{"total":130,"rows":[{"id":"2110.04544","title":"CLIP-Adapter: Better Vision-Language Models with Feature Adapters","created":"2021-10-09","cited_by_count":1026,"area":"mllm"},{"id":"2306.13549","title":"A Survey on Multimodal Large Language Models","created":"2023-06-23","cited_by_count":909,"area":"mllm"},{"id":"2108.10904","title":"SimVLM: Simple Visual Language Model Pretraining with Weak Supervision","created":"2021-08-24","cited_by_count":647,"area":"mllm"},{"id":"2309.05519","title":"NExT-GPT: Any-to-Any Multimodal LLM","created":"2023-09-11","cited_by_count":615,"area":"mllm"},{"id":"2111.03930","title":"Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling","created":"2021-11-06","cited_by_count":431,"area":"mllm"},{"id":"2207.09519","title":"Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classification","created":"2022-07-19","cited_by_count":362,"area":"mllm"},{"id":"2111.08276","title":"Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts","created":"2021-11-16","cited_by_count":256,"area":"mllm"},{"id":"2104.03135","title":"Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning","created":"2021-04-07","cited_by_count":187,"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":"2202.09061","title":"VLP: A Survey on Vision-Language Pre-training","created":"2022-02-18","cited_by_count":159,"area":"mllm"},{"id":"2211.10277","title":"Task Residual for Tuning Vision-Language Models","created":"2022-11-18","cited_by_count":123,"area":"mllm"},{"id":"2111.12085","title":"UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language Modeling","created":"2021-11-23","cited_by_count":122,"area":"mllm"},{"id":"2210.09263","title":"Vision-Language Pre-training: Basics, Recent Advances, and Future Trends","created":"2022-10-17","cited_by_count":117,"area":"mllm"},{"id":"2103.03493","title":"Causal Attention for Vision-Language Tasks","created":"2021-03-05","cited_by_count":111,"area":"mllm"},{"id":"2101.00529","title":"VinVL: Revisiting Visual Representations in Vision-Language Models","created":"2021-01-02","cited_by_count":110,"area":"mllm"},{"id":"2305.18279","title":"Contextual Object Detection with Multimodal Large Language Models","created":"2023-05-29","cited_by_count":100,"area":"mllm"},{"id":"2105.11333","title":"Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Training","created":"2021-05-24","cited_by_count":96,"area":"mllm"},{"id":"2312.14074","title":"LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding","created":"2023-12-21","cited_by_count":95,"area":"mllm"},{"id":"2106.01804","title":"E2E-VLP: End-to-End Vision-Language Pre-training Enhanced by Visual Learning","created":"2021-06-03","cited_by_count":85,"area":"mllm"},{"id":"2411.17558","title":"Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey","created":"2024-11-26","cited_by_count":76,"area":"mllm"},{"id":"2210.07688","title":"Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-training","created":"2022-10-14","cited_by_count":72,"area":"mllm"},{"id":"2203.06386","title":"Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation","created":"2022-03-12","cited_by_count":70,"area":"mllm"},{"id":"2401.11170","title":"Inducing High Energy-Latency of Large Vision-Language Models with Verbose Images","created":"2024-01-20","cited_by_count":69,"area":"mllm"},{"id":"2208.02515","title":"Fine-Grained Semantically Aligned Vision-Language Pre-Training","created":"2022-08-04","cited_by_count":68,"area":"mllm"},{"id":"2103.16110","title":"Kaleido-BERT: Vision-Language Pre-training on Fashion Domain","created":"2021-03-30","cited_by_count":67,"area":"mllm"},{"id":"2106.13488","title":"Probing Inter-modality: Visual Parsing with Self-Attention for Vision-Language Pre-training","created":"2021-06-25","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":"2210.07795","title":"EfficientVLM: Fast and Accurate Vision-Language Models via Knowledge Distillation and Modal-adaptive Pruning","created":"2022-10-14","cited_by_count":56,"area":"mllm"},{"id":"2406.05127","title":"Towards Semantic Equivalence of Tokenization in Multimodal LLM","created":"2024-06-07","cited_by_count":45,"area":"mllm"},{"id":"2303.02995","title":"HiCLIP: Contrastive Language-Image Pretraining with Hierarchy-aware Attention","created":"2023-03-06","cited_by_count":44,"area":"mllm"},{"id":"2201.12944","title":"Deep Learning Approaches on Image Captioning: A Review","created":"2022-01-31","cited_by_count":43,"area":"mllm"},{"id":"2302.04858","title":"Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning","created":"2023-02-09","cited_by_count":41,"area":"mllm"},{"id":"2406.05491","title":"One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models","created":"2024-06-08","cited_by_count":40,"area":"mllm"},{"id":"2406.01920","title":"CODE: Contrasting Self-generated Description to Combat Hallucination in Large Multi-modal Models","created":"2024-06-04","cited_by_count":38,"area":"mllm"},{"id":"2506.14766","title":"ASCD: Attention-Steerable Contrastive Decoding for Reducing Hallucination in MLLM","created":"2025-06-17","cited_by_count":37,"area":"mllm"},{"id":"2204.07441","title":"COTS: Collaborative Two-Stream Vision-Language Pre-Training Model for Cross-Modal Retrieval","created":"2022-04-15","cited_by_count":33,"area":"mllm"},{"id":"2305.17718","title":"FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions","created":"2023-05-28","cited_by_count":33,"area":"mllm"},{"id":"2205.11169","title":"PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models","created":"2022-05-23","cited_by_count":30,"area":"mllm"},{"id":"2212.09737","title":"Position-guided Text Prompt for Vision-Language Pre-training","created":"2022-12-19","cited_by_count":27,"area":"mllm"},{"id":"2305.20087","title":"Too Large; Data Reduction for Vision-Language Pre-Training","created":"2023-05-31","cited_by_count":26,"area":"mllm"},{"id":"2206.15462","title":"Improving Visual Grounding by Encouraging Consistent Gradient-based Explanations","created":"2022-06-30","cited_by_count":24,"area":"mllm"},{"id":"2204.07955","title":"Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis","created":"2022-04-17","cited_by_count":21,"area":"mllm"},{"id":"2301.12429","title":"Debiased Fine-Tuning for Vision-language Models by Prompt Regularization","created":"2023-01-29","cited_by_count":21,"area":"mllm"},{"id":"2203.09067","title":"UNIMO-2: End-to-End Unified Vision-Language Grounded Learning","created":"2022-03-17","cited_by_count":19,"area":"mllm"},{"id":"2503.14604","title":"Image Captioning Evaluation in the Age of Multimodal LLMs: Challenges and Future Perspectives","created":"2025-03-18","cited_by_count":18,"area":"mllm"},{"id":"2101.11562","title":"Scheduled Sampling in Vision-Language Pretraining with Decoupled Encoder-Decoder Network","created":"2021-01-27","cited_by_count":17,"area":"mllm"},{"id":"2510.22391","title":"Top-Down Semantic Refinement for Image Captioning","created":"2025-10-25","cited_by_count":16,"area":"mllm"},{"id":"2301.07584","title":"Joint Representation Learning for Text and 3D Point Cloud","created":"2023-01-18","cited_by_count":14,"area":"mllm"},{"id":"2311.11241","title":"Open-Vocabulary Camouflaged Object Segmentation","created":"2023-11-19","cited_by_count":14,"area":"mllm"},{"id":"2404.16557","title":"Energy-Latency Manipulation of Multi-modal Large Language Models via Verbose Samples","created":"2024-04-25","cited_by_count":14,"area":"mllm"},{"id":"2201.12596","title":"MVPTR: Multi-Level Semantic Alignment for Vision-Language Pre-Training via Multi-Stage Learning","created":"2022-01-29","cited_by_count":13,"area":"mllm"},{"id":"2311.17091","title":"Beyond Sole Strength: Customized Ensembles for Generalized Vision-Language Models","created":"2023-11-28","cited_by_count":13,"area":"mllm"},{"id":"2403.14760","title":"Can 3D Vision-Language Models Truly Understand Natural Language?","created":"2024-03-21","cited_by_count":12,"area":"mllm"},{"id":"2205.15237","title":"VLUE: A Multi-Task Benchmark for Evaluating Vision-Language Models","created":"2022-05-30","cited_by_count":11,"area":"mllm"},{"id":"2301.01893","title":"GIVL: Improving Geographical Inclusivity of Vision-Language Models with Pre-Training Methods","created":"2023-01-05","cited_by_count":11,"area":"mllm"},{"id":"2304.13273","title":"From Association to Generation: Text-only Captioning by Unsupervised Cross-modal Mapping","created":"2023-04-26","cited_by_count":11,"area":"mllm"},{"id":"2412.04317","title":"FlashSloth: Lightning Multimodal Large Language Models via Embedded Visual Compression","created":"2024-12-05","cited_by_count":11,"area":"mllm"},{"id":"2507.13773","title":"Teaching Vision-Language Models to Ask: Resolving Ambiguity in Visual Questions","created":"2025-07-18","cited_by_count":11,"area":"mllm"},{"id":"2301.07236","title":"Effective End-to-End Vision Language Pretraining with Semantic Visual Loss","created":"2023-01-18","cited_by_count":10,"area":"mllm"},{"id":"2308.01126","title":"Beyond Generic: Enhancing Image Captioning with Real-World Knowledge using Vision-Language Pre-Training Model","created":"2023-08-02","cited_by_count":10,"area":"mllm"},{"id":"2505.05804","title":"Describe Anything in Medical Images","created":"2025-05-09","cited_by_count":10,"area":"mllm"},{"id":"2211.12402","title":"X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks","created":"2022-11-22","cited_by_count":9,"area":"mllm"},{"id":"2109.07301","title":"What Vision-Language Models `See' when they See Scenes","created":"2021-09-15","cited_by_count":8,"area":"mllm"},{"id":"2207.11401","title":"Chunk-aware Alignment and Lexical Constraint for Visual Entailment with Natural Language Explanations","created":"2022-07-23","cited_by_count":8,"area":"mllm"},{"id":"2209.15270","title":"ERNIE-ViL 2.0: Multi-view Contrastive Learning for Image-Text Pre-training","created":"2022-09-30","cited_by_count":8,"area":"mllm"},{"id":"2305.14676","title":"GRILL: Grounded Vision-language Pre-training via Aligning Text and Image Regions","created":"2023-05-24","cited_by_count":8,"area":"mllm"},{"id":"2201.04026","title":"Uni-EDEN: Universal Encoder-Decoder Network by Multi-Granular Vision-Language Pre-training","created":"2022-01-11","cited_by_count":7,"area":"mllm"},{"id":"2209.06954","title":"Correlation Information Bottleneck: Towards Adapting Pretrained Multimodal Models for Robust Visual Question Answering","created":"2022-09-14","cited_by_count":7,"area":"mllm"},{"id":"2211.04861","title":"ERNIE-UniX2: A Unified Cross-lingual Cross-modal Framework for Understanding and Generation","created":"2022-11-09","cited_by_count":7,"area":"mllm"},{"id":"2211.16504","title":"Improving Commonsense in Vision-Language Models via Knowledge Graph Riddles","created":"2022-11-29","cited_by_count":7,"area":"mllm"},{"id":"2304.10727","title":"RoCOCO: Robustness Benchmark of MS-COCO to Stress-test Image-Text Matching Models","created":"2023-04-21","cited_by_count":7,"area":"mllm"},{"id":"2306.00931","title":"\"Let's not Quote out of Context\": Unified Vision-Language Pretraining for Context Assisted Image Captioning","created":"2023-06-01","cited_by_count":7,"area":"mllm"},{"id":"2311.08844","title":"Violet: A Vision-Language Model for Arabic Image Captioning with Gemini Decoder","created":"2023-11-15","cited_by_count":7,"area":"mllm"},{"id":"2501.15074","title":"PatentLMM: Large Multimodal Model for Generating Descriptions for Patent Figures","created":"2025-01-25","cited_by_count":7,"area":"mllm"},{"id":"2504.13199","title":"Building Trustworthy Multimodal AI: A Review of Fairness, Transparency, and Ethics in Vision-Language Tasks","created":"2025-04-14","cited_by_count":7,"area":"mllm"},{"id":"2108.09479","title":"Grid-VLP: Revisiting Grid Features for Vision-Language Pre-training","created":"2021-08-21","cited_by_count":6,"area":"mllm"},{"id":"2308.16689","title":"ViLTA: Enhancing Vision-Language Pre-training through Textual Augmentation","created":"2023-08-31","cited_by_count":6,"area":"mllm"},{"id":"2303.01239","title":"MixPHM: Redundancy-Aware Parameter-Efficient Tuning for Low-Resource Visual Question Answering","created":"2023-03-02","cited_by_count":5,"area":"mllm"},{"id":"2306.06494","title":"Multi-modal Pre-training for Medical Vision-language Understanding and Generation: An Empirical Study with A New Benchmark","created":"2023-06-10","cited_by_count":5,"area":"mllm"},{"id":"2404.13947","title":"Self-Bootstrapped Visual-Language Model for Knowledge Selection and Question Answering","created":"2024-04-22","cited_by_count":5,"area":"mllm"},{"id":"2506.15477","title":"Multimodal Large Language Models for Medical Report Generation via Customized Prompt Tuning","created":"2025-06-18","cited_by_count":5,"area":"mllm"},{"id":"2509.25654","title":"DescribeEarth: Describe Anything for Remote Sensing Images","created":"2025-09-30","cited_by_count":5,"area":"mllm"},{"id":"2205.12105","title":"HiVLP: Hierarchical Vision-Language Pre-Training for Fast Image-Text Retrieval","created":"2022-05-24","cited_by_count":4,"area":"mllm"},{"id":"2305.10046","title":"Probing the Role of Positional Information in Vision-Language Models","created":"2023-05-17","cited_by_count":4,"area":"mllm"},{"id":"2503.13938","title":"ChatBEV: Empowering Traffic Scene Understanding and Simulation via Vision-Language Model","created":"2025-03-18","cited_by_count":4,"area":"mllm"},{"id":"2404.08226","title":"Improving Continuous Sign Language Recognition with Adapted Image Models","created":"2024-04-12","cited_by_count":3,"area":"mllm"},{"id":"2501.07109","title":"The Quest for Visual Understanding: A Journey Through the Evolution of Visual Question Answering","created":"2025-01-13","cited_by_count":3,"area":"mllm"},{"id":"2503.09445","title":"Astrea: A MOE-based Visual Understanding Model with Progressive Alignment","created":"2025-03-12","cited_by_count":3,"area":"mllm"},{"id":"2505.20503","title":"Embodied AI with Foundation Models for Mobile Service Robots: A Systematic Review","created":"2025-05-26","cited_by_count":3,"area":"mllm"},{"id":"2507.08679","title":"ByDeWay: Boost Your multimodal LLM with DEpth prompting in a Training-Free Way","created":"2025-07-11","cited_by_count":3,"area":"mllm"},{"id":"2211.00262","title":"Training Vision-Language Models with Less Bimodal Supervision","created":"2022-11-01","cited_by_count":2,"area":"mllm"},{"id":"2305.14882","title":"Dynamic Clue Bottlenecks: Towards Interpretable-by-Design Visual Question Answering","created":"2023-05-24","cited_by_count":2,"area":"mllm"},{"id":"2306.17675","title":"Multimodal Prompt Retrieval for Generative Visual Question Answering","created":"2023-06-30","cited_by_count":2,"area":"mllm"},{"id":"2407.03314","title":"BACON: Improving Clarity of Image Captions via Bag-of-Concept Graphs","created":"2024-07-03","cited_by_count":2,"area":"mllm"},{"id":"2506.16691","title":"LaVi: Efficient Large Vision-Language Models via Internal Feature Modulation","created":"2025-06-20","cited_by_count":2,"area":"mllm"},{"id":"2506.17645","title":"Histopathology Image Report Generation by Vision Language Model with Multimodal In-Context Learning","created":"2025-06-21","cited_by_count":2,"area":"mllm"},{"id":"2508.08066","title":"ExpVG: Investigating the Design Space of Visual Grounding in Multimodal Large Language Model","created":"2025-08-11","cited_by_count":2,"area":"mllm"},{"id":"2206.03354","title":"cViL: Cross-Lingual Training of Vision-Language Models using Knowledge Distillation","created":"2022-06-07","cited_by_count":1,"area":"mllm"},{"id":"2310.17737","title":"ArchBERT: Bi-Modal Understanding of Neural Architectures and Natural Languages","created":"2023-10-26","cited_by_count":1,"area":"mllm"},{"id":"2404.17534","title":"Exploring the Distinctiveness and Fidelity of the Descriptions Generated by Large Vision-Language Models","created":"2024-04-26","cited_by_count":1,"area":"mllm"}],"next_cursor":"100"}