{"list":"other","total":59,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"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":"1907.11692","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","created":"2019-07-26","cited_by_count":13372},{"id":"1506.01497","title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","created":"2015-06-04","cited_by_count":9639},{"id":"2005.12872","title":"End-to-End Object Detection with Transformers","created":"2020-05-26","cited_by_count":7414},{"id":"1612.03144","title":"Feature Pyramid Networks for Object Detection","created":"2016-12-09","cited_by_count":5036},{"id":"1709.01507","title":"Squeeze-and-Excitation Networks","created":"2017-09-05","cited_by_count":4789},{"id":"1604.01685","title":"The Cityscapes Dataset for Semantic Urban Scene Understanding","created":"2016-04-06","cited_by_count":4444},{"id":"2010.04159","title":"Deformable DETR: Deformable Transformers for End-to-End Object Detection","created":"2020-10-08","cited_by_count":3422},{"id":"1504.08083","title":"Fast R-CNN","created":"2015-04-30","cited_by_count":2933},{"id":"1807.06521","title":"CBAM: Convolutional Block Attention Module","created":"2018-07-17","cited_by_count":2477},{"id":"1711.07971","title":"Non-local Neural Networks","created":"2017-11-21","cited_by_count":2293},{"id":"1311.2524","title":"Rich feature hierarchies for accurate object detection and semantic segmentation","created":"2013-11-11","cited_by_count":2162},{"id":"1409.7495","title":"Unsupervised Domain Adaptation by Backpropagation","created":"2014-09-26","cited_by_count":1917},{"id":"2011.00931","title":"Point Transformer","created":"2020-11-02","cited_by_count":1470},{"id":"2012.15840","title":"Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers","created":"2020-12-31","cited_by_count":1369},{"id":"1707.01083","title":"ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices","created":"2017-07-04","cited_by_count":1200},{"id":"1807.11164","title":"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design","created":"2018-07-30","cited_by_count":1136},{"id":"2101.11986","title":"Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet","created":"2021-01-28","cited_by_count":1101},{"id":"1712.02560","title":"Maximum Classifier Discrepancy for Unsupervised Domain Adaptation","created":"2017-12-07","cited_by_count":683},{"id":"1811.12833","title":"ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation","created":"2018-11-30","cited_by_count":623},{"id":"1910.03151","title":"ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks","created":"2019-10-08","cited_by_count":575},{"id":"2004.05498","title":"FDA: Fourier Domain Adaptation for Semantic Segmentation","created":"2020-04-11","cited_by_count":541},{"id":"2103.10697","title":"ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases","created":"2021-03-19","cited_by_count":484},{"id":"2004.08955","title":"ResNeSt: Split-Attention Networks","created":"2020-04-19","cited_by_count":445},{"id":"1803.03243","title":"Domain Adaptive Faster R-CNN for Object Detection in the Wild","created":"2018-03-08","cited_by_count":443},{"id":"1908.09822","title":"Confidence Regularized Self-Training","created":"2019-08-26","cited_by_count":419},{"id":"1708.07819","title":"Semantic Foggy Scene Understanding with Synthetic Data","created":"2017-08-25","cited_by_count":403},{"id":"1904.11492","title":"GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond","created":"2019-04-25","cited_by_count":331},{"id":"1812.04798","title":"Strong-Weak Distribution Alignment for Adaptive Object Detection","created":"2018-12-12","cited_by_count":270},{"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":"1803.11365","title":"Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation","created":"2018-03-30","cited_by_count":218},{"id":"1610.01983","title":"Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?","created":"2016-10-06","cited_by_count":194},{"id":"2103.02370","title":"FSDR: Frequency Space Domain Randomization for Domain Generalization","created":"2021-03-03","cited_by_count":164},{"id":"2003.00707","title":"Unbiased Mean Teacher for Cross-domain Object Detection","created":"2020-03-02","cited_by_count":159},{"id":"1910.11319","title":"Progressive Domain Adaptation for Object Detection","created":"2019-10-24","cited_by_count":134},{"id":"2003.09152","title":"Exploring Categorical Regularization for Domain Adaptive Object Detection","created":"2020-03-20","cited_by_count":131},{"id":"2003.06297","title":"Harmonizing Transferability and Discriminability for Adapting Object Detectors","created":"2020-03-13","cited_by_count":126},{"id":"1905.05396","title":"Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection","created":"2019-05-14","cited_by_count":123},{"id":"2009.14082","title":"Attentional Feature Fusion","created":"2020-09-29","cited_by_count":115},{"id":"1907.10343","title":"Multi-adversarial Faster-RCNN for Unrestricted Object Detection","created":"2019-07-24","cited_by_count":112},{"id":"2003.12849","title":"Cross-domain Detection via Graph-induced Prototype Alignment","created":"2020-03-28","cited_by_count":109},{"id":"2103.04224","title":"MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection","created":"2021-03-07","cited_by_count":106},{"id":"2003.10275","title":"Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation","created":"2020-03-23","cited_by_count":82},{"id":"1711.08995","title":"Unsupervised Domain Adaptation with Similarity Learning","created":"2017-11-24","cited_by_count":73},{"id":"2007.02424","title":"Contextual-Relation Consistent Domain Adaptation for Semantic Segmentation","created":"2020-07-05","cited_by_count":70},{"id":"2103.00236","title":"Uncertainty-Aware Unsupervised Domain Adaptation in Object Detection","created":"2021-02-27","cited_by_count":60},{"id":"2003.12979","title":"Spatial Attention Pyramid Network for Unsupervised Domain Adaptation","created":"2020-03-29","cited_by_count":56},{"id":"2009.08119","title":"Collaborative Training between Region Proposal Localization and Classification for Domain Adaptive Object Detection","created":"2020-09-17","cited_by_count":44},{"id":"2103.02584","title":"Cross-View Regularization for Domain Adaptive Panoptic Segmentation","created":"2021-03-03","cited_by_count":41},{"id":"1911.02559","title":"SCL: Towards Accurate Domain Adaptive Object Detection via Gradient Detach Based Stacked Complementary Losses","created":"2019-11-06","cited_by_count":36},{"id":"1905.09646","title":"Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks","created":"2019-05-23","cited_by_count":33},{"id":"1907.08719","title":"Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to Night","created":"2019-07-19","cited_by_count":30},{"id":"1810.00345","title":"Pixel and Feature Level Based Domain Adaption for Object Detection in Autonomous Driving","created":"2018-09-30","cited_by_count":18},{"id":"1911.07158","title":"Unsupervised Domain Adaptation for Object Detection via Cross-Domain Semi-Supervised Learning","created":"2019-11-17","cited_by_count":17},{"id":"1611.08998","title":"DeepSetNet: Predicting Sets with Deep Neural Networks","created":"2016-11-28","cited_by_count":15}],"next_cursor":null}