{"list":"other","total":71,"rows":[{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"1405.0312","title":"Microsoft COCO: Common Objects in Context","created":"2014-05-01","cited_by_count":17608},{"id":"1612.03144","title":"Feature Pyramid Networks for Object Detection","created":"2016-12-09","cited_by_count":5036},{"id":"1604.01685","title":"The Cityscapes Dataset for Semantic Urban Scene Understanding","created":"2016-04-06","cited_by_count":4444},{"id":"2210.08402","title":"LAION-5B: An open large-scale dataset for training next generation image-text models","created":"2022-10-16","cited_by_count":4152},{"id":"2201.03545","title":"A ConvNet for the 2020s","created":"2022-01-10","cited_by_count":3579},{"id":"2010.04159","title":"Deformable DETR: Deformable Transformers for End-to-End Object Detection","created":"2020-10-08","cited_by_count":3422},{"id":"2112.01527","title":"Masked-attention Mask Transformer for Universal Image Segmentation","created":"2021-12-02","cited_by_count":2351},{"id":"1606.04797","title":"V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation","created":"2016-06-15","cited_by_count":2292},{"id":"1812.04606","title":"Deep Anomaly Detection with Outlier Exposure","created":"2018-12-11","cited_by_count":942},{"id":"2001.05566","title":"Image Segmentation Using Deep Learning: A Survey","created":"2020-01-15","cited_by_count":660},{"id":"1911.11132","title":"Scaling Out-of-Distribution Detection for Real-World Settings","created":"2019-11-25","cited_by_count":465},{"id":"2111.14887","title":"DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation","created":"2021-11-29","cited_by_count":300},{"id":"2101.11939","title":"Exploring Cross-Image Pixel Contrast for Semantic Segmentation","created":"2021-01-28","cited_by_count":242},{"id":"2308.02487","title":"Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP","created":"2023-08-04","cited_by_count":201},{"id":"1910.04851","title":"Addressing Failure Prediction by Learning Model Confidence","created":"2019-10-01","cited_by_count":186},{"id":"2104.14812","title":"SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation","created":"2021-04-30","cited_by_count":150},{"id":"1811.12709","title":"Evaluating Bayesian Deep Learning Methods for Semantic Segmentation","created":"2018-11-30","cited_by_count":143},{"id":"1904.07595","title":"Detecting the Unexpected via Image Resynthesis","created":"2019-04-16","cited_by_count":137},{"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":"2003.08440","title":"Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation","created":"2020-03-18","cited_by_count":96},{"id":"2203.09283","title":"PanoFormer: Panorama Transformer for Indoor 360 Depth Estimation","created":"2022-03-17","cited_by_count":95},{"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":"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},{"id":"2203.01452","title":"Bending Reality: Distortion-aware Transformers for Adapting to Panoramic Semantic Segmentation","created":"2022-03-02","cited_by_count":72},{"id":"1803.06189","title":"Triplet-Center Loss for Multi-View 3D Object Retrieval","created":"2018-03-16","cited_by_count":69},{"id":"2211.14293","title":"RbA: Segmenting Unknown Regions Rejected by All","created":"2022-11-25","cited_by_count":67},{"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":"2303.13434","title":"Patch-Mix Transformer for Unsupervised Domain Adaptation: A Game Perspective","created":"2023-03-23","cited_by_count":54},{"id":"2108.04562","title":"Deep Metric Learning for Open World Semantic Segmentation","created":"2021-08-10","cited_by_count":51},{"id":"2103.05687","title":"Capturing Omni-Range Context for Omnidirectional Segmentation","created":"2021-03-09","cited_by_count":50},{"id":"2303.14360","title":"Both Style and Distortion Matter: Dual-Path Unsupervised Domain Adaptation for Panoramic Semantic Segmentation","created":"2023-03-25","cited_by_count":47},{"id":"2207.11860","title":"Behind Every Domain There is a Shift: Adapting Distortion-aware Vision Transformers for Panoramic Semantic Segmentation","created":"2022-07-25","cited_by_count":46},{"id":"2205.05570","title":"Review on Panoramic Imaging and Its Applications in Scene Understanding","created":"2022-05-11","cited_by_count":43},{"id":"2207.02606","title":"DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition","created":"2022-07-06","cited_by_count":42},{"id":"2301.03407","title":"On Advantages of Mask-level Recognition for Outlier-aware Segmentation","created":"2023-01-09","cited_by_count":42},{"id":"2308.05493","title":"Look at the Neighbor: Distortion-aware Unsupervised Domain Adaptation for Panoramic Semantic Segmentation","created":"2023-08-10","cited_by_count":38},{"id":"2108.06383","title":"DensePASS: Dense Panoramic Semantic Segmentation via Unsupervised Domain Adaptation with Attention-Augmented Context Exchange","created":"2021-08-13","cited_by_count":36},{"id":"2110.11062","title":"Transfer beyond the Field of View: Dense Panoramic Semantic Segmentation via Unsupervised Domain Adaptation","created":"2021-10-21","cited_by_count":34},{"id":"2305.16972","title":"Maskomaly:Zero-Shot Mask Anomaly Segmentation","created":"2023-05-26","cited_by_count":34},{"id":"2402.15374","title":"Outlier detection by ensembling uncertainty with negative objectness","created":"2024-02-23","cited_by_count":34},{"id":"2403.12505","title":"Semantics, Distortion, and Style Matter: Towards Source-free UDA for Panoramic Segmentation","created":"2024-03-19","cited_by_count":34},{"id":"2108.10743","title":"DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based Optimization","created":"2021-08-24","cited_by_count":33},{"id":"2012.13633","title":"Detecting Road Obstacles by Erasing Them","created":"2020-12-25","cited_by_count":29},{"id":"2403.16370","title":"GoodSAM: Bridging Domain and Capacity Gaps via Segment Anything Model for Distortion-aware Panoramic Semantic Segmentation","created":"2024-03-25","cited_by_count":28},{"id":"2404.16501","title":"360SFUDA++: Towards Source-free UDA for Panoramic Segmentation by Learning Reliable Category Prototypes","created":"2024-04-25","cited_by_count":28},{"id":"2503.07098","title":"OmniSAM: Omnidirectional Segment Anything Model for UDA in Panoramic Semantic Segmentation","created":"2025-03-10","cited_by_count":28},{"id":"2306.03403","title":"SGAT4PASS: Spherical Geometry-Aware Transformer for PAnoramic Semantic Segmentation","created":"2023-06-06","cited_by_count":26},{"id":"2312.09231","title":"Reliability in Semantic Segmentation: Can We Use Synthetic Data?","created":"2023-12-14","cited_by_count":24},{"id":"2502.10444","title":"A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision","created":"2025-02-11","cited_by_count":24},{"id":"2308.14726","title":"PanoSwin: a Pano-style Swin Transformer for Panorama Understanding","created":"2023-08-28","cited_by_count":20},{"id":"2305.09078","title":"PanelNet: Understanding 360 Indoor Environment via Panel Representation","created":"2023-05-16","cited_by_count":19},{"id":"2303.11910","title":"360BEV: Panoramic Semantic Mapping for Indoor Bird's-Eye View","created":"2023-03-21","cited_by_count":18},{"id":"2308.09369","title":"Single Frame Semantic Segmentation Using Multi-Modal Spherical Images","created":"2023-08-18","cited_by_count":18},{"id":"2309.04573","title":"Mask2Anomaly: Mask Transformer for Universal Open-set Segmentation","created":"2023-09-08","cited_by_count":18},{"id":"2407.02685","title":"Open Panoramic Segmentation","created":"2024-07-02","cited_by_count":17},{"id":"1909.00676","title":"This is not what I imagined: Error Detection for Semantic Segmentation through Visual Dissimilarity","created":"2019-09-02","cited_by_count":16},{"id":"2306.10485","title":"Balanced Energy Regularization Loss for Out-of-distribution Detection","created":"2023-06-18","cited_by_count":16},{"id":"1909.00532","title":"Semantic Segmentation of Panoramic Images Using a Synthetic Dataset","created":"2019-09-02","cited_by_count":15},{"id":"2312.05538","title":"CSL: Class-Agnostic Structure-Constrained Learning for Segmentation Including the Unseen","created":"2023-12-09","cited_by_count":15},{"id":"2206.10711","title":"Panoramic Panoptic Segmentation: Insights Into Surrounding Parsing for Mobile Agents via Unsupervised Contrastive Learning","created":"2022-06-21","cited_by_count":14},{"id":"2106.00472","title":"Detecting Anomalies in Semantic Segmentation with Prototypes","created":"2021-06-01","cited_by_count":13},{"id":"2306.12992","title":"Minimalist and High-Quality Panoramic Imaging with PSF-aware Transformers","created":"2023-06-22","cited_by_count":13},{"id":"2503.08695","title":"Out-of-Distribution Segmentation in Autonomous Driving: Problems and State of the Art","created":"2025-03-04","cited_by_count":13},{"id":"2402.16392","title":"Placing Objects in Context via Inpainting for Out-of-distribution Segmentation","created":"2024-02-26","cited_by_count":12},{"id":"2408.09115","title":"GoodSAM++: Bridging Domain and Capacity Gaps via Segment Anything Model for Panoramic Semantic Segmentation","created":"2024-08-17","cited_by_count":11},{"id":"2408.16469","title":"Multi-source Domain Adaptation for Panoramic Semantic Segmentation","created":"2024-08-29","cited_by_count":8},{"id":"2407.02182","title":"Occlusion-Aware Seamless Segmentation","created":"2024-07-02","cited_by_count":7},{"id":"2111.15463","title":"Consensus Synergizes with Memory: A Simple Approach for Anomaly Segmentation in Urban Scenes","created":"2021-11-24","cited_by_count":5},{"id":"2303.08646","title":"High-level Feature Guided Decoding for Semantic Segmentation","created":"2023-03-15","cited_by_count":2}],"next_cursor":null}