attacks","created":"2017-11-27","cited_by_count":126},{"id":"2106.09249","title":"Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks","created":"2021-06-17","cited_by_count":101},{"id":"2010.12050","title":"Contrastive Learning with Adversarial Examples","created":"2020-10-22","cited_by_count":91},{"id":"2103.10284","title":"SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation","created":"2021-03-18","cited_by_count":87},{"id":"2103.06504","title":"Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink","created":"2021-03-11","cited_by_count":75},{"id":"2011.11814","title":"MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera","created":"2020-11-24","cited_by_count":59},{"id":"2207.04718","title":"Physical Attack on Monocular Depth Estimation with Optimal Adversarial Patches","created":"2022-07-11","cited_by_count":57},{"id":"1907.10310","title":"Towards Adversarially Robust Object Detection","created":"2019-07-24","cited_by_count":55},{"id":"1904.07087","title":"Recurrent Neural Network for (Un-)supervised Learning of Monocular VideoVisual Odometry and Depth","created":"2019-04-15","cited_by_count":50},{"id":"2109.12484","title":"Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimation","created":"2021-09-26","cited_by_count":48},{"id":"2101.06784","title":"Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving","created":"2021-01-17","cited_by_count":45},{"id":"2103.13886","title":"Robust and Accurate Object Detection via Adversarial Learning","created":"2021-03-23","cited_by_count":42},{"id":"2004.10681","title":"Pseudo RGB-D for Self-Improving Monocular SLAM and Depth Prediction","created":"2020-04-22","cited_by_count":41},{"id":"2006.08602","title":"Targeted Adversarial Perturbations for Monocular Depth Prediction","created":"2020-06-12","cited_by_count":40},{"id":"2007.10983","title":"Learning Monocular Visual Odometry via Self-Supervised Long-Term Modeling","created":"2020-07-21","cited_by_count":39},{"id":"2003.06555","title":"Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation","created":"2020-03-14","cited_by_count":38},{"id":"2106.10189","title":"Adversarial Training Helps Transfer Learning via Better Representations","created":"2021-06-18","cited_by_count":36},{"id":"2106.02022","title":"Single Image Depth Prediction with Wavelet Decomposition","created":"2021-06-03","cited_by_count":34},{"id":"2012.02366","title":"DenserNet: Weakly Supervised Visual Localization Using Multi-scale Feature Aggregation","created":"2020-12-04","cited_by_count":33},{"id":"1912.08166","title":"APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection","created":"2019-12-17","cited_by_count":31},{"id":"2010.06323","title":"LM-Reloc: Levenberg-Marquardt Based Direct Visual Relocalization","created":"2020-10-13","cited_by_count":28},{"id":"2103.16148","title":"Class-Aware Robust Adversarial Training for Object Detection","created":"2021-03-30","cited_by_count":27},{"id":"2209.06351","title":"DevNet: Self-supervised Monocular Depth Learning via Density Volume Construction","created":"2022-09-14","cited_by_count":22},{"id":"2003.10315","title":"Adversarial Attacks on Monocular Depth Estimation","created":"2020-03-23","cited_by_count":20},{"id":"1911.08790","title":"Analysis of Deep Networks for Monocular Depth Estimation Through Adversarial Attacks with Proposal of a Defense Method","created":"2019-11-20","cited_by_count":10},{"id":"2005.14302","title":"Monocular Depth Estimators: Vulnerabilities and Attacks","created":"2020-05-28","cited_by_count":7}],"next_cursor":null}