{"total":22,"rows":[{"id":"2311.17034","title":"Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence","created":"2023-11-28","cited_by_count":86,"area":null},{"id":"2301.13173","title":"Shape-aware Text-driven Layered Video Editing","created":"2023-01-30","cited_by_count":54,"area":null},{"id":"2303.12786","title":"FeatureNeRF: Learning Generalizable NeRFs by Distilling Foundation Models","created":"2023-03-22","cited_by_count":47,"area":null},{"id":"2210.02689","title":"Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence","created":"2022-10-06","cited_by_count":32,"area":null},{"id":"2207.06825","title":"Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions","created":"2022-07-14","cited_by_count":31,"area":null},{"id":"2208.06974","title":"Learning Semantic Correspondence with Sparse Annotations","created":"2022-08-15","cited_by_count":31,"area":null},{"id":"2403.11120","title":"Unifying Feature and Cost Aggregation with Transformers for Semantic and Visual Correspondence","created":"2024-03-17","cited_by_count":14,"area":null},{"id":"2511.20886","title":"V$^{2}$-SAM: Marrying SAM2 with Multi-Prompt Experts for Cross-View Object Correspondence","created":"2025-11-25","cited_by_count":14,"area":null},{"id":"2506.05312","title":"Do It Yourself: Learning Semantic Correspondence from Pseudo-Labels","created":"2025-06-05","cited_by_count":13,"area":null},{"id":"2407.18907","title":"SHIC: Shape-Image Correspondences with no Keypoint Supervision","created":"2024-07-26","cited_by_count":12,"area":null},{"id":"2403.12038","title":"Zero-Shot Image Feature Consensus with Deep Functional Maps","created":"2024-03-18","cited_by_count":10,"area":null},{"id":"2307.12774","title":"Fast Full-frame Video Stabilization with Iterative Optimization","created":"2023-07-24","cited_by_count":8,"area":null},{"id":"2209.08742","title":"Integrative Feature and Cost Aggregation with Transformers for Dense Correspondence","created":"2022-09-19","cited_by_count":6,"area":null},{"id":"2305.02385","title":"SimSC: A Simple Framework for Semantic Correspondence with Temperature Learning","created":"2023-05-03","cited_by_count":6,"area":null},{"id":"2311.04336","title":"Efficient Semantic Matching with Hypercolumn Correlation","created":"2023-11-07","cited_by_count":6,"area":null},{"id":"2506.08220","title":"Jamais Vu: Exposing the Generalization Gap in Supervised Semantic Correspondence","created":"2025-06-09","cited_by_count":6,"area":null},{"id":"2508.00746","title":"GECO: Geometrically Consistent Embedding with Lightspeed Inference","created":"2025-08-01","cited_by_count":6,"area":null},{"id":"2404.11156","title":"Learning SO(3)-Invariant Semantic Correspondence via Local Shape Transform","created":"2024-04-17","cited_by_count":3,"area":null},{"id":"2505.18060","title":"Semantic Correspondence: Unified Benchmarking and a Strong Baseline","created":"2025-05-23","cited_by_count":3,"area":null},{"id":"2509.21263","title":"Towards Geometry-Grounded Dense Semantic Matching with VGGT Priors","created":"2025-09-25","cited_by_count":3,"area":null},{"id":"2311.18540","title":"Match me if you can: Semi-Supervised Semantic Correspondence Learning with Unpaired Images","created":"2023-11-30","cited_by_count":2,"area":null},{"id":"2312.15471","title":"Residual Learning for Image Point Descriptors","created":"2023-12-24","cited_by_count":0,"area":null}],"next_cursor":null}