{"total":32,"rows":[{"id":"2401.12665","title":"ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation","created":"2024-01-23","cited_by_count":49,"area":null},{"id":"2409.19483","title":"MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation","created":"2024-09-28","cited_by_count":39,"area":null},{"id":"2403.20253","title":"MedCLIP-SAM: Bridging Text and Image Towards Universal Medical Image Segmentation","created":"2024-03-29","cited_by_count":37,"area":null},{"id":"2503.12068","title":"Prototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentation","created":"2025-03-15","cited_by_count":17,"area":null},{"id":"2403.01909","title":"Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey","created":"2024-03-04","cited_by_count":12,"area":null},{"id":"2505.24103","title":"Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors","created":"2025-05-30","cited_by_count":9,"area":null},{"id":"2405.14294","title":"Tuning-free Universally-Supervised Semantic Segmentation","created":"2024-05-23","cited_by_count":6,"area":null},{"id":"2408.10843","title":"Detecting Wildfires on UAVs with Real-time Segmentation Trained by Larger Teacher Models","created":"2024-08-19","cited_by_count":5,"area":null},{"id":"2407.10131","title":"WPS-SAM: Towards Weakly-Supervised Part Segmentation with Foundation Models","created":"2024-07-14","cited_by_count":4,"area":null},{"id":"2401.11791","title":"Semantic Prompt Learning for Weakly-Supervised Semantic Segmentation","created":"2024-01-22","cited_by_count":3,"area":null},{"id":"2401.14074","title":"ProCNS: Progressive Prototype Calibration and Noise Suppression for Weakly-Supervised Medical Image Segmentation","created":"2024-01-25","cited_by_count":3,"area":null},{"id":"2404.00380","title":"DHR: Dual Features-Driven Hierarchical Rebalancing in Inter- and Intra-Class Regions for Weakly-Supervised Semantic Segmentation","created":"2024-03-30","cited_by_count":3,"area":null},{"id":"2406.19057","title":"Segment Anything Model for automated image data annotation: empirical studies using text prompts from Grounding DINO","created":"2024-06-27","cited_by_count":3,"area":null},{"id":"2409.14627","title":"SOS: Segment Object System for Open-World Instance Segmentation With Object Priors","created":"2024-09-22","cited_by_count":3,"area":null},{"id":"2601.00537","title":"Boosting Segment Anything Model to Generalize Visually Non-Salient Scenarios","created":"2026-01-02","cited_by_count":3,"area":null},{"id":"2408.02039","title":"Pixel-Level Domain Adaptation: A New Perspective for Enhancing Weakly Supervised Semantic Segmentation","created":"2024-08-04","cited_by_count":2,"area":null},{"id":"2501.12632","title":"TeD-Loc: Text Distillation for Weakly Supervised Object Localization","created":"2025-01-22","cited_by_count":2,"area":null},{"id":"2503.22237","title":"SCHNet: SAM Marries CLIP for Human Parsing","created":"2025-03-28","cited_by_count":2,"area":null},{"id":"2505.16625","title":"Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation","created":"2025-05-22","cited_by_count":2,"area":null},{"id":"2410.13621","title":"EP-SAM: Weakly Supervised Histopathology Segmentation via Enhanced Prompt with Segment Anything","created":"2024-10-17","cited_by_count":1,"area":null},{"id":"2412.17601","title":"AFANet: Adaptive Frequency-Aware Network for Weakly-Supervised Few-Shot Semantic Segmentation","created":"2024-12-23","cited_by_count":1,"area":null},{"id":"2605.13674","title":"Weakly Supervised Segmentation as Semantic-Based Regularization","created":"2026-05-13","cited_by_count":1,"area":null},{"id":"2405.06586","title":"Enhancing Weakly Supervised Semantic Segmentation with Multi-modal Foundation Models: An End-to-End Approach","created":"2024-05-10","cited_by_count":0,"area":null},{"id":"2507.21587","title":"Emerging Trends in Pseudo-Label Refinement for Weakly Supervised Semantic Segmentation with Image-Level Supervision","created":"2025-07-29","cited_by_count":0,"area":null},{"id":"2509.17702","title":"Depth Edge Alignment Loss: DEALing with Depth in Weakly Supervised Semantic Segmentation","created":"2025-09-22","cited_by_count":0,"area":null},{"id":"2510.25134","title":"Region-CAM: Towards Accurate Object Regions in Class Activation Maps for Weakly Supervised Learning Tasks","created":"2025-10-29","cited_by_count":0,"area":null},{"id":"2605.24807","title":"CLIP-Guided SAM: Parameter-Efficient Semantic Conditioning for Promptable Segmentation","created":"2026-05-24","cited_by_count":0,"area":null},{"id":"2607.19877","title":"Robust Activation Map Rectification for Weakly Supervised Volumetric Segmentation: Temporal Coherence as a Free Lunch","created":"2026-07-22","cited_by_count":0,"area":null},{"id":"2608.05258","title":"Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI","created":"2026-08-05","cited_by_count":0,"area":null},{"id":"2609.34183","title":"CRF Loss is How Networks Should Learn Boundaries in Weakly Supervised Segmentation","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2610.10012","title":"Playing with Kruskal: algorithms for flat and hierarchical watershed cuts","created":"2026-10-07","cited_by_count":0,"area":null},{"id":"2610.14651","title":"TAG: Calibrating SAM3 for Universal Image Segmentation without Dense Supervision","created":"2026-10-10","cited_by_count":0,"area":null}],"next_cursor":null}