{"total":45,"rows":[{"id":"2406.01837","title":"Boosting Vision-Language Models with Transduction","created":"2024-06-03","cited_by_count":36,"area":"mllm"},{"id":"2501.03729","title":"Realistic Test-Time Adaptation of Vision-Language Models","created":"2025-01-07","cited_by_count":14,"area":"mllm"},{"id":"2506.07016","title":"MAGNET: A Multi-agent Framework for Finding Audio-Visual Needles by Reasoning over Multi-Video Haystacks","created":"2025-06-08","cited_by_count":12,"area":"mllm"},{"id":"2505.15130","title":"Few-Shot Adversarial Low-Rank Fine-Tuning of Vision-Language Models","created":"2025-05-21","cited_by_count":9,"area":"mllm"},{"id":"2501.04352","title":"Online Gaussian Test-Time Adaptation of Vision-Language Models","created":"2025-01-08","cited_by_count":7,"area":"mllm"},{"id":"2502.02069","title":"LoRA-TTT: Low-Rank Test-Time Training for Vision-Language Models","created":"2025-02-04","cited_by_count":7,"area":"mllm"},{"id":"2503.05776","title":"FAA-CLIP: Federated Adversarial Adaptation of CLIP","created":"2025-02-26","cited_by_count":5,"area":"mllm"},{"id":"2602.21397","title":"MMLoP: Multi-Modal Low-Rank Prompting for Efficient Vision-Language Adaptation","created":"2026-02-24","cited_by_count":5,"area":"mllm"},{"id":"2507.05394","title":"pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language Models","created":"2025-07-07","cited_by_count":4,"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":"2603.13341","title":"Mind the Discriminability Trap in Source-Free Cross-domain Few-shot Learning","created":"2026-03-07","cited_by_count":4,"area":"mllm"},{"id":"2603.17655","title":"Interpretable Cross-Domain Few-Shot Learning with Rectified Target-Domain Local Alignment","created":"2026-03-18","cited_by_count":4,"area":"mllm"},{"id":"2410.08211","title":"LatteCLIP: Unsupervised CLIP Fine-Tuning via LMM-Synthetic Texts","created":"2024-10-10","cited_by_count":3,"area":"mllm"},{"id":"2507.13089","title":"GLAD: Generalizable Tuning for Vision-Language Models","created":"2025-07-17","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":"2505.11535","title":"Bridging Human Oversight and Black-box Driver Assistance: Vision-Language Models for Predictive Alerting in Lane Keeping Assist Systems","created":"2025-05-14","cited_by_count":2,"area":"mllm"},{"id":"2505.21382","title":"DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models","created":"2025-05-27","cited_by_count":2,"area":"mllm"},{"id":"2603.24528","title":"Cross-Modal Prototype Alignment and Mixing for Training-Free Few-Shot Classification","created":"2026-03-25","cited_by_count":2,"area":"mllm"},{"id":"2605.25799","title":"Addressing Exacerbated Attention Sink for Source-Free Cross-Domain Few-Shot Learning","created":"2026-05-25","cited_by_count":2,"area":"mllm"},{"id":"2410.05270","title":"CLIP's Visual Embedding Projector is a Few-shot Cornucopia","created":"2024-10-07","cited_by_count":1,"area":"mllm"},{"id":"2503.06993","title":"CAPT: Class-Aware Prompt Tuning for Federated Long-Tailed Learning with Vision-Language Model","created":"2025-03-10","cited_by_count":1,"area":"mllm"},{"id":"2504.12436","title":"Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation","created":"2025-04-16","cited_by_count":1,"area":"mllm"},{"id":"2505.12912","title":"Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption","created":"2025-05-19","cited_by_count":1,"area":"mllm"},{"id":"2506.04005","title":"Vocabulary-free few-shot learning for Vision-Language Models","created":"2025-06-04","cited_by_count":1,"area":"mllm"},{"id":"2509.20792","title":"DAC-LoRA: Dynamic Adversarial Curriculum for Efficient and Robust Few-Shot Adaptation","created":"2025-09-25","cited_by_count":1,"area":"mllm"},{"id":"2510.07135","title":"Few-Shot Adaptation Benchmark for Remote Sensing Vision-Language Models","created":"2025-10-08","cited_by_count":1,"area":"mllm"},{"id":"2510.21175","title":"Memory-Free Continual Learning with Null Space Adaptation for Zero-Shot Vision-Language Models","created":"2025-10-24","cited_by_count":1,"area":"mllm"},{"id":"2602.20409","title":"CLIPoint3D: Language-Grounded Few-Shot Unsupervised 3D Point Cloud Domain Adaptation","created":"2026-02-23","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":"2605.05054","title":"Direct Product Flow Matching: Decoupling Radial and Angular Dynamics for Few-Shot Adaptation","created":"2026-05-06","cited_by_count":1,"area":"mllm"},{"id":"2605.29776","title":"Improving CLIP Adaptation by Breaking Tail Alignment for Source-Free Cross-Domain Few-Shot Learning","created":"2026-05-28","cited_by_count":1,"area":"mllm"},{"id":"2505.21698","title":"Adapting Foundation Vision-Language Models to Medical Diagnosis via Query-Driven Expert Bridging","created":"2025-05-27","cited_by_count":0,"area":"mllm"},{"id":"2507.21521","title":"Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration","created":"2025-07-29","cited_by_count":0,"area":"mllm"},{"id":"2508.07835","title":"Effortless Vision-Language Model Specialization in Histopathology without Annotation","created":"2025-08-11","cited_by_count":0,"area":"mllm"},{"id":"2509.00305","title":"Language-Aware Information Maximization for Transductive Few-Shot CLIP","created":"2025-08-30","cited_by_count":0,"area":"mllm"},{"id":"2512.00597","title":"Scaling Down to Scale Up: Towards Operationally-Efficient and Deployable Clinical Models via Cross-Modal Low-Rank Adaptation for Medical Vision-Language Models","created":"2025-11-29","cited_by_count":0,"area":"mllm"},{"id":"2512.17313","title":"Auxiliary Descriptive Knowledge for Few-Shot Adaptation of Vision-Language Model","created":"2025-12-19","cited_by_count":0,"area":"mllm"},{"id":"2512.19219","title":"Selective LoRA for Visual Tokens and Attention Heads","created":"2025-12-22","cited_by_count":0,"area":"mllm"},{"id":"2603.00720","title":"MARS: Harmonizing Multimodal Convergence via Adaptive Rank Search","created":"2026-02-28","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":"2605.27900","title":"Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning","created":"2026-05-27","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":"2607.10985","title":"MED-DSLC: Multi-Expert-Domain Classification via Domain Supervision and Logit Calibration","created":"2026-07-13","cited_by_count":0,"area":"mllm"},{"id":"2609.09462","title":"Low-Rank Prompt Learning for Vision-Language Models with Fixed-Token Bases","created":"2026-09-08","cited_by_count":0,"area":"mllm"},{"id":"2609.32203","title":"Kernel-Based Steering of CLIP with Vision-Language Model Preferences","created":"2026-09-26","cited_by_count":0,"area":"mllm"}],"next_cursor":null}