{"list":"other","total":55,"rows":[{"id":"1903.11027","title":"nuScenes: A multimodal dataset for autonomous driving","created":"2019-03-26","cited_by_count":4523},{"id":"1612.01474","title":"Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles","created":"2016-12-05","cited_by_count":3926},{"id":"1802.02611","title":"Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation","created":"2018-02-07","cited_by_count":3223},{"id":"1512.03012","title":"ShapeNet: An Information-Rich 3D Model Repository","created":"2015-12-09","cited_by_count":2897},{"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":"2112.01527","title":"Masked-attention Mask Transformer for Universal Image Segmentation","created":"2021-12-02","cited_by_count":2351},{"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":"1904.08755","title":"4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks","created":"2019-04-18","cited_by_count":1208},{"id":"2107.06278","title":"Per-Pixel Classification is Not All You Need for Semantic Segmentation","created":"2021-07-13","cited_by_count":1152},{"id":"1906.07413","title":"Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss","created":"2019-06-18","cited_by_count":989},{"id":"1812.04606","title":"Deep Anomaly Detection with Outlier Exposure","created":"2018-12-11","cited_by_count":942},{"id":"1801.00868","title":"Panoptic Segmentation","created":"2018-01-03","cited_by_count":802},{"id":"2110.11334","title":"Generalized Out-of-Distribution Detection: A Survey","created":"2021-10-21","cited_by_count":802},{"id":"1904.05160","title":"Large-Scale Long-Tailed Recognition in an Open World","created":"2019-04-10","cited_by_count":705},{"id":"1911.11132","title":"Scaling Out-of-Distribution Detection for Real-World Settings","created":"2019-11-25","cited_by_count":465},{"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":"2202.01197","title":"VOS: Learning What You Don't Know by Virtual Outlier Synthesis","created":"2022-02-02","cited_by_count":300},{"id":"2104.14812","title":"SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation","created":"2021-04-30","cited_by_count":150},{"id":"2012.06575","title":"Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic Segmentation","created":"2020-12-09","cited_by_count":133},{"id":"1904.03215","title":"The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation","created":"2019-04-05","cited_by_count":122},{"id":"2008.01550","title":"Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation","created":"2020-08-04","cited_by_count":112},{"id":"2210.14707","title":"Is Out-of-Distribution Detection Learnable?","created":"2022-10-26","cited_by_count":109},{"id":"2112.00412","title":"The Majority Can Help The Minority: Context-rich Minority Oversampling for Long-tailed Classification","created":"2021-12-01","cited_by_count":100},{"id":"2210.02025","title":"GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models","created":"2022-10-05","cited_by_count":87},{"id":"2111.12264","title":"Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes","created":"2021-11-24","cited_by_count":83},{"id":"1910.11296","title":"Identifying Unknown Instances for Autonomous Driving","created":"2019-10-24","cited_by_count":74},{"id":"2211.14293","title":"RbA: Segmenting Unknown Regions Rejected by All","created":"2022-11-25","cited_by_count":67},{"id":"2403.20078","title":"Negative Label Guided OOD Detection with Pretrained Vision-Language Models","created":"2024-03-29","cited_by_count":66},{"id":"2211.14512","title":"Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation","created":"2022-11-26","cited_by_count":55},{"id":"2307.13316","title":"Unmasking Anomalies in Road-Scene Segmentation","created":"2023-07-25","cited_by_count":45},{"id":"2207.01160","title":"Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition","created":"2022-07-04","cited_by_count":44},{"id":"2303.05033","title":"Out-of-distribution Detection with Implicit Outlier Transformation","created":"2023-03-09","cited_by_count":43},{"id":"2311.01796","title":"Learning to Augment Distributions for Out-of-Distribution Detection","created":"2023-11-03","cited_by_count":38},{"id":"2402.15374","title":"Outlier detection by ensembling uncertainty with negative objectness","created":"2024-02-23","cited_by_count":34},{"id":"2311.03236","title":"Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources","created":"2023-11-06","cited_by_count":31},{"id":"2207.01452","title":"Open-world Semantic Segmentation for LIDAR Point Clouds","created":"2022-07-04","cited_by_count":29},{"id":"2210.15198","title":"Watermarking for Out-of-distribution Detection","created":"2022-10-27","cited_by_count":28},{"id":"2309.16133","title":"Mask4Former: Mask Transformer for 4D Panoptic Segmentation","created":"2023-09-28","cited_by_count":24},{"id":"2312.10686","title":"Out-of-Distribution Detection in Long-Tailed Recognition with Calibrated Outlier Class Learning","created":"2023-12-17","cited_by_count":23},{"id":"2209.14435","title":"Out-of-Distribution Detection for LiDAR-based 3D Object Detection","created":"2022-09-28","cited_by_count":17},{"id":"2312.08939","title":"EAT: Towards Long-Tailed Out-of-Distribution Detection","created":"2023-12-14","cited_by_count":16},{"id":"2409.06424","title":"A Likelihood Ratio-Based Approach to Segmenting Unknown Objects","created":"2024-09-10","cited_by_count":12},{"id":"2404.15879","title":"Revisiting Out-of-Distribution Detection in LiDAR-based 3D Object Detection","created":"2024-04-24","cited_by_count":11},{"id":"2505.02148","title":"Spotting the Unexpected (STU): A 3D LiDAR Dataset for Anomaly Segmentation in Autonomous Driving","created":"2025-05-04","cited_by_count":10},{"id":"2503.10091","title":"G$^{2}$SF-MIAD: Geometry-Guided Score Fusion for Multimodal Industrial Anomaly Detection","created":"2025-03-13","cited_by_count":7},{"id":"2407.16430","title":"Rethinking Out-of-Distribution Detection on Imbalanced Data Distribution","created":"2024-07-23","cited_by_count":6},{"id":"2410.09821","title":"DAS3D: Dual-modality Anomaly Synthesis for 3D Anomaly Detection","created":"2024-10-13","cited_by_count":5},{"id":"2411.18207","title":"From Open Vocabulary to Open World: Teaching Vision Language Models to Detect Novel Objects","created":"2024-11-27","cited_by_count":5},{"id":"2309.10230","title":"LiON: Learning Point-wise Abstaining Penalty for LiDAR Outlier DetectioN Using Diverse Synthetic Data","created":"2023-09-19","cited_by_count":3},{"id":"2504.12856","title":"3D-PNAS: 3D Industrial Surface Anomaly Synthesis with Perlin Noise","created":"2025-04-17","cited_by_count":2},{"id":"2507.00570","title":"Out-of-distribution detection in 3D applications: a review","created":"2025-07-01","cited_by_count":2},{"id":"2511.06720","title":"Relative Energy Learning for LiDAR Out-of-Distribution Detection","created":"2025-11-10","cited_by_count":2},{"id":"2511.18816","title":"SupLID: Geometrical Guidance for Out-of-Distribution Detection in Semantic Segmentation","created":"2025-11-24","cited_by_count":1}],"next_cursor":null}