{"total":49,"rows":[{"id":"2503.13443","title":"DPC: Dual-Prompt Collaboration for Tuning Vision-Language Models","created":"2025-03-17","cited_by_count":18,"area":"mllm"},{"id":"2501.11175","title":"ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models","created":"2025-01-19","cited_by_count":15,"area":"mllm"},{"id":"2410.15397","title":"IPO: Interpretable Prompt Optimization for Vision-Language Models","created":"2024-10-20","cited_by_count":14,"area":"mllm"},{"id":"2505.05180","title":"OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning","created":"2025-05-08","cited_by_count":12,"area":"mllm"},{"id":"2507.14976","title":"Hierarchical Cross-modal Prompt Learning for Vision-Language Models","created":"2025-07-20","cited_by_count":12,"area":"mllm"},{"id":"2412.11509","title":"Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves","created":"2024-12-16","cited_by_count":10,"area":"mllm"},{"id":"2410.02681","title":"Understanding and Mitigating Miscalibration in Prompt Tuning for Vision-Language Models","created":"2024-10-03","cited_by_count":9,"area":"mllm"},{"id":"2407.02350","title":"Conceptual Codebook Learning for Vision-Language Models","created":"2024-07-02","cited_by_count":7,"area":"mllm"},{"id":"2410.11201","title":"Tree of Attributes Prompt Learning for Vision-Language Models","created":"2024-10-15","cited_by_count":7,"area":"mllm"},{"id":"2410.12183","title":"TransAgent: Transfer Vision-Language Foundation Models with Heterogeneous Agent Collaboration","created":"2024-10-16","cited_by_count":5,"area":"mllm"},{"id":"2411.16018","title":"Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models","created":"2024-11-25","cited_by_count":5,"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":"2602.21397","title":"MMLoP: Multi-Modal Low-Rank Prompting for Efficient Vision-Language Adaptation","created":"2026-02-24","cited_by_count":5,"area":"mllm"},{"id":"2411.11223","title":"Efficient Transfer Learning for Video-language Foundation Models","created":"2024-11-18","cited_by_count":4,"area":"mllm"},{"id":"2503.08906","title":"Prompt-OT: An Optimal Transport Regularization Paradigm for Knowledge Preservation in Vision-Language Model Adaptation","created":"2025-03-11","cited_by_count":4,"area":"mllm"},{"id":"2504.20860","title":"FedMVP: Federated Multimodal Visual Prompt Tuning for Vision-Language Models","created":"2025-04-29","cited_by_count":4,"area":"mllm"},{"id":"2505.10088","title":"MMRL++: Parameter-Efficient and Interaction-Aware Representation Learning for Vision-Language Models","created":"2025-05-15","cited_by_count":4,"area":"mllm"},{"id":"2407.04207","title":"Elevating All Zero-Shot Sketch-Based Image Retrieval Through Multimodal Prompt Learning","created":"2024-07-05","cited_by_count":3,"area":"mllm"},{"id":"2502.11969","title":"Learning Generalizable Prompt for CLIP with Class Similarity Knowledge","created":"2025-02-17","cited_by_count":3,"area":"mllm"},{"id":"2503.01263","title":"Generalizable Prompt Learning of CLIP: A Brief Overview","created":"2025-03-03","cited_by_count":3,"area":"mllm"},{"id":"2503.18160","title":"MAO: Efficient Model-Agnostic Optimization of Prompt Tuning for Vision-Language Models","created":"2025-03-23","cited_by_count":3,"area":"mllm"},{"id":"2511.22664","title":"VaMP: Variational Multi-Modal Prompt Learning for Vision-Language Models","created":"2025-11-27","cited_by_count":3,"area":"mllm"},{"id":"2408.14812","title":"HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling","created":"2024-08-27","cited_by_count":2,"area":"mllm"},{"id":"2409.01835","title":"Towards Generative Class Prompt Learning for Fine-grained Visual Recognition","created":"2024-09-03","cited_by_count":2,"area":"mllm"},{"id":"2410.13030","title":"Sensitivity of Generative VLMs to Semantically and Lexically Altered Prompts","created":"2024-10-16","cited_by_count":2,"area":"mllm"},{"id":"2501.07769","title":"BMIP: Bi-directional Modality Interaction Prompt Learning for VLM","created":"2025-01-14","cited_by_count":2,"area":"mllm"},{"id":"2506.01724","title":"Active Learning via Vision-Language Model Adaptation with Open Data","created":"2025-06-02","cited_by_count":2,"area":"mllm"},{"id":"2507.08410","title":"Multi-modal Mutual-Guidance Conditional Prompt Learning for Vision-Language Models","created":"2025-07-11","cited_by_count":2,"area":"mllm"},{"id":"2604.12403","title":"Dual-Modality Anchor-Guided Filtering for Test-time Prompt Tuning","created":"2026-04-14","cited_by_count":2,"area":"mllm"},{"id":"2309.07866","title":"Gradient constrained sharpness-aware prompt learning for vision-language models","created":"2023-09-14","cited_by_count":1,"area":"mllm"},{"id":"2407.15894","title":"Craft: Cross-modal Aligned Features Improve Robustness of Prompt Tuning","created":"2024-07-22","cited_by_count":1,"area":"mllm"},{"id":"2412.19087","title":"MoPD: Mixture-of-Prompts Distillation for Vision-Language Models","created":"2024-12-26","cited_by_count":1,"area":"mllm"},{"id":"2505.07675","title":"Simple yet Effective Semi-supervised Knowledge Distillation from Vision-Language Models via Dual-Head Optimization","created":"2025-05-12","cited_by_count":1,"area":"mllm"},{"id":"2508.10645","title":"SemPT: Semantic Prompt Tuning for Vision-Language Models","created":"2025-08-14","cited_by_count":1,"area":"mllm"},{"id":"2602.19198","title":"Prompt Tuning for CLIP on the Pretrained Manifold","created":"2026-02-22","cited_by_count":1,"area":"mllm"},{"id":"2603.08708","title":"FVG-PT: Adaptive Foreground View-Guided Prompt Tuning for Vision-Language Models","created":"2026-03-09","cited_by_count":1,"area":"mllm"},{"id":"2605.30968","title":"Variational Adapter for Cross-modal Similarity Representation","created":"2026-05-29","cited_by_count":1,"area":"mllm"},{"id":"2405.07921","title":"Can Better Text Semantics in Prompt Tuning Improve VLM Generalization?","created":"2024-05-13","cited_by_count":0,"area":"mllm"},{"id":"2501.14148","title":"SelfPrompt: Confidence-Aware Semi-Supervised Tuning for Robust Vision-Language Model Adaptation","created":"2025-01-24","cited_by_count":0,"area":"mllm"},{"id":"2502.19777","title":"InPK: Infusing Prior Knowledge into Prompt for Vision-Language Models","created":"2025-02-27","cited_by_count":0,"area":"mllm"},{"id":"2506.00910","title":"PCoreSet: Effective Active Learning through Knowledge Distillation from Vision-Language Models","created":"2025-06-01","cited_by_count":0,"area":"mllm"},{"id":"2511.03367","title":"Decoupling Augmentation Bias in Prompt Learning for Vision-Language Models","created":"2025-11-05","cited_by_count":0,"area":"mllm"},{"id":"2603.11617","title":"Noise-aware few-shot learning through bi-directional multi-view prompt alignment","created":"2026-03-12","cited_by_count":0,"area":"mllm"},{"id":"2604.03980","title":"Gram-Anchored Prompt Learning for Vision-Language Models via Second-Order Statistics","created":"2026-04-05","cited_by_count":0,"area":"mllm"},{"id":"2604.15703","title":"P3T: Prototypical Point-level Prompt Tuning with Enhanced Generalization for 3D Vision-Language Models","created":"2026-04-17","cited_by_count":0,"area":"mllm"},{"id":"2605.11939","title":"Cluster-Aware Neural Collapse Prompt Tuning for Long-Tailed Generalization of Vision-Language Models","created":"2026-05-12","cited_by_count":0,"area":"mllm"},{"id":"2605.15615","title":"Neutral-Reference Prompting for Vision-Language Models","created":"2026-05-15","cited_by_count":0,"area":"mllm"},{"id":"2609.24276","title":"Hierarchical Prompt Learning for Hyperbolic Vision-Language Models","created":"2026-09-21","cited_by_count":0,"area":"mllm"},{"id":"2609.30434","title":"ProCAP: Probabilistic Cross-Attentive Prompt Learning for Vision-Language Models","created":"2026-09-24","cited_by_count":0,"area":"mllm"}],"next_cursor":null}