{"total":80,"rows":[{"id":"2304.00685","title":"Vision-Language Models for Vision Tasks: A Survey","created":"2023-04-03","cited_by_count":839,"area":"mllm"},{"id":"2209.07511","title":"Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models","created":"2022-09-15","cited_by_count":382,"area":"mllm"},{"id":"2308.06038","title":"Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning","created":"2023-08-11","cited_by_count":154,"area":"mllm"},{"id":"2211.16198","title":"SuS-X: Training-Free Name-Only Transfer of Vision-Language Models","created":"2022-11-28","cited_by_count":119,"area":"mllm"},{"id":"2405.18541","title":"Low-Rank Few-Shot Adaptation of Vision-Language Models","created":"2024-05-28","cited_by_count":104,"area":"mllm"},{"id":"2306.07282","title":"Waffling around for Performance: Visual Classification with Random Words and Broad Concepts","created":"2023-06-12","cited_by_count":101,"area":"mllm"},{"id":"2403.02781","title":"PromptKD: Unsupervised Prompt Distillation for Vision-Language Models","created":"2024-03-05","cited_by_count":72,"area":"mllm"},{"id":"2309.13625","title":"GraphAdapter: Tuning Vision-Language Models With Dual Knowledge Graph","created":"2023-09-24","cited_by_count":69,"area":"mllm"},{"id":"2311.16194","title":"BadCLIP: Trigger-Aware Prompt Learning for Backdoor Attacks on CLIP","created":"2023-11-26","cited_by_count":67,"area":"mllm"},{"id":"2405.02266","title":"On the test-time zero-shot generalization of vision-language models: Do we really need prompt learning?","created":"2024-05-03","cited_by_count":67,"area":"mllm"},{"id":"2308.14960","title":"Read-only Prompt Optimization for Vision-Language Few-shot Learning","created":"2023-08-29","cited_by_count":58,"area":"mllm"},{"id":"2211.12739","title":"Texts as Images in Prompt Tuning for Multi-Label Image Recognition","created":"2022-11-23","cited_by_count":51,"area":"mllm"},{"id":"2304.01752","title":"Black Box Few-Shot Adaptation for Vision-Language models","created":"2023-04-04","cited_by_count":39,"area":"mllm"},{"id":"2306.06048","title":"How Does Fine-Tuning Impact Out-of-Distribution Detection for Vision-Language Models?","created":"2023-06-09","cited_by_count":38,"area":"mllm"},{"id":"2401.02418","title":"Learning to Prompt with Text Only Supervision for Vision-Language Models","created":"2024-01-04","cited_by_count":38,"area":"mllm"},{"id":"2406.01837","title":"Boosting Vision-Language Models with Transduction","created":"2024-06-03","cited_by_count":36,"area":"mllm"},{"id":"2407.21794","title":"Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey","created":"2024-07-31","cited_by_count":36,"area":"mllm"},{"id":"2210.03794","title":"SVL-Adapter: Self-Supervised Adapter for Vision-Language Pretrained Models","created":"2022-10-07","cited_by_count":34,"area":"mllm"},{"id":"2211.16191","title":"SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image Classification","created":"2022-11-28","cited_by_count":34,"area":"mllm"},{"id":"2306.01669","title":"Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt Tuning","created":"2023-06-02","cited_by_count":30,"area":"mllm"},{"id":"2403.11755","title":"Meta-Prompting for Automating Zero-shot Visual Recognition with LLMs","created":"2024-03-18","cited_by_count":29,"area":"mllm"},{"id":"2404.04072","title":"Label Propagation for Zero-shot Classification with Vision-Language Models","created":"2024-04-05","cited_by_count":28,"area":"mllm"},{"id":"2407.04603","title":"AWT: Transferring Vision-Language Models via Augmentation, Weighting, and Transportation","created":"2024-07-05","cited_by_count":28,"area":"mllm"},{"id":"2310.19752","title":"Intra-Modal Proxy Learning for Zero-Shot Visual Categorization with CLIP","created":"2023-10-30","cited_by_count":27,"area":"mllm"},{"id":"2404.08958","title":"AMU-Tuning: Effective Logit Bias for CLIP-based Few-shot Learning","created":"2024-04-13","cited_by_count":22,"area":"mllm"},{"id":"2307.11978","title":"Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels?","created":"2023-07-22","cited_by_count":21,"area":"mllm"},{"id":"2406.10502","title":"Candidate Pseudolabel Learning: Enhancing Vision-Language Models by Prompt Tuning with Unlabeled Data","created":"2024-06-15","cited_by_count":20,"area":"mllm"},{"id":"2409.17805","title":"Cascade Prompt Learning for Vision-Language Model Adaptation","created":"2024-09-26","cited_by_count":19,"area":"mllm"},{"id":"2407.03056","title":"Improving Zero-shot Generalization of Learned Prompts via Unsupervised Knowledge Distillation","created":"2024-07-03","cited_by_count":18,"area":"mllm"},{"id":"2308.01313","title":"PerceptionCLIP: Visual Classification by Inferring and Conditioning on Contexts","created":"2023-08-02","cited_by_count":17,"area":"mllm"},{"id":"2303.09252","title":"GridCLIP: One-Stage Object Detection by Grid-Level CLIP Representation Learning","created":"2023-03-16","cited_by_count":15,"area":"mllm"},{"id":"2406.07236","title":"Let Go of Your Labels with Unsupervised Transfer","created":"2024-06-11","cited_by_count":14,"area":"mllm"},{"id":"2308.12919","title":"Realistic Unsupervised CLIP Fine-tuning with Universal Entropy Optimization","created":"2023-08-24","cited_by_count":13,"area":"mllm"},{"id":"2311.07593","title":"Follow-Up Differential Descriptions: Language Models Resolve Ambiguities for Image Classification","created":"2023-11-10","cited_by_count":13,"area":"mllm"},{"id":"2307.12980","title":"A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models","created":"2023-07-24","cited_by_count":12,"area":"mllm"},{"id":"2307.15460","title":"Cross-Modal Concept Learning and Inference for Vision-Language Models","created":"2023-07-28","cited_by_count":12,"area":"mllm"},{"id":"2311.11904","title":"LLMs as Visual Explainers: Advancing Image Classification with Evolving Visual Descriptions","created":"2023-11-20","cited_by_count":11,"area":"mllm"},{"id":"2506.24000","title":"The Illusion of Progress? A Critical Look at Test-Time Adaptation for Vision-Language Models","created":"2025-06-30","cited_by_count":8,"area":"mllm"},{"id":"2305.11439","title":"Few-Shot Learning with Visual Distribution Calibration and Cross-Modal Distribution Alignment","created":"2023-05-19","cited_by_count":7,"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":"2508.05547","title":"Adapting Vision-Language Models Without Labels: A Comprehensive Survey","created":"2025-08-07","cited_by_count":7,"area":"mllm"},{"id":"2405.16417","title":"CRoFT: Robust Fine-Tuning with Concurrent Optimization for OOD Generalization and Open-Set OOD Detection","created":"2024-05-26","cited_by_count":6,"area":"mllm"},{"id":"2405.20141","title":"OpenDAS: Open-Vocabulary Domain Adaptation for 2D and 3D Segmentation","created":"2024-05-30","cited_by_count":6,"area":"mllm"},{"id":"2407.07638","title":"Tuning Vision-Language Models with Candidate Labels by Prompt Alignment","created":"2024-07-10","cited_by_count":6,"area":"mllm"},{"id":"2410.06154","title":"GLOV: Guided Large Language Models as Implicit Optimizers for Vision Language Models","created":"2024-10-08","cited_by_count":6,"area":"mllm"},{"id":"2504.02349","title":"Large (Vision) Language Models are Unsupervised In-Context Learners","created":"2025-04-03","cited_by_count":6,"area":"mllm"},{"id":"2308.11507","title":"Unsupervised Prototype Adapter for Vision-Language Models","created":"2023-08-22","cited_by_count":5,"area":"mllm"},{"id":"2404.16339","title":"Training-Free Unsupervised Prompt for Vision-Language Models","created":"2024-04-25","cited_by_count":5,"area":"mllm"},{"id":"2408.08855","title":"DPA: Dual Prototypes Alignment for Unsupervised Adaptation of Vision-Language Models","created":"2024-08-16","cited_by_count":5,"area":"mllm"},{"id":"2508.11262","title":"Vision-Language Models display a strong gender bias","created":"2025-08-15","cited_by_count":5,"area":"mllm"},{"id":"2307.11227","title":"UP-DP: Unsupervised Prompt Learning for Data Pre-Selection with Vision-Language Models","created":"2023-07-20","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":"2508.02671","title":"Raw Data Matters: Enhancing Prompt Tuning by Internal Augmentation on Vision-Language Models","created":"2025-08-04","cited_by_count":3,"area":"mllm"},{"id":"2502.20292","title":"Visual Adaptive Prompting for Compositional Zero-Shot Learning","created":"2025-02-27","cited_by_count":2,"area":"mllm"},{"id":"2409.12597","title":"LARE: Latent Augmentation using Regional Embedding with Vision-Language Model","created":"2024-09-19","cited_by_count":1,"area":"mllm"},{"id":"2410.11686","title":"A Survey of Low-shot Vision-Language Model Adaptation via Representer Theorem","created":"2024-10-15","cited_by_count":1,"area":"mllm"},{"id":"2502.18735","title":"QueryAdapter: Rapid Adaptation of Vision-Language Models in Response to Natural Language Queries","created":"2025-02-26","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":"2503.15886","title":"Enhancing Zero-Shot Image Recognition in Vision-Language Models through Human-like Concept Guidance","created":"2025-03-20","cited_by_count":1,"area":"mllm"},{"id":"2505.18053","title":"FDBPL: Faster Distillation-Based Prompt Learning for Region-Aware Vision-Language Models Adaptation","created":"2025-05-23","cited_by_count":1,"area":"mllm"},{"id":"2507.09615","title":"Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score","created":"2025-07-13","cited_by_count":1,"area":"mllm"},{"id":"2510.11296","title":"$\\Delta \\mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization","created":"2025-10-13","cited_by_count":1,"area":"mllm"},{"id":"2603.08942","title":"BiCLIP: Domain Canonicalization via Structured Geometric Transformation","created":"2026-03-09","cited_by_count":1,"area":"mllm"},{"id":"2309.14928","title":"Noise-Tolerant Few-Shot Unsupervised Adapter for Vision-Language Models","created":"2023-09-26","cited_by_count":0,"area":"mllm"},{"id":"2312.11570","title":"Understanding the Multi-modal Prompts of the Pre-trained Vision-Language Model","created":"2023-12-18","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":"2504.11930","title":"Beyond Words: Augmenting Discriminative Richness via Diffusions in Unsupervised Prompt Learning","created":"2025-04-16","cited_by_count":0,"area":"mllm"},{"id":"2505.02056","title":"Handling Imbalanced Pseudolabels for Vision-Language Models with Concept Alignment and Confusion-Aware Calibrated Margin","created":"2025-05-04","cited_by_count":0,"area":"mllm"},{"id":"2507.22075","title":"Prototype-Guided Pseudo-Labeling with Neighborhood-Aware Consistency for Unsupervised Adaptation","created":"2025-07-22","cited_by_count":0,"area":"mllm"},{"id":"2509.22229","title":"A Tale of Two Experts: Cooperative Learning for Source-Free Unsupervised Domain Adaptation","created":"2025-09-26","cited_by_count":0,"area":"mllm"},{"id":"2510.02270","title":"microCLIP: Unsupervised CLIP Adaptation via Coarse-Fine Token Fusion for Fine-Grained Image Classification","created":"2025-10-02","cited_by_count":0,"area":"mllm"},{"id":"2510.20477","title":"Bi-CoG: Bi-Consistency-Guided Self-Training for Vision-Language Models","created":"2025-10-23","cited_by_count":0,"area":"mllm"},{"id":"2602.04337","title":"Fine-tuning Pre-trained Vision-Language Models in a Human-Annotation-Free Manner","created":"2026-02-04","cited_by_count":0,"area":"mllm"},{"id":"2603.07911","title":"Beyond Heuristic Prompting: A Concept-Guided Bayesian Framework for Zero-Shot Image Recognition","created":"2026-03-09","cited_by_count":0,"area":"mllm"},{"id":"2604.06614","title":"Holistic Optimal Label Selection for Robust Prompt Learning under Partial Labels","created":"2026-04-08","cited_by_count":0,"area":"mllm"},{"id":"2605.00591","title":"Intrinsic Gradient Suppression for Label-Noise Prompt Tuning in Vision-Language Models","created":"2026-05-01","cited_by_count":0,"area":"mllm"},{"id":"2607.03900","title":"USE: A Unified Self-Ensembling Framework for Test-Time Prompt Tuning","created":"2026-07-04","cited_by_count":0,"area":"mllm"},{"id":"2607.09008","title":"C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes","created":"2026-07-10","cited_by_count":0,"area":"mllm"},{"id":"2608.28145","title":"Dual-Stream Semantic Guidance with Prototype Anchor Calibration for Source-Fully-Free Adaptation of Vision-Language Models","created":"2026-08-28","cited_by_count":0,"area":"mllm"},{"id":"2610.07903","title":"Unsupervised Long-Tailed Adaptation of Vision-Language Models","created":"2026-10-06","cited_by_count":0,"area":"mllm"}],"next_cursor":null}