{"total":19,"rows":[{"id":"2510.09867","title":"Cluster-Aware Prompt Ensemble Learning for Few-Shot Vision-Language Model Adaptation","created":"2025-10-10","cited_by_count":7,"area":"mllm"},{"id":"2511.12639","title":"Medical Knowledge Intervention Prompt Tuning for Medical Image Classification","created":"2025-11-16","cited_by_count":5,"area":"mllm"},{"id":"2602.20423","title":"MedCLIPSeg: Probabilistic Vision-Language Adaptation for Data-Efficient and Generalizable Medical Image Segmentation","created":"2026-02-23","cited_by_count":4,"area":"mllm"},{"id":"2506.23903","title":"Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models","created":"2025-06-30","cited_by_count":3,"area":"mllm"},{"id":"2509.03740","title":"CLIP-SVD: Efficient and Interpretable Vision-Language Adaptation via Singular Values","created":"2025-09-03","cited_by_count":3,"area":"mllm"},{"id":"2510.22785","title":"Self-Calibrated Consistency can Fight Back for Adversarial Robustness in Vision-Language Models","created":"2025-10-26","cited_by_count":3,"area":"mllm"},{"id":"2601.08617","title":"SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning","created":"2026-01-13","cited_by_count":2,"area":"mllm"},{"id":"2506.14136","title":"Interpreting Biomedical VLMs on High-Imbalance Out-of-Distributions: An Insight into BiomedCLIP on Radiology","created":"2025-06-17","cited_by_count":1,"area":"mllm"},{"id":"2603.17079","title":"ACE-LoRA: Graph-Attentive Context Enhancement for Parameter-Efficient Adaptation of Medical Vision-Language Models","created":"2026-03-17","cited_by_count":1,"area":"mllm"},{"id":"2607.16726","title":"Can Experts Adapt Without Training? On Test-Time Modality Generalization in MVLMs","created":"2026-07-18","cited_by_count":1,"area":"mllm"},{"id":"2608.26848","title":"MedFG-VQA: Low-Frequency Memory and Graph Attention for Lightweight Medical VQA","created":"2026-08-27","cited_by_count":1,"area":"mllm"},{"id":"2510.15866","title":"BiomedXPro: Prompt Optimization for Explainable Diagnosis with Biomedical Vision Language Models","created":"2025-10-17","cited_by_count":0,"area":"mllm"},{"id":"2602.07017","title":"XAI-CLIP: ROI-Guided Perturbation Framework for Explainable Medical Image Segmentation in Multimodal Vision-Language Models","created":"2026-02-01","cited_by_count":0,"area":"mllm"},{"id":"2603.06340","title":"K-MaT: Knowledge-Anchored Manifold Transport for Cross-Modal Prompt Learning in Medical Imaging","created":"2026-03-06","cited_by_count":0,"area":"mllm"},{"id":"2605.26292","title":"Evi-Steer: Learning to Steer Biomedical Vision-Language Models through Efficient and Generalizable Evidential Tuning","created":"2026-05-25","cited_by_count":0,"area":"mllm"},{"id":"2606.04922","title":"Geometry-Aware Distillation for Prompt Tuning Biomedical Vision-Language Models","created":"2026-06-03","cited_by_count":0,"area":"mllm"},{"id":"2606.24740","title":"BioMedVR: Confusion-Aware Mixture-of-Prompt Experts for Biomedical Visual Reprogramming","created":"2026-06-23","cited_by_count":0,"area":"mllm"},{"id":"2607.09562","title":"TCLA: Training-Free Class-wise Logit Adaptation for Medical Vision-Language Models","created":"2026-07-10","cited_by_count":0,"area":"mllm"},{"id":"2607.28696","title":"Mitigating Class-Tail Undercoverage in Medical Vision-Language Models under Clinical Shift","created":"2026-07-30","cited_by_count":0,"area":"mllm"}],"next_cursor":null}