{"total":20,"rows":[{"id":"2104.14812","title":"SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation","created":"2021-04-30","cited_by_count":150,"area":null},{"id":"2103.05445","title":"Pixel-wise Anomaly Detection in Complex Driving Scenes","created":"2021-03-09","cited_by_count":109,"area":null},{"id":"2108.01634","title":"Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentation","created":"2021-08-03","cited_by_count":40,"area":null},{"id":"2104.14235","title":"Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety","created":"2021-04-29","cited_by_count":35,"area":null},{"id":"2305.16972","title":"Maskomaly:Zero-Shot Mask Anomaly Segmentation","created":"2023-05-26","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":"2304.00212","title":"Devil is in the Queries: Advancing Mask Transformers for Real-world Medical Image Segmentation and Out-of-Distribution Localization","created":"2023-04-01","cited_by_count":16,"area":null},{"id":"2112.05341","title":"Hyperdimensional Feature Fusion for Out-Of-Distribution Detection","created":"2021-12-10","cited_by_count":13,"area":null},{"id":"2201.01073","title":"Towards Unsupervised Open World Semantic Segmentation","created":"2022-01-04","cited_by_count":13,"area":null},{"id":"2202.07006","title":"A Survey of Visual Sensory Anomaly Detection","created":"2022-02-14","cited_by_count":11,"area":null},{"id":"2309.04302","title":"Have We Ever Encountered This Before? Retrieving Out-of-Distribution Road Obstacles from Driving Scenes","created":"2023-09-08","cited_by_count":9,"area":null},{"id":"2401.04942","title":"Latency-aware Road Anomaly Segmentation in Videos: A Photorealistic Dataset and New Metrics","created":"2024-01-10","cited_by_count":8,"area":null},{"id":"2106.03899","title":"Shifting Transformation Learning for Out-of-Distribution Detection","created":"2021-06-07","cited_by_count":6,"area":null},{"id":"2308.08696","title":"Improving Anomaly Segmentation with Multi-Granularity Cross-Domain Alignment","created":"2023-08-16","cited_by_count":6,"area":null},{"id":"2401.17013","title":"Evaluation of Out-of-Distribution Detection Performance on Autonomous Driving Datasets","created":"2024-01-30","cited_by_count":5,"area":null},{"id":"2202.08700","title":"Detecting and Learning the Unknown in Semantic Segmentation","created":"2022-02-17","cited_by_count":4,"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":"2409.11373","title":"Uncertainty and Prediction Quality Estimation for Semantic Segmentation via Graph Neural Networks","created":"2024-09-17","cited_by_count":0,"area":null},{"id":"2412.10765","title":"Neural Network Meta Classifier: Improving the Reliability of Anomaly Segmentation","created":"2024-12-14","cited_by_count":0,"area":null},{"id":"2603.16122","title":"Out-of-Distribution Object Detection in Street Scenes via Synthetic Outlier Exposure and Transfer Learning","created":"2026-03-17","cited_by_count":0,"area":null}],"next_cursor":null}