{"total":57,"rows":[{"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":"2412.04292","title":"SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Model","created":"2024-12-05","cited_by_count":108,"area":"mllm"},{"id":"2504.16072","title":"Describe Anything: Detailed Localized Image and Video Captioning","created":"2025-04-22","cited_by_count":84,"area":"mllm"},{"id":"2501.01163","title":"3D-LLaVA: Towards Generalist 3D LMMs with Omni Superpoint Transformer","created":"2025-01-02","cited_by_count":68,"area":"mllm"},{"id":"2410.09855","title":"Text4Seg: Reimagining Image Segmentation as Text Generation","created":"2024-10-13","cited_by_count":61,"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":"2410.16236","title":"LLaVA-KD: A Framework of Distilling Multimodal Large Language Models","created":"2024-10-21","cited_by_count":40,"area":"mllm"},{"id":"2501.02765","title":"Visual Large Language Models for Generalized and Specialized Applications","created":"2025-01-06","cited_by_count":36,"area":"mllm"},{"id":"2412.14006","title":"InstructSeg: Unifying Instructed Visual Segmentation with Multi-modal Large Language Models","created":"2024-12-18","cited_by_count":31,"area":"mllm"},{"id":"2412.05679","title":"RSUniVLM: A Unified Vision Language Model for Remote Sensing via Granularity-oriented Mixture of Experts","created":"2024-12-07","cited_by_count":29,"area":"mllm"},{"id":"2412.00832","title":"EventGPT: Event Stream Understanding with Multimodal Large Language Models","created":"2024-12-01","cited_by_count":28,"area":"mllm"},{"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":"2504.10465","title":"Pixel-SAIL: Single Transformer For Pixel-Grounded Understanding","created":"2025-04-14","cited_by_count":22,"area":"mllm"},{"id":"2503.13026","title":"HiMTok: Learning Hierarchical Mask Tokens for Image Segmentation with Large Multimodal Model","created":"2025-03-17","cited_by_count":20,"area":"mllm"},{"id":"2510.01954","title":"Patch-as-Decodable-Token: Towards Unified Multi-Modal Vision Tasks in MLLMs","created":"2025-10-02","cited_by_count":20,"area":"mllm"},{"id":"2510.18876","title":"Grasp Any Region: Towards Precise, Contextual Pixel Understanding for Multimodal LLMs","created":"2025-10-21","cited_by_count":18,"area":"mllm"},{"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":"2506.24102","title":"DenseWorld-1M: Towards Detailed Dense Grounded Caption in the Real World","created":"2025-06-30","cited_by_count":16,"area":"mllm"},{"id":"2505.05804","title":"Describe Anything in Medical Images","created":"2025-05-09","cited_by_count":10,"area":"mllm"},{"id":"2506.14471","title":"Dense360: Dense Understanding from Omnidirectional Panoramas","created":"2025-06-17","cited_by_count":9,"area":"mllm"},{"id":"2509.06321","title":"Text4Seg++: Advancing Image Segmentation via Generative Language Modeling","created":"2025-09-08","cited_by_count":9,"area":"mllm"},{"id":"2509.15178","title":"Unleashing the Potential of Multimodal LLMs for Zero-Shot Spatio-Temporal Video Grounding","created":"2025-09-18","cited_by_count":9,"area":"mllm"},{"id":"2409.14993","title":"Multi-modal Generative AI: Multi-modal LLMs, Diffusions, and the Unification","created":"2024-09-23","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":"2505.18812","title":"SAMA: Towards Multi-Turn Referential Grounded Video Chat with Large Language Models","created":"2025-05-24","cited_by_count":8,"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":"2507.06272","title":"LIRA: Inferring Segmentation in Large Multi-modal Models with Local Interleaved Region Assistance","created":"2025-07-08","cited_by_count":5,"area":"mllm"},{"id":"2509.14977","title":"EchoVLM: Dynamic Mixture-of-Experts Vision-Language Model for Universal Ultrasound Intelligence","created":"2025-09-18","cited_by_count":5,"area":"mllm"},{"id":"2601.19228","title":"Towards Pixel-Level VLM Perception via Simple Points Prediction","created":"2026-01-27","cited_by_count":5,"area":"mllm"},{"id":"2502.04192","title":"PixFoundation: Are We Heading in the Right Direction with Pixel-level Vision Foundation Models?","created":"2025-02-06","cited_by_count":4,"area":"mllm"},{"id":"2503.10779","title":"The Power of One: A Single Example is All it Takes for Segmentation in VLMs","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":"2504.14467","title":"LGD: Leveraging Generative Descriptions for Zero-Shot Referring Image Segmentation","created":"2025-04-20","cited_by_count":4,"area":"mllm"},{"id":"2507.10202","title":"A Training-Free, Task-Agnostic Framework for Enhancing MLLM Performance on High-Resolution Images","created":"2025-07-14","cited_by_count":4,"area":"mllm"},{"id":"2410.16163","title":"Griffon-G: Bridging Vision-Language and Vision-Centric Tasks via Large Multimodal Models","created":"2024-10-21","cited_by_count":3,"area":"mllm"},{"id":"2508.05202","title":"SPEX: A Vision-Language Model for Land Cover Extraction on Spectral Remote Sensing Images","created":"2025-08-07","cited_by_count":3,"area":"mllm"},{"id":"2511.21339","title":"SurgMLLMBench: A Multimodal Large Language Model Benchmark Dataset for Surgical Scene Understanding","created":"2025-11-26","cited_by_count":3,"area":"mllm"},{"id":"2603.18795","title":"Perceptio: Perception Enhanced Vision Language Models via Spatial Token Generation","created":"2026-03-19","cited_by_count":3,"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":"2603.10703","title":"WalkGPT: Grounded Vision-Language Conversation with Depth-Aware Segmentation for Pedestrian Navigation","created":"2026-03-11","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":"2412.20070","title":"Exploring Compositional Generalization of Multimodal LLMs for Medical Imaging","created":"2024-12-28","cited_by_count":1,"area":"mllm"},{"id":"2505.20122","title":"MEBench: A Novel Benchmark for Understanding Mutual Exclusivity Bias in Vision-Language Models","created":"2025-05-26","cited_by_count":1,"area":"mllm"},{"id":"2509.13676","title":"Re-purposing SAM into Efficient Visual Projectors for MLLM-Based Referring Image Segmentation","created":"2025-09-17","cited_by_count":1,"area":"mllm"},{"id":"2510.25263","title":"LangHOPS: Language Grounded Hierarchical Open-Vocabulary Part Segmentation","created":"2025-10-29","cited_by_count":1,"area":"mllm"},{"id":"2603.01305","title":"AG-VAS: Anchor-Guided Zero-Shot Visual Anomaly Segmentation with Large Multimodal Models","created":"2026-03-01","cited_by_count":1,"area":"mllm"},{"id":"2603.17228","title":"From Drop-off to Recovery: A Mechanistic Analysis of Segmentation in MLLMs","created":"2026-03-18","cited_by_count":1,"area":"mllm"},{"id":"2605.00891","title":"X2SAM: Any Segmentation in Images and Videos","created":"2026-04-27","cited_by_count":1,"area":"mllm"},{"id":"2606.19534","title":"PerceptionDLM: Parallel Region Perception with Multimodal Diffusion Language Models","created":"2026-06-17","cited_by_count":1,"area":"mllm"},{"id":"2607.11581","title":"Actor as Its Own Critic: Unifying Region Understanding and Localization via CycleGRPO","created":"2026-07-13","cited_by_count":1,"area":"mllm"},{"id":"2503.06472","title":"CalliReader: Contextualizing Chinese Calligraphy via an Embedding-Aligned Vision-Language Model","created":"2025-03-09","cited_by_count":0,"area":"mllm"},{"id":"2510.14583","title":"Talking Points: Describing and Localizing Pixels","created":"2025-10-16","cited_by_count":0,"area":"mllm"},{"id":"2605.01827","title":"Referring Multiple Regions with Large Multimodal Models via Contextual Latent Steering","created":"2026-05-03","cited_by_count":0,"area":"mllm"},{"id":"2606.06760","title":"MedSIGHT: Towards Grounded Visual Comprehension in Medical Large Vision-Language Models","created":"2026-06-04","cited_by_count":0,"area":"mllm"},{"id":"2606.26196","title":"From Structure to Synergy: A Survey of Vision-Language Perception Paradigm Evolution in Multimodal Large Language Models","created":"2026-06-24","cited_by_count":0,"area":"mllm"},{"id":"2608.01354","title":"PixVL: Self-Supervised Training of Pixel-Level MLLMs via a Unified Mask--Text Consistency Cycle","created":"2026-08-02","cited_by_count":0,"area":"mllm"},{"id":"2610.18729","title":"Segment Where You Look: Attention as the Interface Between Language Models and Segmenters","created":"2026-10-13","cited_by_count":0,"area":"mllm"}],"next_cursor":null}