{"list":"other","total":61,"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":"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":"1506.01497","title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","created":"2015-06-04","cited_by_count":9639},{"id":"1409.0575","title":"ImageNet Large Scale Visual Recognition Challenge","created":"2014-09-01","cited_by_count":8498},{"id":"1411.7766","title":"Deep Learning Face Attributes in the Wild","created":"2014-11-28","cited_by_count":3693},{"id":"1502.01852","title":"Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification","created":"2015-02-06","cited_by_count":3611},{"id":"1703.05175","title":"Prototypical Networks for Few-shot Learning","created":"2017-03-15","cited_by_count":3015},{"id":"1804.02767","title":"YOLOv3: An Incremental Improvement","created":"2018-04-08","cited_by_count":2659},{"id":"1610.02136","title":"A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks","created":"2016-10-07","cited_by_count":2385},{"id":"2004.07780","title":"Shortcut Learning in Deep Neural Networks","created":"2020-04-16","cited_by_count":1969},{"id":"1807.03888","title":"A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks","created":"2018-07-10","cited_by_count":1485},{"id":"1811.12231","title":"ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness","created":"2018-11-29","cited_by_count":1432},{"id":"1506.03365","title":"LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop","created":"2015-06-10","cited_by_count":1355},{"id":"1706.02690","title":"Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks","created":"2017-06-08","cited_by_count":1328},{"id":"2010.03759","title":"Energy-based Out-of-distribution Detection","created":"2020-10-08","cited_by_count":1299},{"id":"1911.08731","title":"Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization","created":"2019-11-20","cited_by_count":1247},{"id":"1908.03195","title":"LVIS: A Dataset for Large Vocabulary Instance Segmentation","created":"2019-08-08","cited_by_count":1217},{"id":"1812.04606","title":"Deep Anomaly Detection with Outlier Exposure","created":"2018-12-11","cited_by_count":942},{"id":"2110.11334","title":"Generalized Out-of-Distribution Detection: A Survey","created":"2021-10-21","cited_by_count":802},{"id":"1412.1897","title":"Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images","created":"2014-12-05","cited_by_count":776},{"id":"1807.04975","title":"Recognition in Terra Incognita","created":"2018-07-13","cited_by_count":658},{"id":"2204.06507","title":"Out-of-Distribution Detection with Deep Nearest Neighbors","created":"2022-04-13","cited_by_count":600},{"id":"2107.09044","title":"Just Train Twice: Improving Group Robustness without Training Group Information","created":"2021-07-19","cited_by_count":523},{"id":"1906.12340","title":"Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty","created":"2019-06-28","cited_by_count":514},{"id":"2111.12797","title":"ReAct: Out-of-distribution Detection With Rectified Activations","created":"2021-11-24","cited_by_count":486},{"id":"2203.10807","title":"ViM: Out-Of-Distribution with Virtual-logit Matching","created":"2022-03-21","cited_by_count":405},{"id":"2204.02937","title":"Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations","created":"2022-04-06","cited_by_count":381},{"id":"2110.00218","title":"On the Importance of Gradients for Detecting Distributional Shifts in the Wild","created":"2021-10-01","cited_by_count":339},{"id":"2210.07242","title":"OpenOOD: Benchmarking Generalized Out-of-Distribution Detection","created":"2022-10-13","cited_by_count":268},{"id":"2205.09310","title":"Mitigating Neural Network Overconfidence with Logit Normalization","created":"2022-05-19","cited_by_count":252},{"id":"1802.04865","title":"Learning Confidence for Out-of-Distribution Detection in Neural Networks","created":"2018-02-13","cited_by_count":245},{"id":"2105.01879","title":"MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space","created":"2021-05-05","cited_by_count":233},{"id":"2211.13445","title":"Delving into Out-of-Distribution Detection with Vision-Language Representations","created":"2022-11-24","cited_by_count":216},{"id":"2209.09858","title":"Extremely Simple Activation Shaping for Out-of-Distribution Detection","created":"2022-09-20","cited_by_count":212},{"id":"2106.09022","title":"A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection","created":"2021-06-16","cited_by_count":207},{"id":"1504.06755","title":"TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking","created":"2015-04-25","cited_by_count":165},{"id":"1908.04951","title":"Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancy","created":"2019-08-14","cited_by_count":112},{"id":"2108.11941","title":"Semantically Coherent Out-of-Distribution Detection","created":"2021-08-26","cited_by_count":107},{"id":"2008.11600","title":"Estimating Example Difficulty Using Variance of Gradients","created":"2020-08-26","cited_by_count":97},{"id":"2212.04825","title":"A Whac-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others","created":"2022-12-09","cited_by_count":85},{"id":"2310.00227","title":"Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancement","created":"2023-09-30","cited_by_count":77},{"id":"2109.05642","title":"On the Impact of Spurious Correlation for Out-of-distribution Detection","created":"2021-09-12","cited_by_count":68},{"id":"2403.20078","title":"Negative Label Guided OOD Detection with Pretrained Vision-Language Models","created":"2024-03-29","cited_by_count":66},{"id":"2309.14888","title":"Nearest Neighbor Guidance for Out-of-Distribution Detection","created":"2023-09-26","cited_by_count":64},{"id":"2312.11536","title":"Fast Decision Boundary based Out-of-Distribution Detector","created":"2023-12-15","cited_by_count":49},{"id":"2402.02653","title":"Learning with Mixture of Prototypes for Out-of-Distribution Detection","created":"2024-02-05","cited_by_count":43},{"id":"2310.06823","title":"NECO: NEural Collapse Based Out-of-distribution detection","created":"2023-10-10","cited_by_count":42},{"id":"2212.04871","title":"Spurious Features Everywhere -- Large-Scale Detection of Harmful Spurious Features in ImageNet","created":"2022-12-09","cited_by_count":41},{"id":"2311.01479","title":"Detecting Out-of-Distribution Through the Lens of Neural Collapse","created":"2023-11-02","cited_by_count":34},{"id":"1912.12510","title":"Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matrices","created":"2019-12-28","cited_by_count":32},{"id":"2301.12321","title":"Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data","created":"2023-01-29","cited_by_count":20},{"id":"2310.08027","title":"Exploring Large Language Models for Multi-Modal Out-of-Distribution Detection","created":"2023-10-12","cited_by_count":13},{"id":"2312.02615","title":"Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models","created":"2023-12-05","cited_by_count":9},{"id":"2407.16430","title":"Rethinking Out-of-Distribution Detection on Imbalanced Data Distribution","created":"2024-07-23","cited_by_count":6},{"id":"2404.07664","title":"Finding Dino: A Plug-and-Play Framework for Zero-Shot Detection of Out-of-Distribution Objects Using Prototypes","created":"2024-04-11","cited_by_count":5},{"id":"2404.19644","title":"MetaCoCo: A New Few-Shot Classification Benchmark with Spurious Correlation","created":"2024-04-30","cited_by_count":5},{"id":"2410.18472","title":"What If the Input is Expanded in OOD Detection?","created":"2024-10-24","cited_by_count":4},{"id":"2301.13012","title":"Key Feature Replacement of In-Distribution Samples for Out-of-Distribution Detection","created":"2022-12-26","cited_by_count":3},{"id":"2503.17717","title":"BackMix: Regularizing Open Set Recognition by Removing Underlying Fore-Background Priors","created":"2025-03-22","cited_by_count":3}],"next_cursor":null}