{"total":53,"rows":[{"id":"2204.14198","title":"Flamingo: a Visual Language Model for Few-Shot Learning","created":"2022-04-29","cited_by_count":4386,"area":"mllm"},{"id":"2109.11797","title":"CPT: Colorful Prompt Tuning for Pre-trained Vision-Language Models","created":"2021-09-24","cited_by_count":183,"area":"mllm"},{"id":"2211.11720","title":"Multitask Vision-Language Prompt Tuning","created":"2022-11-21","cited_by_count":51,"area":"mllm"},{"id":"2306.01879","title":"Revisiting the Role of Language Priors in Vision-Language Models","created":"2023-06-02","cited_by_count":50,"area":"mllm"},{"id":"2406.11171","title":"SUGARCREPE++ Dataset: Vision-Language Model Sensitivity to Semantic and Lexical Alterations","created":"2024-06-17","cited_by_count":42,"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":"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":"2310.00647","title":"Beyond Task Performance: Evaluating and Reducing the Flaws of Large Multimodal Models with In-Context Learning","created":"2023-10-01","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":"2411.04118","title":"Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress?","created":"2024-11-06","cited_by_count":28,"area":"mllm"},{"id":"2310.01651","title":"Fool Your (Vision and) Language Model With Embarrassingly Simple Permutations","created":"2023-10-02","cited_by_count":25,"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":"2410.22187","title":"Active Learning for Vision-Language Models","created":"2024-10-29","cited_by_count":15,"area":"mllm"},{"id":"2306.09996","title":"Investigating Prompting Techniques for Zero- and Few-Shot Visual Question Answering","created":"2023-06-16","cited_by_count":14,"area":"mllm"},{"id":"2403.14401","title":"Pensieve: Retrospect-then-Compare Mitigates Visual Hallucination","created":"2024-03-21","cited_by_count":14,"area":"mllm"},{"id":"2210.10841","title":"Prompting through Prototype: A Prototype-based Prompt Learning on Pretrained Vision-Language Models","created":"2022-10-19","cited_by_count":12,"area":"mllm"},{"id":"2504.08851","title":"Mimic In-Context Learning for Multimodal Tasks","created":"2025-04-11","cited_by_count":12,"area":"mllm"},{"id":"2407.00902","title":"From Introspection to Best Practices: Principled Analysis of Demonstrations in Multimodal In-Context Learning","created":"2024-07-01","cited_by_count":11,"area":"mllm"},{"id":"2510.04560","title":"ContextNav: Towards Agentic Multimodal In-Context Learning","created":"2025-10-06","cited_by_count":10,"area":"mllm"},{"id":"2406.09353","title":"Enhancing Domain Adaptation through Prompt Gradient Alignment","created":"2024-06-13","cited_by_count":8,"area":"mllm"},{"id":"2509.24621","title":"FreeRet: MLLMs as Training-Free Retrievers","created":"2025-09-29","cited_by_count":8,"area":"mllm"},{"id":"2304.08479","title":"Towards Robust Prompts on Vision-Language Models","created":"2023-04-17","cited_by_count":7,"area":"mllm"},{"id":"2604.02543","title":"Overconfidence and Calibration in Medical VQA: Empirical Findings and Hallucination-Aware Mitigation","created":"2026-04-02","cited_by_count":6,"area":"mllm"},{"id":"2505.20362","title":"VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration","created":"2025-05-26","cited_by_count":5,"area":"mllm"},{"id":"2506.23298","title":"Exposing and Mitigating Calibration Biases and Demographic Unfairness in MLLM Few-Shot In-Context Learning for Medical Image Classification","created":"2025-06-29","cited_by_count":5,"area":"mllm"},{"id":"2404.00318","title":"Cognitive Planning for Object Goal Navigation using Generative AI Models","created":"2024-03-30","cited_by_count":4,"area":"mllm"},{"id":"2503.07094","title":"A Novel Ophthalmic Benchmark for Evaluating Multimodal Large Language Models with Fundus Photographs and OCT Images","created":"2025-03-10","cited_by_count":4,"area":"mllm"},{"id":"2509.13395","title":"TICL: Text-Embedding KNN For Speech In-Context Learning Unlocks Speech Recognition Abilities of Large Multimodal Models","created":"2025-09-16","cited_by_count":3,"area":"mllm"},{"id":"2407.12879","title":"Large Visual-Language Models Are Also Good Classifiers: A Study of In-Context Multimodal Fake News Detection","created":"2024-07-16","cited_by_count":2,"area":"mllm"},{"id":"2503.02334","title":"BiasICL: In-Context Learning and Demographic Biases of Vision Language Models","created":"2025-03-04","cited_by_count":2,"area":"mllm"},{"id":"2604.07422","title":"Multimodal Large Language Models for Multi-Subject In-Context Image Generation","created":"2026-04-08","cited_by_count":2,"area":"mllm"},{"id":"2405.19675","title":"Knowledge-grounded Adaptation Strategy for Vision-language Models: Building Unique Case-set for Screening Mammograms for Residents Training","created":"2024-05-30","cited_by_count":1,"area":"mllm"},{"id":"2601.08871","title":"Semantic visually-guided acoustic highlighting with large vision-language models","created":"2026-01-12","cited_by_count":1,"area":"mllm"},{"id":"2601.14622","title":"Probing Prompt Design for Socially Compliant Robot Navigation with Vision Language Models","created":"2026-01-21","cited_by_count":1,"area":"mllm"},{"id":"2602.00344","title":"When RAG Hurts: Diagnosing and Mitigating Attention Distraction in Retrieval-Augmented LVLMs","created":"2026-01-30","cited_by_count":1,"area":"mllm"},{"id":"2603.15134","title":"Confusion-Aware In-Context-Learning for Vision-Language Models in Robotic Manipulation","created":"2026-03-16","cited_by_count":1,"area":"mllm"},{"id":"2603.19790","title":"Geometric Risk Control for Vision-Language Model OCR","created":"2026-03-20","cited_by_count":1,"area":"mllm"},{"id":"2605.18419","title":"Geometry-Aware Uncertainty Coresets for Robust Visual In-Context Learning in Histopathology","created":"2026-05-18","cited_by_count":1,"area":"mllm"},{"id":"2307.05591","title":"Linear Alignment of Vision-language Models for Image Captioning","created":"2023-07-10","cited_by_count":0,"area":"mllm"},{"id":"2603.11689","title":"Explicit Logic Channel for Validation and Enhancement of MLLMs on Zero-Shot Tasks","created":"2026-03-12","cited_by_count":0,"area":"mllm"},{"id":"2603.28387","title":"Prompts Without Evidence: How Neuroimaging Mentions Shift Clinical Vision-Language Model Predictions","created":"2026-03-30","cited_by_count":0,"area":"mllm"},{"id":"2605.28215","title":"Explaining is Harder Than Predicting Alone: Evaluating Concept-based Explanations of MLLMs as ICL Visual Classifiers","created":"2026-05-27","cited_by_count":0,"area":"mllm"},{"id":"2606.05874","title":"Evaluating Stochastic Collapse and Implicit Bias in Multimodal Large Language Models","created":"2026-06-04","cited_by_count":0,"area":"mllm"},{"id":"2606.21861","title":"Zero-Shot Vision-Language Models for Classroom Engagement Recognition: A Benchmark Study of Prompt Sensitivity and Cross-Dataset Generalization","created":"2026-06-20","cited_by_count":0,"area":"mllm"},{"id":"2607.00125","title":"Decompose, Compare, and Decide: Multimodal LLMs are Implicit Few-Shot Learners","created":"2026-06-30","cited_by_count":0,"area":"mllm"},{"id":"2607.18819","title":"In-Context Learning for Wound Classification with Small Multimodal Language Models","created":"2026-07-21","cited_by_count":0,"area":"mllm"},{"id":"2607.20351","title":"Test-Time Training for Modality Order Consistency in Vision-Language Models","created":"2026-07-22","cited_by_count":0,"area":"mllm"},{"id":"2607.29602","title":"FriendBench: Benchmarking Dyadic Familiarity Inference in Humans and Multimodal Large Language Models","created":"2026-07-31","cited_by_count":0,"area":"mllm"},{"id":"2608.19208","title":"When Irrelevant Text Matters: Affine Margin Shifts in Multimodal Large Language Models","created":"2026-06-12","cited_by_count":0,"area":"mllm"},{"id":"2608.26143","title":"Beyond Accuracy: A Qualitative Analysis of Vision-Language Models for Hate Speech Detection in Memes","created":"2026-06-27","cited_by_count":0,"area":"mllm"},{"id":"2608.30210","title":"Frontier vision-language models have overtaken young adults at detecting AI-generated portraits -- but not their calibration","created":"2026-08-31","cited_by_count":0,"area":"mllm"},{"id":"2609.31448","title":"ViSTA: A Simple Bridge Extends Visual Alignment to Clinical Time-Series Understanding in Multimodal LLMs","created":"2026-09-25","cited_by_count":0,"area":"mllm"},{"id":"2610.13111","title":"What Do Visual-Head Discovery Scores Actually Find? Attention Source, Necessity, and Random-Control Pitfalls in Vision-Language Models","created":"2026-10-09","cited_by_count":0,"area":"mllm"}],"next_cursor":null}