{"total":161,"rows":[{"id":"2403.15388","title":"LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models","created":"2024-03-22","cited_by_count":402,"area":"lr"},{"id":"2402.07872","title":"PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs","created":"2024-02-12","cited_by_count":190,"area":"lr"},{"id":"2501.04001","title":"Sa2VA: Marrying SAM2 with MLLM for Dense Grounded Understanding of Images and Videos","created":"2025-01-07","cited_by_count":159,"area":"mllm"},{"id":"2502.13130","title":"Magma: A Foundation Model for Multimodal AI Agents","created":"2025-02-18","cited_by_count":139,"area":"agents"},{"id":"2406.19389","title":"OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and Understanding","created":"2024-06-27","cited_by_count":127,"area":"lr"},{"id":"2403.20271","title":"Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want","created":"2024-03-29","cited_by_count":101,"area":"mllm"},{"id":"2410.10818","title":"TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models","created":"2024-10-14","cited_by_count":97,"area":null},{"id":"2412.02193","title":"LayoutVLM: Differentiable Optimization of 3D Layout via Vision-Language Models","created":"2024-12-03","cited_by_count":96,"area":"lr"},{"id":"2405.17430","title":"Matryoshka Multimodal Models","created":"2024-05-27","cited_by_count":84,"area":"mllm"},{"id":"2501.00599","title":"VideoRefer Suite: Advancing Spatial-Temporal Object Understanding with Video LLM","created":"2024-12-31","cited_by_count":72,"area":null},{"id":"2402.13232","title":"A Touch, Vision, and Language Dataset for Multimodal Alignment","created":"2024-02-20","cited_by_count":69,"area":null},{"id":"2411.10332","title":"Number it: Temporal Grounding Videos like Flipping Manga","created":"2024-11-15","cited_by_count":65,"area":null},{"id":"2403.02325","title":"Contrastive Region Guidance: Improving Grounding in Vision-Language Models without Training","created":"2024-03-04","cited_by_count":64,"area":"mllm"},{"id":"2407.06192","title":"Multi-Object Hallucination in Vision-Language Models","created":"2024-07-08","cited_by_count":60,"area":"mllm"},{"id":"2409.15310","title":"Visual Prompting in Multimodal Large Language Models: A Survey","created":"2024-09-05","cited_by_count":54,"area":"mllm"},{"id":"2510.17269","title":"FineVision: Open Data Is All You Need","created":"2025-10-20","cited_by_count":53,"area":"mllm"},{"id":"2406.20095","title":"LLaRA: Supercharging Robot Learning Data for Vision-Language Policy","created":"2024-06-28","cited_by_count":52,"area":"mllm"},{"id":"2506.05302","title":"Perceive Anything: Recognize, Explain, Caption, and Segment Anything in Images and Videos","created":"2025-06-05","cited_by_count":51,"area":null},{"id":"2501.13787","title":"Parameter-Efficient Fine-Tuning for Foundation Models","created":"2025-01-23","cited_by_count":45,"area":null},{"id":"2405.16919","title":"VoCoT: Unleashing Visually Grounded Multi-Step Reasoning in Large Multi-Modal Models","created":"2024-05-27","cited_by_count":43,"area":"mllm"},{"id":"2402.13254","title":"CounterCurate: Enhancing Physical and Semantic Visio-Linguistic Compositional Reasoning via Counterfactual Examples","created":"2024-02-20","cited_by_count":35,"area":"lr"},{"id":"2407.21534","title":"ControlMLLM: Training-Free Visual Prompt Learning for Multimodal Large Language Models","created":"2024-07-31","cited_by_count":33,"area":"mllm"},{"id":"2403.09333","title":"Griffon v2: Advancing Multimodal Perception with High-Resolution Scaling and Visual-Language Co-Referring","created":"2024-03-14","cited_by_count":29,"area":"mllm"},{"id":"2407.13596","title":"EarthMarker: A Visual Prompting Multi-modal Large Language Model for Remote Sensing","created":"2024-07-18","cited_by_count":29,"area":"mllm"},{"id":"2605.27365","title":"LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding","created":"2026-05-26","cited_by_count":28,"area":"mllm"},{"id":"2405.03194","title":"CityLLaVA: Efficient Fine-Tuning for VLMs in City Scenario","created":"2024-05-06","cited_by_count":27,"area":null},{"id":"2411.15411","title":"FINECAPTION: Compositional Image Captioning Focusing on Wherever You Want at Any Granularity","created":"2024-11-23","cited_by_count":27,"area":"mllm"},{"id":"2402.11248","title":"CoLLaVO: Crayon Large Language and Vision mOdel","created":"2024-02-17","cited_by_count":26,"area":"mllm"},{"id":"2407.15850","title":"AutoAD-Zero: A Training-Free Framework for Zero-Shot Audio Description","created":"2024-07-22","cited_by_count":23,"area":null},{"id":"2409.15477","title":"MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation models","created":"2024-09-23","cited_by_count":23,"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":"2505.00693","title":"Robotic Visual Instruction","created":"2025-05-01","cited_by_count":22,"area":null},{"id":"2410.20327","title":"R-LLaVA: Improving Med-VQA Understanding through Visual Region of Interest","created":"2024-10-27","cited_by_count":21,"area":null},{"id":"2506.05287","title":"EOC-Bench: Can MLLMs Identify, Recall, and Forecast Objects in an Egocentric World?","created":"2025-06-05","cited_by_count":21,"area":"mllm"},{"id":"2510.23603","title":"PixelRefer: A Unified Framework for Spatio-Temporal Object Referring with Arbitrary Granularity","created":"2025-10-27","cited_by_count":21,"area":"mllm"},{"id":"2404.06510","title":"Can Large Vision-Language Models Correct Semantic Grounding Errors By Themselves?","created":"2024-04-09","cited_by_count":20,"area":"mllm"},{"id":"2406.11548","title":"AIC MLLM: Autonomous Interactive Correction MLLM for Robust Robotic Manipulation","created":"2024-06-17","cited_by_count":20,"area":"lr"},{"id":"2408.15205","title":"Leveraging Hallucinations to Reduce Manual Prompt Dependency in Promptable Segmentation","created":"2024-08-27","cited_by_count":20,"area":null},{"id":"2501.08326","title":"Omni-RGPT: Unifying Image and Video Region-level Understanding via Token Marks","created":"2025-01-14","cited_by_count":17,"area":"mllm"},{"id":"2408.09530","title":"PA-LLaVA: A Large Language-Vision Assistant for Human Pathology Image Understanding","created":"2024-08-18","cited_by_count":16,"area":null},{"id":"2501.04670","title":"Are They the Same? Exploring Visual Correspondence Shortcomings of Multimodal LLMs","created":"2025-01-08","cited_by_count":16,"area":"lr"},{"id":"2503.15426","title":"Visual Position Prompt for MLLM based Visual Grounding","created":"2025-03-19","cited_by_count":16,"area":"mllm"},{"id":"2411.16799","title":"One is Plenty: A Polymorphic Feature Interpreter for Immutable Heterogeneous Collaborative Perception","created":"2024-11-25","cited_by_count":15,"area":null},{"id":"2510.10011","title":"MIMO: A medical vision language model with visual referring multimodal input and pixel grounding multimodal output","created":"2025-10-11","cited_by_count":15,"area":"mllm"},{"id":"2509.25866","title":"DeepSketcher: Internalizing Visual Manipulation for Multimodal Reasoning","created":"2025-09-30","cited_by_count":14,"area":"lr"},{"id":"2407.04681","title":"Rethinking Visual Prompting for Multimodal Large Language Models with External Knowledge","created":"2024-07-05","cited_by_count":13,"area":"lr"},{"id":"2412.03409","title":"PrefixKV: Adaptive Prefix KV Cache is What Vision Instruction-Following Models Need for Efficient Generation","created":"2024-12-04","cited_by_count":13,"area":"lr"},{"id":"2507.19599","title":"Object-centric Video Question Answering with Visual Grounding and Referring","created":"2025-07-25","cited_by_count":13,"area":null},{"id":"2508.00549","title":"Your other Left! Vision-Language Models Fail to Identify Relative Positions in Medical Images","created":"2025-08-01","cited_by_count":13,"area":"mllm"},{"id":"2502.14113","title":"Object-centric Binding in Contrastive Language-Image Pretraining","created":"2025-02-19","cited_by_count":12,"area":"mllm"},{"id":"2503.19851","title":"Towards Online Multi-Modal Social Interaction Understanding","created":"2025-03-25","cited_by_count":12,"area":null},{"id":"2506.01955","title":"Dual-Process Image Generation","created":"2025-06-02","cited_by_count":12,"area":null},{"id":"2402.13636","title":"A Unified Framework and Dataset for Assessing Societal Bias in Vision-Language Models","created":"2024-02-21","cited_by_count":11,"area":"mllm"},{"id":"2503.17736","title":"V2P-Bench: Evaluating Video-Language Understanding with Visual Prompts for Better Human-Model Interaction","created":"2025-03-22","cited_by_count":11,"area":"mllm"},{"id":"2504.12795","title":"EarthGPT-X: A Spatial MLLM for Multi-level Multi-Source Remote Sensing Imagery Understanding with Visual Prompting","created":"2025-04-17","cited_by_count":11,"area":"lr"},{"id":"2601.16093","title":"SAMTok: Representing Any Mask with Two Words","created":"2026-01-22","cited_by_count":11,"area":null},{"id":"2405.19567","title":"Dr-LLaVA: Visual Instruction Tuning with Symbolic Clinical Grounding","created":"2024-05-29","cited_by_count":10,"area":"mllm"},{"id":"2504.02862","title":"Towards Understanding How Knowledge Evolves in Large Vision-Language Models","created":"2025-03-31","cited_by_count":10,"area":"mllm"},{"id":"2504.21336","title":"UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation","created":"2025-04-30","cited_by_count":10,"area":null},{"id":"2509.10345","title":"Towards Understanding Visual Grounding in Visual Language Models","created":"2025-09-12","cited_by_count":10,"area":"mllm"},{"id":"2403.12848","title":"Planner3D: LLM-enhanced graph prior meets 3D indoor scene explicit regularization","created":"2024-03-19","cited_by_count":9,"area":null},{"id":"2502.05165","title":"Multitwine: Multi-Object Compositing with Text and Layout Control","created":"2025-02-07","cited_by_count":9,"area":null},{"id":"2506.14471","title":"Dense360: Dense Understanding from Omnidirectional Panoramas","created":"2025-06-17","cited_by_count":9,"area":"mllm"},{"id":"2509.08820","title":"RoboChemist: Long-Horizon and Safety-Compliant Robotic Chemical Experimentation","created":"2025-09-10","cited_by_count":9,"area":"vla"},{"id":"2411.15446","title":"freePruner: A Training-free Approach for Large Multimodal Model Acceleration","created":"2024-11-23","cited_by_count":8,"area":"mllm"},{"id":"2412.03565","title":"INST-IT: Boosting Instance Understanding via Explicit Visual Prompt Instruction Tuning","created":"2024-12-04","cited_by_count":8,"area":"mllm"},{"id":"2502.02452","title":"Personalization Toolkit: Training Free Personalization of Large Vision Language Models","created":"2025-02-04","cited_by_count":8,"area":"mllm"},{"id":"2505.23740","title":"LayerPeeler: Autoregressive Peeling for Layer-wise Image Vectorization","created":"2025-05-29","cited_by_count":8,"area":null},{"id":"2506.13901","title":"Alignment Quality Index (AQI) : Beyond Refusals: AQI as an Intrinsic Alignment Diagnostic via Latent Geometry, Cluster Divergence, and Layer wise Pooled Representations","created":"2025-06-16","cited_by_count":8,"area":"alignment"},{"id":"2509.25757","title":"NePTune: A Neuro-Pythonic Framework for Tunable Compositional Reasoning on Vision-Language","created":"2025-09-30","cited_by_count":8,"area":"lr"},{"id":"2501.02385","title":"Guiding Medical Vision-Language Models with Explicit Visual Prompts: Framework Design and Comprehensive Exploration of Prompt Variations","created":"2025-01-04","cited_by_count":7,"area":"mllm"},{"id":"2505.23242","title":"ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering","created":"2025-05-29","cited_by_count":7,"area":"lr"},{"id":"2506.08708","title":"PhyBlock: A Progressive Benchmark for Physical Understanding and Planning via 3D Block Assembly","created":"2025-06-10","cited_by_count":7,"area":"mllm"},{"id":"2508.00395","title":"Decouple before Align: Visual Disentanglement Enhances Prompt Tuning","created":"2025-08-01","cited_by_count":7,"area":"mllm"},{"id":"2508.06763","title":"SafePLUG: Empowering Multimodal LLMs with Pixel-Level Insight and Temporal Grounding for Traffic Accident Understanding","created":"2025-08-09","cited_by_count":6,"area":"mllm"},{"id":"2509.23724","title":"Video Panels for Long Video Understanding","created":"2025-09-28","cited_by_count":6,"area":null},{"id":"2510.15749","title":"SEGA: A Stepwise Evolution Paradigm for Content-Aware Layout Generation with Design Prior","created":"2025-10-17","cited_by_count":6,"area":null},{"id":"2604.11789","title":"LMMs Meet Object-Centric Vision: Understanding, Segmentation, Editing and Generation","created":"2026-04-13","cited_by_count":6,"area":"lr"},{"id":"2407.09686","title":"SPIN: Hierarchical Segmentation with Subpart Granularity in Natural Images","created":"2024-07-12","cited_by_count":5,"area":null},{"id":"2503.10546","title":"KUDA: Keypoints to Unify Dynamics Learning and Visual Prompting for Open-Vocabulary Robotic Manipulation","created":"2025-03-13","cited_by_count":5,"area":"mllm"},{"id":"2505.21061","title":"LPOI: Listwise Preference Optimization for Vision Language Models","created":"2025-05-27","cited_by_count":5,"area":"mllm"},{"id":"2507.06959","title":"CheXPO: Preference Optimization for Chest X-ray VLMs with Counterfactual Rationale","created":"2025-07-09","cited_by_count":5,"area":"mllm"},{"id":"2507.12441","title":"Describe Anything Model for Visual Question Answering on Text-rich Images","created":"2025-07-16","cited_by_count":5,"area":"mllm"},{"id":"2601.02783","title":"EarthVL: A Progressive Earth Vision-Language Understanding and Generation Framework","created":"2026-01-06","cited_by_count":5,"area":null},{"id":"2503.10857","title":"Towards Understanding Graphical Perception in Large Multimodal Models","created":"2025-03-13","cited_by_count":4,"area":"mllm"},{"id":"2503.19647","title":"Show or Tell? Effectively prompting Vision-Language Models for semantic segmentation","created":"2025-03-25","cited_by_count":4,"area":"mllm"},{"id":"2505.02867","title":"RESAnything: Attribute Prompting for Arbitrary Referring Segmentation","created":"2025-05-03","cited_by_count":4,"area":null},{"id":"2505.19614","title":"Multiplicity is an Inevitable and Inherent Challenge in Multimodal Learning","created":"2025-05-26","cited_by_count":4,"area":null},{"id":"2508.01766","title":"VPN: Visual Prompt Navigation","created":"2025-08-03","cited_by_count":4,"area":null},{"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":"2510.27680","title":"PETAR: Localized Findings Generation with Mask-Aware Vision-Language Modeling for PET Automated Reporting","created":"2025-10-31","cited_by_count":4,"area":null},{"id":"2602.08241","title":"Do MLLMs Really See It: Reinforcing Visual Attention in Multimodal LLMs","created":"2026-02-09","cited_by_count":4,"area":"lr"},{"id":"2603.14659","title":"VisionCoach: Reinforcing Grounded Video Reasoning via Visual-Perception Prompting","created":"2026-03-15","cited_by_count":4,"area":"distill"},{"id":"2406.04716","title":"MGIMM: Multi-Granularity Instruction Multimodal Model for Attribute-Guided Remote Sensing Image Detailed Description","created":"2024-06-07","cited_by_count":3,"area":"mllm"},{"id":"2412.16869","title":"CoF: Coarse to Fine-Grained Image Understanding for Multi-modal Large Language Models","created":"2024-12-22","cited_by_count":3,"area":"mllm"},{"id":"2510.26915","title":"Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence","created":"2025-10-30","cited_by_count":3,"area":"mllm"},{"id":"2511.02384","title":"RxnCaption: Reformulating Reaction Diagram Parsing as Visual Prompt Guided Captioning","created":"2025-11-04","cited_by_count":3,"area":null},{"id":"2601.08151","title":"Where Does Vision Meet Language? Understanding and Refining Visual Fusion in MLLMs via Contrastive Attention","created":"2026-01-13","cited_by_count":3,"area":"mllm"},{"id":"2603.23186","title":"ViKey: Enhancing Temporal Understanding in Videos via Visual Prompting","created":"2026-03-24","cited_by_count":3,"area":null},{"id":"2607.15193","title":"Plover: Steering GUI Agents through Plan-Centric Interaction","created":"2026-07-16","cited_by_count":3,"area":"agents"}],"next_cursor":"100"}