{"total":97,"rows":[{"id":"2308.02490","title":"MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities","created":"2023-08-04","cited_by_count":1239,"area":"mllm"},{"id":"2304.00685","title":"Vision-Language Models for Vision Tasks: A Survey","created":"2023-04-03","cited_by_count":839,"area":"mllm"},{"id":"2311.12320","title":"A Survey on Multimodal Large Language Models for Autonomous Driving","created":"2023-11-21","cited_by_count":290,"area":"mllm"},{"id":"2310.16045","title":"Woodpecker: Hallucination Correction for Multimodal Large Language Models","created":"2023-10-24","cited_by_count":226,"area":"mllm"},{"id":"2310.02992","title":"Kosmos-G: Generating Images in Context with Multimodal Large Language Models","created":"2023-10-04","cited_by_count":105,"area":"mllm"},{"id":"2305.05726","title":"Vision-Language Models in Remote Sensing: Current Progress and Future Trends","created":"2023-05-09","cited_by_count":93,"area":"mllm"},{"id":"2312.06960","title":"Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignment","created":"2023-12-12","cited_by_count":71,"area":"mllm"},{"id":"2401.06341","title":"AffordanceLLM: Grounding Affordance from Vision Language Models","created":"2024-01-12","cited_by_count":55,"area":"mllm"},{"id":"2405.10255","title":"When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models","created":"2024-05-16","cited_by_count":43,"area":"mllm"},{"id":"2502.04263","title":"Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion","created":"2025-02-06","cited_by_count":43,"area":"mllm"},{"id":"2403.02969","title":"Multi-modal Instruction Tuned LLMs with Fine-grained Visual Perception","created":"2024-03-05","cited_by_count":39,"area":"mllm"},{"id":"2304.01457","title":"Exploring Vision-Language Models for Imbalanced Learning","created":"2023-04-04","cited_by_count":35,"area":"mllm"},{"id":"2411.15421","title":"OphCLIP: Hierarchical Retrieval-Augmented Learning for Ophthalmic Surgical Video-Language Pretraining","created":"2024-11-23","cited_by_count":29,"area":"mllm"},{"id":"2306.09244","title":"Text Promptable Surgical Instrument Segmentation with Vision-Language Models","created":"2023-06-15","cited_by_count":28,"area":"mllm"},{"id":"2308.07706","title":"Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models","created":"2023-08-15","cited_by_count":28,"area":"mllm"},{"id":"2312.04016","title":"PartDistill: 3D Shape Part Segmentation by Vision-Language Model Distillation","created":"2023-12-07","cited_by_count":28,"area":"mllm"},{"id":"2404.19287","title":"Revisiting the Adversarial Robustness of Vision Language Models: a Multimodal Perspective","created":"2024-04-30","cited_by_count":28,"area":"mllm"},{"id":"2404.00851","title":"Prompt Learning via Meta-Regularization","created":"2024-04-01","cited_by_count":26,"area":"mllm"},{"id":"2310.07730","title":"Domain-Controlled Prompt Learning","created":"2023-09-30","cited_by_count":22,"area":"mllm"},{"id":"2504.10465","title":"Pixel-SAIL: Single Transformer For Pixel-Grounded Understanding","created":"2025-04-14","cited_by_count":22,"area":"mllm"},{"id":"2510.15042","title":"Comprehensive language-image pre-training for 3D medical image understanding","created":"2025-10-16","cited_by_count":22,"area":"mllm"},{"id":"2403.09410","title":"XCoOp: Explainable Prompt Learning for Computer-Aided Diagnosis via Concept-guided Context Optimization","created":"2024-03-14","cited_by_count":20,"area":"mllm"},{"id":"2408.10202","title":"SANER: Annotation-free Societal Attribute Neutralizer for Debiasing CLIP","created":"2024-08-19","cited_by_count":19,"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":"2505.14361","title":"Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives","created":"2025-05-20","cited_by_count":18,"area":"mllm"},{"id":"2312.08878","title":"Domain Prompt Learning with Quaternion Networks","created":"2023-12-12","cited_by_count":17,"area":"mllm"},{"id":"2410.22187","title":"Active Learning for Vision-Language Models","created":"2024-10-29","cited_by_count":15,"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":"2208.13474","title":"Prompt Tuning with Soft Context Sharing for Vision-Language Models","created":"2022-08-29","cited_by_count":13,"area":"mllm"},{"id":"2405.06196","title":"VLSM-Adapter: Finetuning Vision-Language Segmentation Efficiently with Lightweight Blocks","created":"2024-05-10","cited_by_count":11,"area":"mllm"},{"id":"2411.03313","title":"Classification Done Right for Vision-Language Pre-Training","created":"2024-11-05","cited_by_count":10,"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":"2406.13912","title":"From Descriptive Richness to Bias: Unveiling the Dark Side of Generative Image Caption Enrichment","created":"2024-06-20","cited_by_count":8,"area":"mllm"},{"id":"2311.17315","title":"Explaining CLIP's performance disparities on data from blind/low vision users","created":"2023-11-29","cited_by_count":7,"area":"mllm"},{"id":"2312.17345","title":"3VL: Using Trees to Improve Vision-Language Models' Interpretability","created":"2023-12-28","cited_by_count":7,"area":"mllm"},{"id":"2409.10419","title":"HiFi-CS: Towards Open Vocabulary Visual Grounding For Robotic Grasping Using Vision-Language Models","created":"2024-09-16","cited_by_count":7,"area":"mllm"},{"id":"2506.18504","title":"Generalizing vision-language models to novel domains: A comprehensive survey","created":"2025-06-23","cited_by_count":7,"area":"mllm"},{"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":"2407.10704","title":"Quantized Prompt for Efficient Generalization of Vision-Language Models","created":"2024-07-15","cited_by_count":6,"area":"mllm"},{"id":"2506.08849","title":"Adapting Vision-Language Foundation Model for Next Generation Medical Ultrasound Image Analysis","created":"2025-06-10","cited_by_count":6,"area":"mllm"},{"id":"2506.23785","title":"Visual Textualization for Image Prompted Object Detection","created":"2025-06-30","cited_by_count":6,"area":"mllm"},{"id":"2509.18610","title":"SINGER: An Onboard Generalist Vision-Language Navigation Policy for Drones","created":"2025-09-23","cited_by_count":6,"area":"mllm"},{"id":"2304.06212","title":"[CLS] Token is All You Need for Zero-Shot Semantic Segmentation","created":"2023-04-13","cited_by_count":5,"area":"mllm"},{"id":"2312.10813","title":"Towards Efficient Vision-Language Tuning: More Information Density, More Generalizability","created":"2023-12-17","cited_by_count":5,"area":"mllm"},{"id":"2401.14502","title":"MResT: Multi-Resolution Sensing for Real-Time Control with Vision-Language Models","created":"2024-01-25","cited_by_count":5,"area":"mllm"},{"id":"2404.11046","title":"Lightweight Unsupervised Federated Learning with Pretrained Vision Language Model","created":"2024-04-17","cited_by_count":5,"area":"mllm"},{"id":"2603.17520","title":"PCA-Seg: Revisiting Cost Aggregation for Open-Vocabulary Semantic and Part Segmentation","created":"2026-03-18","cited_by_count":5,"area":"mllm"},{"id":"2308.01532","title":"MA-FSAR: Multimodal Adaptation of CLIP for Few-Shot Action Recognition","created":"2023-08-03","cited_by_count":4,"area":"mllm"},{"id":"2401.00268","title":"COMMA: Co-Articulated Multi-Modal Learning","created":"2023-12-30","cited_by_count":4,"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":"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":"2604.02327","title":"Steerable Visual Representations","created":"2026-04-02","cited_by_count":4,"area":"mllm"},{"id":"2311.16475","title":"Enhancing HOI Detection with Contextual Cues from Large Vision-Language Models","created":"2023-11-26","cited_by_count":3,"area":"mllm"},{"id":"2410.05239","title":"TuneVLSeg: Prompt Tuning Benchmark for Vision-Language Segmentation Models","created":"2024-10-07","cited_by_count":3,"area":"mllm"},{"id":"2506.05439","title":"LLMs Can Compensate for Deficiencies in Visual Representations","created":"2025-06-05","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":"2511.22664","title":"VaMP: Variational Multi-Modal Prompt Learning for Vision-Language Models","created":"2025-11-27","cited_by_count":3,"area":"mllm"},{"id":"2405.08668","title":"Promoting AI Equity in Science: Generalized Domain Prompt Learning for Accessible VLM Research","created":"2024-05-14","cited_by_count":2,"area":"mllm"},{"id":"2412.06760","title":"Ranking-aware adapter for text-driven image ordering with CLIP","created":"2024-12-09","cited_by_count":2,"area":"mllm"},{"id":"2501.06031","title":"Generate, Transduct, Adapt: Iterative Transduction with VLMs","created":"2025-01-10","cited_by_count":2,"area":"mllm"},{"id":"2505.02971","title":"Adversarial Robustness Analysis of Vision-Language Models in Medical Image Segmentation","created":"2025-05-05","cited_by_count":2,"area":"mllm"},{"id":"2506.08678","title":"ATAS: Any-to-Any Self-Distillation for Enhanced Open-Vocabulary Dense Prediction","created":"2025-06-10","cited_by_count":2,"area":"mllm"},{"id":"2508.08644","title":"AME: Aligned Manifold Entropy for Robust Vision-Language Distillation","created":"2025-08-12","cited_by_count":2,"area":"mllm"},{"id":"2511.00191","title":"A Retrospect to Multi-prompt Learning across Vision and Language","created":"2025-10-31","cited_by_count":2,"area":"mllm"},{"id":"2512.13855","title":"Improvise, Adapt, Overcome -- Telescopic Adapters for Efficient Fine-tuning of Vision Language Models in Medical Imaging","created":"2025-12-15","cited_by_count":2,"area":"mllm"},{"id":"2602.17799","title":"Enabling Training-Free Text-Based Remote Sensing Segmentation","created":"2026-02-19","cited_by_count":2,"area":"mllm"},{"id":"2603.19026","title":"Rethinking MLLM Itself as a Segmenter with a Single Segmentation Token","created":"2026-03-19","cited_by_count":2,"area":"mllm"},{"id":"2603.20554","title":"When Negation Is a Geometry Problem in Vision-Language Models","created":"2026-03-20","cited_by_count":2,"area":"mllm"},{"id":"2404.00675","title":"LLM meets Vision-Language Models for Zero-Shot One-Class Classification","created":"2024-03-31","cited_by_count":1,"area":"mllm"},{"id":"2503.02897","title":"ClipGrader: Leveraging Vision-Language Models for Robust Label Quality Assessment in Object Detection","created":"2025-03-03","cited_by_count":1,"area":"mllm"},{"id":"2509.05154","title":"VLSM-Ensemble: Ensembling CLIP-based Vision-Language Models for Enhanced Medical Image Segmentation","created":"2025-09-05","cited_by_count":1,"area":"mllm"},{"id":"2511.20732","title":"Prompt-Aware Adaptive Elastic Weight Consolidation for Continual Learning in Medical Vision-Language Models","created":"2025-11-25","cited_by_count":1,"area":"mllm"},{"id":"2512.01148","title":"SocialFusion: Addressing Social Degradation in Pre-trained Vision-Language Models","created":"2025-11-30","cited_by_count":1,"area":"mllm"},{"id":"2604.11576","title":"Finetune Like You Pretrain: Boosting Zero-shot Adversarial Robustness in Vision-language Models","created":"2026-04-13","cited_by_count":1,"area":"mllm"},{"id":"2607.23271","title":"What CLIP Knows but Cannot Say: Recovering Negation from Frozen Intermediate Features","created":"2026-07-25","cited_by_count":1,"area":"mllm"},{"id":"2307.06795","title":"Leveraging Vision-Language Foundation Models for Fine-Grained Downstream Tasks","created":"2023-07-13","cited_by_count":0,"area":"mllm"},{"id":"2310.04981","title":"Compositional Semantics for Open Vocabulary Spatio-semantic Representations","created":"2023-10-08","cited_by_count":0,"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":"2407.14229","title":"Words2Contact: Identifying Support Contacts from Verbal Instructions Using Foundation Models","created":"2024-07-19","cited_by_count":0,"area":"mllm"},{"id":"2411.16407","title":"A Study on Unsupervised Domain Adaptation for Semantic Segmentation in the Era of Vision-Language Models","created":"2024-11-25","cited_by_count":0,"area":"mllm"},{"id":"2507.07147","title":"Weighted Multi-Prompt Learning with Description-free Large Language Model Distillation","created":"2025-07-09","cited_by_count":0,"area":"mllm"},{"id":"2509.00598","title":"DGL-RSIS: Decoupling Global Spatial Context and Local Class Semantics for Training-Free Remote Sensing Image Segmentation","created":"2025-08-30","cited_by_count":0,"area":"mllm"},{"id":"2511.00480","title":"FedMGP: Personalized Federated Learning with Multi-Group Text-Visual Prompts","created":"2025-11-01","cited_by_count":0,"area":"mllm"},{"id":"2601.09661","title":"LiteEmbed: Adapting CLIP to Rare Classes","created":"2026-01-14","cited_by_count":0,"area":"mllm"},{"id":"2604.04473","title":"Beyond Standard Benchmarks: A Systematic Audit of Vision-Language Model's Robustness to Natural Semantic Variation Across Diverse Tasks","created":"2026-04-06","cited_by_count":0,"area":"mllm"},{"id":"2604.04564","title":"Visual Prompt Based Reasoning for Offroad Mapping using Multimodal LLMs","created":"2026-04-06","cited_by_count":0,"area":"mllm"},{"id":"2604.08956","title":"Low-Data Supervised Adaptation Outperforms Prompting for Cloud Segmentation Under Domain Shift","created":"2026-04-10","cited_by_count":0,"area":"mllm"},{"id":"2606.08420","title":"CheXanatomy: Anatomy-Aware Vision-Language Modeling for Chest Radiographs","created":"2026-06-07","cited_by_count":0,"area":"mllm"},{"id":"2606.13883","title":"Guided Diffusion with Distilled Vision-Language Reliability for Aerial Navigation","created":"2026-06-11","cited_by_count":0,"area":"mllm"},{"id":"2607.03595","title":"Token-Based Affordance Grounding with Large Vision-Language Models","created":"2026-07-03","cited_by_count":0,"area":"mllm"},{"id":"2607.06185","title":"Structured-Condensed Prompt Tuning in Vision-Language Models for Fine-grained Image Recognition","created":"2026-07-07","cited_by_count":0,"area":"mllm"},{"id":"2608.17203","title":"Expressivity In Multimodal Contrastive Learning","created":"2026-08-17","cited_by_count":0,"area":"mllm"},{"id":"2608.18339","title":"From Inference to Adaptation: A Unified Optimal Transport View of Vision Language Model","created":"2026-08-18","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":"2609.17868","title":"Lumen: Parameter-Efficient Alignment of Pretrained Vision and Language Encoders for Zero-Shot Computational Pathology","created":"2026-09-15","cited_by_count":0,"area":"mllm"}],"next_cursor":null}