{"total":28,"rows":[{"id":"2202.01197","title":"VOS: Learning What You Don't Know by Virtual Outlier Synthesis","created":"2022-02-02","cited_by_count":300,"area":null},{"id":"2107.11264","title":"Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation","created":"2021-07-23","cited_by_count":77,"area":null},{"id":"2307.13316","title":"Unmasking Anomalies in Road-Scene Segmentation","created":"2023-07-25","cited_by_count":45,"area":null},{"id":"2207.02606","title":"DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition","created":"2022-07-06","cited_by_count":42,"area":null},{"id":"2301.03407","title":"On Advantages of Mask-level Recognition for Outlier-aware Segmentation","created":"2023-01-09","cited_by_count":42,"area":null},{"id":"2305.16972","title":"Maskomaly:Zero-Shot Mask Anomaly Segmentation","created":"2023-05-26","cited_by_count":34,"area":null},{"id":"2402.15374","title":"Outlier detection by ensembling uncertainty with negative objectness","created":"2024-02-23","cited_by_count":34,"area":null},{"id":"2210.02074","title":"Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects","created":"2022-10-05","cited_by_count":19,"area":null},{"id":"2309.04573","title":"Mask2Anomaly: Mask Transformer for Universal Open-set Segmentation","created":"2023-09-08","cited_by_count":18,"area":null},{"id":"2201.13279","title":"UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs","created":"2022-01-31","cited_by_count":16,"area":null},{"id":"2302.02790","title":"Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey","created":"2023-02-06","cited_by_count":16,"area":null},{"id":"2302.07106","title":"Normalizing Flow based Feature Synthesis for Outlier-Aware Object Detection","created":"2023-02-01","cited_by_count":16,"area":null},{"id":"2306.02031","title":"DOS: Diverse Outlier Sampling for Out-of-Distribution Detection","created":"2023-06-03","cited_by_count":16,"area":null},{"id":"2405.07865","title":"AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving","created":"2024-05-13","cited_by_count":14,"area":null},{"id":"2112.12833","title":"Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Data","created":"2021-12-23","cited_by_count":10,"area":null},{"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,"area":null},{"id":"2301.08555","title":"Hybrid Open-set Segmentation with Synthetic Negative Data","created":"2023-01-19","cited_by_count":8,"area":null},{"id":"2212.14397","title":"AttEntropy: On the Generalization Ability of Supervised Semantic Segmentation Transformers to New Objects in New Domains","created":"2022-12-29","cited_by_count":4,"area":null},{"id":"2501.16718","title":"Outlier Synthesis via Hamiltonian Monte Carlo for Out-of-Distribution Detection","created":"2025-01-28","cited_by_count":4,"area":null},{"id":"2208.14195","title":"Probing Contextual Diversity for Dense Out-of-Distribution Detection","created":"2022-08-30","cited_by_count":3,"area":null},{"id":"2302.06815","title":"Self-Supervised Likelihood Estimation with Energy Guidance for Anomaly Segmentation in Urban Scenes","created":"2023-02-14","cited_by_count":2,"area":null},{"id":"2308.02535","title":"Learning to Generate Training Datasets for Robust Semantic Segmentation","created":"2023-08-01","cited_by_count":2,"area":null},{"id":"2509.13181","title":"Road Obstacle Video Segmentation","created":"2025-09-16","cited_by_count":2,"area":null},{"id":"2402.16911","title":"Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tune","created":"2024-02-25","cited_by_count":1,"area":null},{"id":"2305.15227","title":"Real time dense anomaly detection by learning on synthetic negative data","created":"2023-05-24","cited_by_count":0,"area":null},{"id":"2307.01325","title":"Robust Uncertainty Estimation for Classification of Maritime Objects","created":"2023-07-03","cited_by_count":0,"area":null},{"id":"2508.21770","title":"What Can We Learn from Harry Potter? An Exploratory Study of Visual Representation Learning from Atypical Videos","created":"2025-08-29","cited_by_count":0,"area":null},{"id":"2604.08008","title":"SearchAD: Large-Scale Rare Image Retrieval Dataset for Autonomous Driving","created":"2026-04-09","cited_by_count":0,"area":null}],"next_cursor":null}