{"list":"other","total":173,"rows":[{"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":"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":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"2103.14030","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","created":"2021-03-25","cited_by_count":12139},{"id":"1409.0575","title":"ImageNet Large Scale Visual Recognition Challenge","created":"2014-09-01","cited_by_count":8498},{"id":"2304.07193","title":"DINOv2: Learning Robust Visual Features without Supervision","created":"2023-04-14","cited_by_count":7974},{"id":"1911.05722","title":"Momentum Contrast for Unsupervised Visual Representation Learning","created":"2019-11-13","cited_by_count":7513},{"id":"2111.06377","title":"Masked Autoencoders Are Scalable Vision Learners","created":"2021-11-11","cited_by_count":7403},{"id":"2104.14294","title":"Emerging Properties in Self-Supervised Vision Transformers","created":"2021-04-29","cited_by_count":6968},{"id":"2301.12597","title":"BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models","created":"2023-01-30","cited_by_count":6137},{"id":"1710.09412","title":"mixup: Beyond Empirical Risk Minimization","created":"2017-10-25","cited_by_count":5183},{"id":"1703.03400","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","created":"2017-03-09","cited_by_count":4897},{"id":"1703.05175","title":"Prototypical Networks for Few-shot Learning","created":"2017-03-15","cited_by_count":3015},{"id":"1512.03012","title":"ShapeNet: An Information-Rich 3D Model Repository","created":"2015-12-09","cited_by_count":2897},{"id":"1306.0895","title":"Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances","created":"2013-06-04","cited_by_count":2316},{"id":"1705.06950","title":"The Kinetics Human Action Video Dataset","created":"2017-05-19","cited_by_count":1909},{"id":"1306.5151","title":"Fine-Grained Visual Classification of Aircraft","created":"2013-06-21","cited_by_count":1726},{"id":"1803.07728","title":"Unsupervised Representation Learning by Predicting Image Rotations","created":"2018-03-21","cited_by_count":1591},{"id":"1812.01754","title":"Moment Matching for Multi-Source Domain Adaptation","created":"2018-12-04","cited_by_count":1331},{"id":"1706.07522","title":"Deep Hashing Network for Unsupervised Domain Adaptation","created":"2017-06-22","cited_by_count":1188},{"id":"1911.02685","title":"A Comprehensive Survey on Transfer Learning","created":"2019-11-07","cited_by_count":1160},{"id":"1911.09070","title":"EfficientDet: Scalable and Efficient Object Detection","created":"2019-11-20","cited_by_count":1020},{"id":"2110.11334","title":"Generalized Out-of-Distribution Detection: A Survey","created":"2021-10-21","cited_by_count":802},{"id":"1511.06335","title":"Unsupervised Deep Embedding for Clustering Analysis","created":"2015-11-19","cited_by_count":767},{"id":"2001.05566","title":"Image Segmentation Using Deep Learning: A Survey","created":"2020-01-15","cited_by_count":660},{"id":"2109.00512","title":"Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction","created":"2021-09-01","cited_by_count":652},{"id":"2004.14368","title":"VGGSound: A Large-scale Audio-Visual Dataset","created":"2020-04-29","cited_by_count":648},{"id":"1908.04616","title":"Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data","created":"2019-08-13","cited_by_count":532},{"id":"1710.06924","title":"VisDA: The Visual Domain Adaptation Challenge","created":"2017-10-18","cited_by_count":435},{"id":"2003.04807","title":"Efficient Intent Detection with Dual Sentence Encoders","created":"2020-03-10","cited_by_count":433},{"id":"1909.02027","title":"An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction","created":"2019-09-04","cited_by_count":386},{"id":"2110.06207","title":"Open-Set Recognition: a Good Closed-Set Classifier is All You Need?","created":"2021-10-12","cited_by_count":353},{"id":"1811.08581","title":"Recent Advances in Open Set Recognition: A Survey","created":"2018-11-21","cited_by_count":310},{"id":"2210.07242","title":"OpenOOD: Benchmarking Generalized Out-of-Distribution Detection","created":"2022-10-13","cited_by_count":268},{"id":"1810.10147","title":"FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation","created":"2018-10-24","cited_by_count":254},{"id":"1908.09884","title":"Learning to Discover Novel Visual Categories via Deep Transfer Clustering","created":"2019-08-26","cited_by_count":225},{"id":"2201.02609","title":"Generalized Category Discovery","created":"2022-01-07","cited_by_count":211},{"id":"2003.13911","title":"Proxy Anchor Loss for Deep Metric Learning","created":"2020-03-31","cited_by_count":195},{"id":"2103.16483","title":"Benchmarking Representation Learning for Natural World Image Collections","created":"2021-03-30","cited_by_count":168},{"id":"1903.05566","title":"Benchmarking Natural Language Understanding Services for building Conversational Agents","created":"2019-03-13","cited_by_count":166},{"id":"2306.15880","title":"Towards Open Vocabulary Learning: A Survey","created":"2023-06-28","cited_by_count":166},{"id":"2102.03526","title":"Open-World Semi-Supervised Learning","created":"2021-02-06","cited_by_count":165},{"id":"2108.08536","title":"A Unified Objective for Novel Class Discovery","created":"2021-08-19","cited_by_count":162},{"id":"2002.05714","title":"Automatically Discovering and Learning New Visual Categories with Ranking Statistics","created":"2020-02-13","cited_by_count":143},{"id":"1711.10125","title":"Learning to cluster in order to transfer across domains and tasks","created":"2017-11-28","cited_by_count":128},{"id":"2002.09147","title":"SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances","created":"2020-02-21","cited_by_count":123},{"id":"2106.10731","title":"Neighborhood Contrastive Learning for Novel Class Discovery","created":"2021-06-20","cited_by_count":121},{"id":"2106.15252","title":"AutoNovel: Automatically Discovering and Learning Novel Visual Categories","created":"2021-06-29","cited_by_count":116},{"id":"2211.11727","title":"Parametric Classification for Generalized Category Discovery: A Baseline Study","created":"2022-11-21","cited_by_count":113},{"id":"1709.08267","title":"HDLTex: Hierarchical Deep Learning for Text Classification","created":"2017-09-24","cited_by_count":106},{"id":"2107.03358","title":"Novel Visual Category Discovery with Dual Ranking Statistics and Mutual Knowledge Distillation","created":"2021-07-07","cited_by_count":104},{"id":"1901.00544","title":"Multi-class Classification without Multi-class Labels","created":"2019-01-02","cited_by_count":100},{"id":"1902.11266","title":"Efficient Parameter-free Clustering Using First Neighbor Relations","created":"2019-02-28","cited_by_count":97},{"id":"2004.05551","title":"OpenMix: Reviving Known Knowledge for Discovering Novel Visual Categories in An Open World","created":"2020-04-12","cited_by_count":93},{"id":"2303.17393","title":"Dynamic Conceptional Contrastive Learning for Generalized Category Discovery","created":"2023-03-30","cited_by_count":92},{"id":"2212.05590","title":"PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discovery","created":"2022-12-11","cited_by_count":87},{"id":"2104.08398","title":"Re-TACRED: Addressing Shortcomings of the TACRED Dataset","created":"2021-04-16","cited_by_count":74},{"id":"2305.06144","title":"Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery","created":"2023-05-10","cited_by_count":72},{"id":"2307.09220","title":"A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Future","created":"2023-07-18","cited_by_count":61},{"id":"2311.17055","title":"No Representation Rules Them All in Category Discovery","created":"2023-11-28","cited_by_count":61},{"id":"1906.05372","title":"The Herbarium Challenge 2019 Dataset","created":"2019-06-12","cited_by_count":60},{"id":"2104.12673","title":"Joint Representation Learning and Novel Category Discovery on Single- and Multi-modal Data","created":"2021-04-26","cited_by_count":55},{"id":"2208.11296","title":"Semi-Supervised and Unsupervised Deep Visual Learning: A Survey","created":"2022-08-24","cited_by_count":52},{"id":"2403.13684","title":"SPTNet: An Efficient Alternative Framework for Generalized Category Discovery with Spatial Prompt Tuning","created":"2024-03-20","cited_by_count":51},{"id":"2208.01898","title":"XCon: Learning with Experts for Fine-grained Category Discovery","created":"2022-08-03","cited_by_count":50},{"id":"2208.00219","title":"Meta-DETR: Image-Level Few-Shot Detection with Inter-Class Correlation Exploitation","created":"2022-07-30","cited_by_count":48},{"id":"2204.05604","title":"Towards Open-Set Object Detection and Discovery","created":"2022-04-12","cited_by_count":44},{"id":"2207.08605","title":"Class-incremental Novel Class Discovery","created":"2022-07-18","cited_by_count":42},{"id":"2308.11063","title":"MetaGCD: Learning to Continually Learn in Generalized Category Discovery","created":"2023-08-21","cited_by_count":42},{"id":"2207.10659","title":"Novel Class Discovery without Forgetting","created":"2022-07-21","cited_by_count":39},{"id":"2212.00334","title":"Parametric Information Maximization for Generalized Category Discovery","created":"2022-12-01","cited_by_count":39},{"id":"2310.19776","title":"Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discovery","created":"2023-10-30","cited_by_count":39},{"id":"2404.09451","title":"Contrastive Mean-Shift Learning for Generalized Category Discovery","created":"2024-04-15","cited_by_count":39},{"id":"2207.02261","title":"OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning","created":"2022-07-05","cited_by_count":37},{"id":"2211.15115","title":"Generalized Category Discovery with Decoupled Prototypical Network","created":"2022-11-28","cited_by_count":35},{"id":"2408.14371","title":"SelEx: Self-Expertise in Fine-Grained Generalized Category Discovery","created":"2024-08-26","cited_by_count":34},{"id":"2208.02764","title":"OpenCon: Open-world Contrastive Learning","created":"2022-08-04","cited_by_count":33},{"id":"2210.04174","title":"Grow and Merge: A Unified Framework for Continuous Categories Discovery","created":"2022-10-09","cited_by_count":33},{"id":"2102.04002","title":"Meta Discovery: Learning to Discover Novel Classes given Very Limited Data","created":"2021-02-08","cited_by_count":31},{"id":"2112.01900","title":"Novel Class Discovery in Semantic Segmentation","created":"2021-12-03","cited_by_count":31},{"id":"2403.04272","title":"Active Generalized Category Discovery","created":"2024-03-07","cited_by_count":31},{"id":"2403.07369","title":"Textual Knowledge Matters: Cross-Modality Co-Teaching for Generalized Visual Class Discovery","created":"2024-03-12","cited_by_count":31},{"id":"2207.02269","title":"Towards Realistic Semi-Supervised Learning","created":"2022-07-05","cited_by_count":27},{"id":"2305.14107","title":"Federated Generalized Category Discovery","created":"2023-05-23","cited_by_count":27},{"id":"2403.01759","title":"Open-world machine learning: A review and new outlooks","created":"2024-03-04","cited_by_count":27},{"id":"2403.09974","title":"GET: Unlocking the Multi-modal Potential of CLIP for Generalized Category Discovery","created":"2024-03-15","cited_by_count":27},{"id":"2210.03591","title":"Modeling Inter-Class and Intra-Class Constraints in Novel Class Discovery","created":"2022-10-07","cited_by_count":26},{"id":"2304.06928","title":"CiPR: An Efficient Framework with Cross-instance Positive Relations for Generalized Category Discovery","created":"2023-04-14","cited_by_count":26},{"id":"2501.05272","title":"Solving the Catastrophic Forgetting Problem in Generalized Category Discovery","created":"2025-01-09","cited_by_count":26},{"id":"2304.14310","title":"Incremental Generalized Category Discovery","created":"2023-04-27","cited_by_count":25},{"id":"2305.10420","title":"CLIP-GCD: Simple Language Guided Generalized Category Discovery","created":"2023-05-17","cited_by_count":25},{"id":"2401.13837","title":"Democratizing Fine-grained Visual Recognition with Large Language Models","created":"2024-01-24","cited_by_count":25},{"id":"2302.12028","title":"Novel Class Discovery: an Introduction and Key Concepts","created":"2023-02-22","cited_by_count":24},{"id":"2307.09158","title":"Class-relation Knowledge Distillation for Novel Class Discovery","created":"2023-07-18","cited_by_count":23},{"id":"2408.04591","title":"HiLo: A Learning Framework for Generalized Category Discovery Robust to Domain Shifts","created":"2024-08-08","cited_by_count":23}],"next_cursor":"100"}