{"list":"other","total":53,"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":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"2112.10752","title":"High-Resolution Image Synthesis with Latent Diffusion Models","created":"2021-12-20","cited_by_count":18863},{"id":"2303.08774","title":"GPT-4 Technical Report","created":"2023-03-15","cited_by_count":18396},{"id":"1405.0312","title":"Microsoft COCO: Common Objects in Context","created":"2014-05-01","cited_by_count":17608},{"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":"2304.02643","title":"Segment Anything","created":"2023-04-05","cited_by_count":8234},{"id":"2204.02311","title":"PaLM: Scaling Language Modeling with Pathways","created":"2022-04-05","cited_by_count":5326},{"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":"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":"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":"1805.04687","title":"BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning","created":"2018-05-12","cited_by_count":1288},{"id":"1812.04606","title":"Deep Anomaly Detection with Outlier Exposure","created":"2018-12-11","cited_by_count":942},{"id":"2104.13921","title":"Open-vocabulary Object Detection via Vision and Language Knowledge Distillation","created":"2021-04-28","cited_by_count":906},{"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":"2204.06507","title":"Out-of-Distribution Detection with Deep Nearest Neighbors","created":"2022-04-13","cited_by_count":600},{"id":"1511.06233","title":"Towards Open Set Deep Networks","created":"2015-11-19","cited_by_count":538},{"id":"2007.08176","title":"CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances","created":"2020-07-16","cited_by_count":441},{"id":"2002.11297","title":"Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data","created":"2020-02-26","cited_by_count":408},{"id":"1711.09325","title":"Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples","created":"2017-11-26","cited_by_count":389},{"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":"2210.07574","title":"Is synthetic data from generative models ready for image recognition?","created":"2022-10-14","cited_by_count":320},{"id":"2202.01197","title":"VOS: Learning What You Don't Know by Virtual Outlier Synthesis","created":"2022-02-02","cited_by_count":300},{"id":"2103.02603","title":"Towards Open World Object Detection","created":"2021-03-03","cited_by_count":297},{"id":"2106.09022","title":"A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection","created":"2021-06-16","cited_by_count":207},{"id":"2104.02939","title":"OpenGAN: Open-Set Recognition via Open Data Generation","created":"2021-04-07","cited_by_count":135},{"id":"2112.01513","title":"OW-DETR: Open-world Detection Transformer","created":"2021-12-02","cited_by_count":129},{"id":"2303.02966","title":"Non-Parametric Outlier Synthesis","created":"2023-03-06","cited_by_count":129},{"id":"1710.06677","title":"Dropout Sampling for Robust Object Detection in Open-Set Conditions","created":"2017-10-18","cited_by_count":112},{"id":"2003.04668","title":"Incremental Few-Shot Object Detection","created":"2020-03-10","cited_by_count":102},{"id":"2109.14162","title":"Can multi-label classification networks know what they don't know?","created":"2021-09-29","cited_by_count":100},{"id":"2202.03299","title":"Training OOD Detectors in their Natural Habitats","created":"2022-02-07","cited_by_count":86},{"id":"2309.13415","title":"Dream the Impossible: Outlier Imagination with Diffusion Models","created":"2023-09-23","cited_by_count":77},{"id":"2312.17742","title":"Learning Vision from Models Rivals Learning Vision from Data","created":"2023-12-28","cited_by_count":71},{"id":"2203.03800","title":"Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wild","created":"2022-03-08","cited_by_count":70},{"id":"2302.02914","title":"Energy-based Out-of-Distribution Detection for Graph Neural Networks","created":"2023-02-06","cited_by_count":68},{"id":"2310.14664","title":"Data Pruning via Moving-one-Sample-out","created":"2023-10-23","cited_by_count":67},{"id":"1811.10800","title":"Probabilistic Object Detection: Definition and Evaluation","created":"2018-11-27","cited_by_count":55},{"id":"1809.06006","title":"Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection","created":"2018-09-17","cited_by_count":51},{"id":"2402.03502","title":"How Does Unlabeled Data Provably Help Out-of-Distribution Detection?","created":"2024-02-05","cited_by_count":36},{"id":"2208.13930","title":"SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detection","created":"2022-08-29","cited_by_count":33},{"id":"2311.03236","title":"Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources","created":"2023-11-06","cited_by_count":31},{"id":"2104.08381","title":"Robust Object Detection via Instance-Level Temporal Cycle Confusion","created":"2021-04-16","cited_by_count":24},{"id":"2108.03614","title":"Monte Carlo DropBlock for Modelling Uncertainty in Object Detection","created":"2021-08-08","cited_by_count":20},{"id":"2307.09316","title":"MarS3D: A Plug-and-Play Motion-Aware Model for Semantic Segmentation on Multi-Scan 3D Point Clouds","created":"2023-07-18","cited_by_count":19},{"id":"2406.19568","title":"How Far are AI-generated Videos from Simulating the 3D Visual World: A Learned 3D Evaluation Approach","created":"2024-06-27","cited_by_count":16},{"id":"2405.21070","title":"What Makes CLIP More Robust to Long-Tailed Pre-Training Data? A Controlled Study for Transferable Insights","created":"2024-05-31","cited_by_count":13},{"id":"2403.14760","title":"Can 3D Vision-Language Models Truly Understand Natural Language?","created":"2024-03-21","cited_by_count":12},{"id":"2002.05347","title":"Multi-Task Incremental Learning for Object Detection","created":"2020-02-13","cited_by_count":7}],"next_cursor":null}