{"list":"other","total":68,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"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":"2103.14030","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","created":"2021-03-25","cited_by_count":12139},{"id":"2005.12872","title":"End-to-End Object Detection with Transformers","created":"2020-05-26","cited_by_count":7414},{"id":"2111.06377","title":"Masked Autoencoders Are Scalable Vision Learners","created":"2021-11-11","cited_by_count":7403},{"id":"1709.01507","title":"Squeeze-and-Excitation Networks","created":"2017-09-05","cited_by_count":4789},{"id":"2012.12877","title":"Training data-efficient image transformers & distillation through attention","created":"2020-12-23","cited_by_count":4586},{"id":"2010.04159","title":"Deformable DETR: Deformable Transformers for End-to-End Object Detection","created":"2020-10-08","cited_by_count":3422},{"id":"1311.2901","title":"Visualizing and Understanding Convolutional Networks","created":"2013-11-12","cited_by_count":2290},{"id":"2102.04306","title":"TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation","created":"2021-02-08","cited_by_count":1558},{"id":"1604.07379","title":"Context Encoders: Feature Learning by Inpainting","created":"2016-04-25","cited_by_count":1428},{"id":"2202.04200","title":"MaskGIT: Masked Generative Image Transformer","created":"2022-02-08","cited_by_count":1099},{"id":"2101.01169","title":"Transformers in Vision: A Survey","created":"2021-01-04","cited_by_count":1013},{"id":"2103.15808","title":"CvT: Introducing Convolutions to Vision Transformers","created":"2021-03-29","cited_by_count":947},{"id":"2106.08265","title":"Towards Total Recall in Industrial Anomaly Detection","created":"2021-06-15","cited_by_count":776},{"id":"1802.05751","title":"Image Transformer","created":"2018-02-15","cited_by_count":772},{"id":"1710.09829","title":"Dynamic Routing Between Capsules","created":"2017-10-26","cited_by_count":689},{"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":"2011.08785","title":"PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization","created":"2020-11-17","cited_by_count":594},{"id":"1801.04264","title":"Real-world Anomaly Detection in Surveillance Videos","created":"2018-01-12","cited_by_count":577},{"id":"2112.09133","title":"Masked Feature Prediction for Self-Supervised Visual Pre-Training","created":"2021-12-16","cited_by_count":576},{"id":"1904.02639","title":"Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection","created":"2019-04-04","cited_by_count":564},{"id":"2104.04015","title":"CutPaste: Self-Supervised Learning for Anomaly Detection and Localization","created":"2021-04-08","cited_by_count":531},{"id":"2108.07610","title":"DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection","created":"2021-08-17","cited_by_count":514},{"id":"2110.02642","title":"Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy","created":"2021-10-06","cited_by_count":394},{"id":"1712.09867","title":"Future Frame Prediction for Anomaly Detection -- A New Baseline","created":"2017-12-28","cited_by_count":391},{"id":"1911.02357","title":"Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings","created":"2019-11-06","cited_by_count":386},{"id":"2011.11108","title":"Multiresolution Knowledge Distillation for Anomaly Detection","created":"2020-11-22","cited_by_count":285},{"id":"2003.13228","title":"Learning Memory-guided Normality for Anomaly Detection","created":"2020-03-30","cited_by_count":278},{"id":"1604.04574","title":"Learning Temporal Regularity in Video Sequences","created":"2016-04-15","cited_by_count":254},{"id":"2104.10036","title":"VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization","created":"2021-04-20","cited_by_count":223},{"id":"2008.12577","title":"Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows","created":"2020-08-28","cited_by_count":212},{"id":"2112.10762","title":"StyleSwin: Transformer-based GAN for High-resolution Image Generation","created":"2021-12-20","cited_by_count":152},{"id":"2011.07491","title":"Anomaly Detection in Video via Self-Supervised and Multi-Task Learning","created":"2020-11-15","cited_by_count":148},{"id":"2206.04325","title":"CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localization","created":"2022-06-09","cited_by_count":135},{"id":"2108.06852","title":"A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction","created":"2021-08-16","cited_by_count":129},{"id":"2111.09099","title":"Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection","created":"2021-11-17","cited_by_count":119},{"id":"1908.06351","title":"Anomaly Detection in Video Sequence with Appearance-Motion Correspondence","created":"2019-08-17","cited_by_count":118},{"id":"2109.15222","title":"Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization","created":"2021-09-30","cited_by_count":116},{"id":"1812.04960","title":"Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video","created":"2018-12-11","cited_by_count":111},{"id":"2011.09315","title":"End-to-End Object Detection with Adaptive Clustering Transformer","created":"2020-11-18","cited_by_count":109},{"id":"2111.08644","title":"UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection","created":"2021-11-16","cited_by_count":93},{"id":"2104.13897","title":"Inpainting Transformer for Anomaly Detection","created":"2021-04-28","cited_by_count":91},{"id":"1902.05872","title":"Street Scene: A new dataset and evaluation protocol for video anomaly detection","created":"2019-02-15","cited_by_count":90},{"id":"2004.05993","title":"A Survey of Single-Scene Video Anomaly Detection","created":"2020-04-13","cited_by_count":88},{"id":"2008.12328","title":"A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video","created":"2020-08-27","cited_by_count":86},{"id":"2003.06780","title":"Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection","created":"2020-03-15","cited_by_count":83},{"id":"1708.09644","title":"Abnormal Event Detection in Videos using Generative Adversarial Nets","created":"2017-08-31","cited_by_count":82},{"id":"2001.00294","title":"Video Cloze Procedure for Self-Supervised Spatio-Temporal Learning","created":"2020-01-02","cited_by_count":81},{"id":"2008.11988","title":"Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events","created":"2020-08-27","cited_by_count":70},{"id":"1709.09121","title":"Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledge","created":"2017-09-26","cited_by_count":55},{"id":"1609.00866","title":"Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes","created":"2016-09-03","cited_by_count":50},{"id":"1705.08182","title":"Unmasking the abnormal events in video","created":"2017-05-23","cited_by_count":48},{"id":"2207.08003","title":"SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection","created":"2022-07-16","cited_by_count":45},{"id":"2004.02072","title":"Any-Shot Sequential Anomaly Detection in Surveillance Videos","created":"2020-04-05","cited_by_count":34},{"id":"1610.00307","title":"Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection","created":"2016-10-02","cited_by_count":32},{"id":"2001.09189","title":"Learning a distance function with a Siamese network to localize anomalies in videos","created":"2020-01-24","cited_by_count":31},{"id":"2110.09742","title":"Learning Not to Reconstruct Anomalies","created":"2021-10-19","cited_by_count":31},{"id":"1801.05030","title":"Detecting abnormal events in video using Narrowed Normality Clusters","created":"2018-01-12","cited_by_count":30},{"id":"2006.13265","title":"Anomaly Detection in Medical Imaging with Deep Perceptual Autoencoders","created":"2020-06-23","cited_by_count":30},{"id":"2203.10808","title":"AnoViT: Unsupervised Anomaly Detection and Localization with Vision Transformer-based Encoder-Decoder","created":"2022-03-21","cited_by_count":29},{"id":"1811.06861","title":"Anomaly Detection using Deep Learning based Image Completion","created":"2018-11-16","cited_by_count":27},{"id":"2110.09768","title":"Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection","created":"2021-10-19","cited_by_count":26},{"id":"1609.08938","title":"A Discriminative Framework for Anomaly Detection in Large Videos","created":"2016-09-28","cited_by_count":25},{"id":"1805.09521","title":"AVID: Adversarial Visual Irregularity Detection","created":"2018-05-24","cited_by_count":19},{"id":"2202.05152","title":"Feature-level augmentation to improve robustness of deep neural networks to affine transformations","created":"2022-02-10","cited_by_count":4},{"id":"2104.14430","title":"Discriminative-Generative Dual Memory Video Anomaly Detection","created":"2021-04-29","cited_by_count":2},{"id":"2108.07387","title":"Contextual Convolutional Neural Networks","created":"2021-08-17","cited_by_count":2}],"next_cursor":null}