{"list":"other","total":70,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"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":"1505.04597","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","created":"2015-05-18","cited_by_count":19713},{"id":"1405.0312","title":"Microsoft COCO: Common Objects in Context","created":"2014-05-01","cited_by_count":17608},{"id":"1908.10084","title":"Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks","created":"2019-08-27","cited_by_count":9170},{"id":"1703.06870","title":"Mask R-CNN","created":"2017-03-20","cited_by_count":7277},{"id":"1612.03144","title":"Feature Pyramid Networks for Object Detection","created":"2016-12-09","cited_by_count":5036},{"id":"1703.03400","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","created":"2017-03-09","cited_by_count":4897},{"id":"1411.4038","title":"Fully Convolutional Networks for Semantic Segmentation","created":"2014-11-14","cited_by_count":4859},{"id":"1506.02640","title":"You Only Look Once: Unified, Real-Time Object Detection","created":"2015-06-08","cited_by_count":4852},{"id":"2012.12877","title":"Training data-efficient image transformers & distillation through attention","created":"2020-12-23","cited_by_count":4586},{"id":"2101.00190","title":"Prefix-Tuning: Optimizing Continuous Prompts for Generation","created":"2021-01-01","cited_by_count":3915},{"id":"1703.05175","title":"Prototypical Networks for Few-shot Learning","created":"2017-03-15","cited_by_count":3015},{"id":"1504.08083","title":"Fast R-CNN","created":"2015-04-30","cited_by_count":2933},{"id":"2112.01527","title":"Masked-attention Mask Transformer for Universal Image Segmentation","created":"2021-12-02","cited_by_count":2351},{"id":"2109.01134","title":"Learning to Prompt for Vision-Language Models","created":"2021-09-02","cited_by_count":2281},{"id":"1606.04080","title":"Matching Networks for One Shot Learning","created":"2016-06-13","cited_by_count":2255},{"id":"2107.13586","title":"Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing","created":"2021-07-28","cited_by_count":2234},{"id":"1603.05027","title":"Identity Mappings in Deep Residual Networks","created":"2016-03-16","cited_by_count":1965},{"id":"2203.12119","title":"Visual Prompt Tuning","created":"2022-03-23","cited_by_count":1681},{"id":"1905.02249","title":"MixMatch: A Holistic Approach to Semi-Supervised Learning","created":"2019-05-06","cited_by_count":1391},{"id":"2012.15723","title":"Making Pre-trained Language Models Better Few-shot Learners","created":"2020-12-31","cited_by_count":1256},{"id":"1911.02685","title":"A Comprehensive Survey on Transfer Learning","created":"2019-11-07","cited_by_count":1160},{"id":"1711.06025","title":"Learning to Compare: Relation Network for Few-Shot Learning","created":"2017-11-16","cited_by_count":1143},{"id":"1803.02999","title":"On First-Order Meta-Learning Algorithms","created":"2018-03-08","cited_by_count":1018},{"id":"2201.03546","title":"Language-driven Semantic Segmentation","created":"2022-01-10","cited_by_count":717},{"id":"2106.13884","title":"Multimodal Few-Shot Learning with Frozen Language Models","created":"2021-06-25","cited_by_count":661},{"id":"2112.10003","title":"Image Segmentation Using Text and Image Prompts","created":"2021-12-18","cited_by_count":512},{"id":"1908.06391","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","created":"2019-08-18","cited_by_count":449},{"id":"1707.09835","title":"Meta-SGD: Learning to Learn Quickly for Few-Shot Learning","created":"2017-07-31","cited_by_count":379},{"id":"1709.03410","title":"One-Shot Learning for Semantic Segmentation","created":"2017-09-11","cited_by_count":337},{"id":"1905.03670","title":"S4L: Self-Supervised Semi-Supervised Learning","created":"2019-05-09","cited_by_count":335},{"id":"1805.06561","title":"DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images","created":"2018-05-17","cited_by_count":277},{"id":"2008.01449","title":"Prior Guided Feature Enrichment Network for Few-Shot Segmentation","created":"2020-08-04","cited_by_count":271},{"id":"1906.00817","title":"Zero-Shot Semantic Segmentation","created":"2019-06-03","cited_by_count":245},{"id":"1905.12886","title":"iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images","created":"2019-05-30","cited_by_count":197},{"id":"2104.01538","title":"Hypercorrelation Squeeze for Few-Shot Segmentation","created":"2021-04-04","cited_by_count":193},{"id":"2008.03898","title":"Prototype Mixture Models for Few-shot Semantic Segmentation","created":"2020-08-10","cited_by_count":190},{"id":"1909.13140","title":"Feature Weighting and Boosting for Few-Shot Segmentation","created":"2019-09-28","cited_by_count":189},{"id":"1810.09091","title":"SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation","created":"2018-10-22","cited_by_count":169},{"id":"2007.06309","title":"Part-aware Prototype Network for Few-shot Semantic Segmentation","created":"2020-07-13","cited_by_count":162},{"id":"1907.12347","title":"FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation","created":"2019-07-29","cited_by_count":160},{"id":"2012.06166","title":"Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?","created":"2020-12-11","cited_by_count":135},{"id":"2106.02320","title":"Few-Shot Segmentation via Cycle-Consistent Transformer","created":"2021-06-04","cited_by_count":133},{"id":"2203.07615","title":"Learning What Not to Segment: A New Perspective on Few-Shot Segmentation","created":"2022-03-15","cited_by_count":120},{"id":"2108.03032","title":"Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer","created":"2021-08-06","cited_by_count":116},{"id":"2207.11549","title":"Self-Support Few-Shot Semantic Segmentation","created":"2022-07-23","cited_by_count":96},{"id":"2303.14652","title":"Hierarchical Dense Correlation Distillation for Few-Shot Segmentation","created":"2023-03-26","cited_by_count":84},{"id":"2103.15402","title":"Mining Latent Classes for Few-shot Segmentation","created":"2021-03-29","cited_by_count":69},{"id":"2210.06908","title":"Feature-Proxy Transformer for Few-Shot Segmentation","created":"2022-10-13","cited_by_count":58},{"id":"2205.04903","title":"Learning Non-target Knowledge for Few-shot Semantic Segmentation","created":"2022-05-10","cited_by_count":57},{"id":"2206.06122","title":"Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning","created":"2022-06-13","cited_by_count":50},{"id":"2210.06780","title":"Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation","created":"2022-10-13","cited_by_count":37},{"id":"2308.09294","title":"Self-Calibrated Cross Attention Network for Few-Shot Segmentation","created":"2023-08-18","cited_by_count":32},{"id":"2303.14123","title":"Semantic Prompt for Few-Shot Image Recognition","created":"2023-03-24","cited_by_count":29},{"id":"2203.15712","title":"Integrative Few-Shot Learning for Classification and Segmentation","created":"2022-03-29","cited_by_count":25},{"id":"2305.13864","title":"MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation","created":"2023-05-23","cited_by_count":25},{"id":"2208.06671","title":"Bidirectional Feature Globalization for Few-shot Semantic Segmentation of 3D Point Cloud Scenes","created":"2022-08-13","cited_by_count":23},{"id":"2301.04581","title":"Elevation Estimation-Driven Building 3D Reconstruction from Single-View Remote Sensing Imagery","created":"2023-01-11","cited_by_count":17},{"id":"2207.10278","title":"Beyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classification","created":"2022-07-21","cited_by_count":16},{"id":"2304.08491","title":"Delving into Shape-aware Zero-shot Semantic Segmentation","created":"2023-04-17","cited_by_count":16},{"id":"2208.11658","title":"AGO-Net: Association-Guided 3D Point Cloud Object Detection Network","created":"2022-08-24","cited_by_count":12},{"id":"2111.12263","title":"APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentation","created":"2021-11-24","cited_by_count":11},{"id":"2310.12452","title":"Not Just Learning from Others but Relying on Yourself: A New Perspective on Few-Shot Segmentation in Remote Sensing","created":"2023-10-19","cited_by_count":11},{"id":"2312.06474","title":"Relevant Intrinsic Feature Enhancement Network for Few-Shot Semantic Segmentation","created":"2023-12-11","cited_by_count":11},{"id":"2306.04715","title":"UniBoost: Unsupervised Unimodal Pre-training for Boosting Zero-shot Vision-Language Tasks","created":"2023-06-07","cited_by_count":4},{"id":"2403.18238","title":"TAFormer: A Unified Target-Aware Transformer for Video and Motion Joint Prediction in Aerial Scenes","created":"2024-03-27","cited_by_count":2},{"id":"2402.18044","title":"SFTformer: A Spatial-Frequency-Temporal Correlation-Decoupling Transformer for Radar Echo Extrapolation","created":"2024-02-28","cited_by_count":1}],"next_cursor":null}