{"total":149,"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":"2402.14008","title":"OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems","created":"2024-02-21","cited_by_count":1136,"area":"mllm"},{"id":"2309.14525","title":"Aligning Large Multimodal Models with Factually Augmented RLHF","created":"2023-09-25","cited_by_count":712,"area":"mllm"},{"id":"2311.16922","title":"Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding","created":"2023-11-28","cited_by_count":688,"area":"mllm"},{"id":"2306.16527","title":"OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents","created":"2023-06-21","cited_by_count":316,"area":"mllm"},{"id":"2309.11499","title":"DreamLLM: Synergistic Multimodal Comprehension and Creation","created":"2023-09-20","cited_by_count":297,"area":"mllm"},{"id":"2111.12233","title":"Scaling Up Vision-Language Pre-training for Image Captioning","created":"2021-11-24","cited_by_count":196,"area":"mllm"},{"id":"2402.14289","title":"TinyLLaVA: A Framework of Small-scale Large Multimodal Models","created":"2024-02-22","cited_by_count":164,"area":"mllm"},{"id":"2404.06512","title":"InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD","created":"2024-04-09","cited_by_count":159,"area":"mllm"},{"id":"2402.08567","title":"Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast","created":"2024-02-13","cited_by_count":153,"area":"mllm"},{"id":"2411.10414","title":"Llama Guard 3 Vision: Safeguarding Human-AI Image Understanding Conversations","created":"2024-11-15","cited_by_count":129,"area":"mllm"},{"id":"2310.14414","title":"Vision Language Models in Autonomous Driving: A Survey and Outlook","created":"2023-10-22","cited_by_count":126,"area":"mllm"},{"id":"2402.05755","title":"Spirit LM: Interleaved Spoken and Written Language Model","created":"2024-02-08","cited_by_count":126,"area":"mllm"},{"id":"2312.16886","title":"MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices","created":"2023-12-28","cited_by_count":122,"area":"mllm"},{"id":"2408.02032","title":"Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Models","created":"2024-08-04","cited_by_count":94,"area":"mllm"},{"id":"2404.13046","title":"MoVA: Adapting Mixture of Vision Experts to Multimodal Context","created":"2024-04-19","cited_by_count":83,"area":"mllm"},{"id":"2406.18915","title":"Manipulate-Anything: Automating Real-World Robots using Vision-Language Models","created":"2024-06-27","cited_by_count":83,"area":"mllm"},{"id":"2408.02900","title":"MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine","created":"2024-08-06","cited_by_count":81,"area":"mllm"},{"id":"2405.16640","title":"A Survey of Multimodal Large Language Model from A Data-centric Perspective","created":"2024-05-26","cited_by_count":75,"area":"mllm"},{"id":"2308.12067","title":"MM-LIMA: Less Is More for Alignment in Multi-Modal Datasets","created":"2023-08-23","cited_by_count":61,"area":"mllm"},{"id":"2506.13759","title":"Discrete Diffusion in Large Language and Multimodal Models: A Survey","created":"2025-06-16","cited_by_count":61,"area":"mllm"},{"id":"2411.12915","title":"VILA-M3: Enhancing Vision-Language Models with Medical Expert Knowledge","created":"2024-11-19","cited_by_count":50,"area":"mllm"},{"id":"2412.09596","title":"InternLM-XComposer2.5-OmniLive: A Comprehensive Multimodal System for Long-term Streaming Video and Audio Interactions","created":"2024-12-12","cited_by_count":48,"area":"mllm"},{"id":"2405.08603","title":"A Comprehensive Survey of Large Language Models and Multimodal Large Language Models in Medicine","created":"2024-05-14","cited_by_count":45,"area":"mllm"},{"id":"2403.10854","title":"A Comprehensive Study of Multimodal Large Language Models for Image Quality Assessment","created":"2024-03-16","cited_by_count":43,"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":"2402.08327","title":"PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers","created":"2024-02-13","cited_by_count":42,"area":"mllm"},{"id":"2410.16236","title":"LLaVA-KD: A Framework of Distilling Multimodal Large Language Models","created":"2024-10-21","cited_by_count":40,"area":"mllm"},{"id":"2410.12332","title":"MC-Bench: A Benchmark for Multi-Context Visual Grounding in the Era of MLLMs","created":"2024-10-16","cited_by_count":39,"area":"mllm"},{"id":"2411.16856","title":"SAR3D: Autoregressive 3D Object Generation and Understanding via Multi-scale 3D VQVAE","created":"2024-11-25","cited_by_count":39,"area":"mllm"},{"id":"2311.16093","title":"Visual cognition in multimodal large language models","created":"2023-11-27","cited_by_count":38,"area":"mllm"},{"id":"2410.10855","title":"Core Knowledge Deficits in Multi-Modal Language Models","created":"2024-10-06","cited_by_count":38,"area":"mllm"},{"id":"2407.06438","title":"SOLO: A Single Transformer for Scalable Vision-Language Modeling","created":"2024-07-08","cited_by_count":37,"area":"mllm"},{"id":"2406.07594","title":"MLLMGuard: A Multi-dimensional Safety Evaluation Suite for Multimodal Large Language Models","created":"2024-06-11","cited_by_count":34,"area":"mllm"},{"id":"2503.12941","title":"HiDe-LLaVA: Hierarchical Decoupling for Continual Instruction Tuning of Multimodal Large Language Model","created":"2025-03-17","cited_by_count":31,"area":"mllm"},{"id":"2501.03225","title":"Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation","created":"2025-01-06","cited_by_count":30,"area":"mllm"},{"id":"2405.11165","title":"Automated Multi-level Preference for MLLMs","created":"2024-05-18","cited_by_count":29,"area":"mllm"},{"id":"2501.00654","title":"ICONS: Influence Consensus for Vision-Language Data Selection","created":"2024-12-31","cited_by_count":29,"area":"mllm"},{"id":"2408.14008","title":"LMM-VQA: Advancing Video Quality Assessment with Large Multimodal Models","created":"2024-08-26","cited_by_count":28,"area":"mllm"},{"id":"2510.17111","title":"Efficient Vision-Language-Action Models for Embodied Manipulation: A Systematic Survey","created":"2025-10-20","cited_by_count":26,"area":"mllm"},{"id":"2411.03312","title":"Inference Optimal VLMs Need Fewer Visual Tokens and More Parameters","created":"2024-11-05","cited_by_count":25,"area":"mllm"},{"id":"2406.03496","title":"Wings: Learning Multimodal LLMs without Text-only Forgetting","created":"2024-06-05","cited_by_count":23,"area":"mllm"},{"id":"2406.05130","title":"An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models","created":"2024-06-07","cited_by_count":22,"area":"mllm"},{"id":"2405.07990","title":"Plot2Code: A Comprehensive Benchmark for Evaluating Multi-modal Large Language Models in Code Generation from Scientific Plots","created":"2024-05-13","cited_by_count":21,"area":"mllm"},{"id":"2405.12107","title":"Imp: Highly Capable Large Multimodal Models for Mobile Devices","created":"2024-05-20","cited_by_count":20,"area":"mllm"},{"id":"2208.13628","title":"Efficient Vision-Language Pretraining with Visual Concepts and Hierarchical Alignment","created":"2022-08-29","cited_by_count":19,"area":"mllm"},{"id":"2502.07617","title":"Scaling Pre-training to One Hundred Billion Data for Vision Language Models","created":"2025-02-11","cited_by_count":19,"area":"mllm"},{"id":"2504.14692","title":"OmniV-Med: Scaling Medical Vision-Language Model for Universal Visual Understanding","created":"2025-04-20","cited_by_count":17,"area":"mllm"},{"id":"2508.09210","title":"MME-Emotion: A Holistic Evaluation Benchmark for Emotional Intelligence in Multimodal Large Language Models","created":"2025-08-11","cited_by_count":17,"area":"mllm"},{"id":"2502.00577","title":"Understanding Multimodal LLMs Under Distribution Shifts: An Information-Theoretic Approach","created":"2025-02-01","cited_by_count":16,"area":"mllm"},{"id":"2407.08583","title":"The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective","created":"2024-07-11","cited_by_count":15,"area":"mllm"},{"id":"2407.14093","title":"Routing Experts: Learning to Route Dynamic Experts in Multi-modal Large Language Models","created":"2024-07-19","cited_by_count":14,"area":"mllm"},{"id":"2504.07165","title":"Perception in Reflection","created":"2025-04-09","cited_by_count":13,"area":"mllm"},{"id":"2505.22946","title":"NegVQA: Can Vision Language Models Understand Negation?","created":"2025-05-28","cited_by_count":13,"area":"mllm"},{"id":"2406.05814","title":"TIGeR: Unifying Text-to-Image Generation and Retrieval with Large Multimodal Models","created":"2024-06-09","cited_by_count":12,"area":"mllm"},{"id":"2508.04650","title":"EncQA: Benchmarking Vision-Language Models on Visual Encodings for Charts","created":"2025-08-06","cited_by_count":12,"area":"mllm"},{"id":"2503.00743","title":"Quality-Driven Curation of Remote Sensing Vision-Language Data via Learned Scoring Models","created":"2025-03-02","cited_by_count":11,"area":"mllm"},{"id":"2504.05445","title":"Probing the Visualization Literacy of Vision Language Models: the Good, the Bad, and the Ugly","created":"2025-04-07","cited_by_count":11,"area":"mllm"},{"id":"2410.00324","title":"Vision Language Models See What You Want but not What You See","created":"2024-10-01","cited_by_count":10,"area":"mllm"},{"id":"2506.15681","title":"GenRecal: Generation after Recalibration from Large to Small Vision-Language Models","created":"2025-06-18","cited_by_count":10,"area":"mllm"},{"id":"2408.00620","title":"Are Bigger Encoders Always Better in Vision Large Models?","created":"2024-08-01","cited_by_count":9,"area":"mllm"},{"id":"2503.08216","title":"Attention Hijackers: Detect and Disentangle Attention Hijacking in LVLMs for Hallucination Mitigation","created":"2025-03-11","cited_by_count":9,"area":"mllm"},{"id":"2510.19307","title":"Unified Reinforcement and Imitation Learning for Vision-Language Models","created":"2025-10-22","cited_by_count":8,"area":"mllm"},{"id":"2408.14744","title":"RSTeller: Scaling Up Visual Language Modeling in Remote Sensing with Rich Linguistic Semantics from Openly Available Data and Large Language Models","created":"2024-08-27","cited_by_count":7,"area":"mllm"},{"id":"2409.07239","title":"PiTe: Pixel-Temporal Alignment for Large Video-Language Model","created":"2024-09-11","cited_by_count":7,"area":"mllm"},{"id":"2410.13088","title":"Self-Comparison for Dataset-Level Membership Inference in Large (Vision-)Language Models","created":"2024-10-16","cited_by_count":7,"area":"mllm"},{"id":"2505.15367","title":"Better Safe Than Sorry? Overreaction Problem of Vision Language Models in Visual Emergency Recognition","created":"2025-05-21","cited_by_count":7,"area":"mllm"},{"id":"2507.23362","title":"Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers","created":"2025-07-31","cited_by_count":7,"area":"mllm"},{"id":"2510.16596","title":"SHIELD: Suppressing Hallucinations In LVLM Encoders via Bias and Vulnerability Defense","created":"2025-10-18","cited_by_count":7,"area":"mllm"},{"id":"2512.00395","title":"Better, Stronger, Faster: Tackling the Trilemma in MLLM-based Segmentation with Simultaneous Textual Mask Prediction","created":"2025-11-29","cited_by_count":7,"area":"mllm"},{"id":"2407.19778","title":"Multimodal Large Language Models for Bioimage Analysis","created":"2024-07-29","cited_by_count":6,"area":"mllm"},{"id":"2409.07480","title":"EEG-Language Pretraining for Highly Label-Efficient Clinical Phenotyping","created":"2024-09-02","cited_by_count":6,"area":"mllm"},{"id":"2410.13694","title":"Exploring the Design Space of Visual Context Representation in Video MLLMs","created":"2024-10-17","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":"2505.17436","title":"Scaling Up Biomedical Vision-Language Models: Fine-Tuning, Instruction Tuning, and Multi-Modal Learning","created":"2025-05-23","cited_by_count":6,"area":"mllm"},{"id":"2512.22238","title":"Masking Teacher and Reinforcing Student for Distilling Vision-Language Models","created":"2025-12-23","cited_by_count":6,"area":"mllm"},{"id":"2312.00414","title":"Vision-Language Models Learn Super Images for Efficient Partially Relevant Video Retrieval","created":"2023-12-01","cited_by_count":5,"area":"mllm"},{"id":"2408.08575","title":"Tell Codec What Worth Compressing: Semantically Disentangled Image Coding for Machine with LMMs","created":"2024-08-16","cited_by_count":5,"area":"mllm"},{"id":"2510.16785","title":"Segmentation as A Plug-and-Play Capability for Frozen Multimodal LLMs","created":"2025-10-19","cited_by_count":5,"area":"mllm"},{"id":"2502.02740","title":"Vision-Language Model Dialog Games for Self-Improvement","created":"2025-02-04","cited_by_count":4,"area":"mllm"},{"id":"2509.21354","title":"KV-Efficient VLA: A Method to Speed up Vision Language Models with RNN-Gated Chunked KV Cache","created":"2025-09-20","cited_by_count":4,"area":"mllm"},{"id":"2510.03721","title":"Person-Centric Annotations of LAION-400M: Auditing Bias and Its Transfer to Models","created":"2025-10-04","cited_by_count":4,"area":"mllm"},{"id":"2510.21879","title":"TernaryCLIP: Efficiently Compressing Vision-Language Models with Ternary Weights and Distilled Knowledge","created":"2025-10-23","cited_by_count":4,"area":"mllm"},{"id":"2603.09573","title":"More than the Sum: Panorama-Language Models for Adverse Omni-Scenes","created":"2026-03-10","cited_by_count":4,"area":"mllm"},{"id":"2605.00814","title":"Persistent Visual Memory: Sustaining Perception for Deep Generation in LVLMs","created":"2026-05-01","cited_by_count":4,"area":"mllm"},{"id":"2410.14154","title":"RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training","created":"2024-10-18","cited_by_count":3,"area":"mllm"},{"id":"2503.06980","title":"Exploring Multimodal Perception in Large Language Models Through Perceptual Strength Ratings","created":"2025-03-10","cited_by_count":3,"area":"mllm"},{"id":"2505.18855","title":"Inference Compute-Optimal Video Vision Language Models","created":"2025-05-24","cited_by_count":3,"area":"mllm"},{"id":"2506.01293","title":"Abstractive Visual Understanding of Multi-modal Structured Knowledge: A New Perspective for MLLM Evaluation","created":"2025-06-02","cited_by_count":3,"area":"mllm"},{"id":"2602.22144","title":"NoLan: Mitigating Object Hallucinations in Large Vision-Language Models via Dynamic Suppression of Language Priors","created":"2026-02-25","cited_by_count":3,"area":"mllm"},{"id":"2604.03191","title":"The Compression Gap: Why Discrete Tokenization Limits Vision-Language-Action Model Scaling","created":"2026-04-03","cited_by_count":3,"area":"mllm"},{"id":"2502.19917","title":"Picking the Cream of the Crop: Visual-Centric Data Selection with Collaborative Agents","created":"2025-02-27","cited_by_count":2,"area":"mllm"},{"id":"2503.21757","title":"Fwd2Bot: LVLM Visual Token Compression with Double Forward Bottleneck","created":"2025-03-27","cited_by_count":2,"area":"mllm"},{"id":"2505.08971","title":"Prioritizing Image-Related Tokens Enhances Vision-Language Pre-Training","created":"2025-05-13","cited_by_count":2,"area":"mllm"},{"id":"2507.07247","title":"Attentions Under the Microscope: A Comparative Study of Resource Utilization for Variants of Self-Attention","created":"2025-07-09","cited_by_count":2,"area":"mllm"},{"id":"2512.19115","title":"Generative Giants, Retrieval Weaklings: Why do Multimodal Large Language Models Fail at Multimodal Retrieval?","created":"2025-12-22","cited_by_count":2,"area":"mllm"},{"id":"2512.19675","title":"Multimodal LLMs for Historical Dataset Construction from Archival Image Scans: German Patents (1877-1918)","created":"2025-12-22","cited_by_count":2,"area":"mllm"},{"id":"2601.01798","title":"VerLM: Explaining Face Verification Using Natural Language","created":"2026-01-05","cited_by_count":2,"area":"mllm"},{"id":"2601.13886","title":"Revisiting Multi-Task Visual Representation Learning","created":"2026-01-20","cited_by_count":2,"area":"mllm"},{"id":"2601.15287","title":"Towards Understanding Best Practices for Quantization of Vision-Language Models","created":"2026-01-21","cited_by_count":2,"area":"mllm"}],"next_cursor":"100"}