{"total":127,"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":"2407.11325","title":"VISA: Reasoning Video Object Segmentation via Large Language Models","created":"2024-07-16","cited_by_count":127,"area":"lr"},{"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":"2412.19806","title":"Vitron: A Unified Pixel-level Vision LLM for Understanding, Generating, Segmenting, Editing","created":"2024-10-08","cited_by_count":49,"area":null},{"id":"2506.22624","title":"Seg-R1: Segmentation Can Be Surprisingly Simple with Reinforcement Learning","created":"2025-06-27","cited_by_count":44,"area":"lr"},{"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":"2503.14908","title":"POSTA: A Go-to Framework for Customized Artistic Poster Generation","created":"2025-03-19","cited_by_count":36,"area":null},{"id":"2411.17606","title":"HyperSeg: Towards Universal Visual Segmentation with Large Language Model","created":"2024-11-26","cited_by_count":33,"area":"lr"},{"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":"2503.08625","title":"SegAgent: Exploring Pixel Understanding Capabilities in MLLMs by Imitating Human Annotator Trajectories","created":"2025-03-11","cited_by_count":30,"area":null},{"id":"2508.04655","title":"X-SAM: From Segment Anything to Any Segmentation","created":"2025-08-06","cited_by_count":30,"area":null},{"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":"2505.23727","title":"PixelThink: Towards Efficient Chain-of-Pixel Reasoning","created":"2025-05-29","cited_by_count":28,"area":"lr"},{"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":"2503.01342","title":"UFO: A Unified Approach to Fine-grained Visual Perception via Open-ended Language Interface","created":"2025-03-03","cited_by_count":27,"area":null},{"id":"2407.14500","title":"ViLLa: Video Reasoning Segmentation with Large Language Model","created":"2024-07-18","cited_by_count":24,"area":"lr"},{"id":"2504.10465","title":"Pixel-SAIL: Single Transformer For Pixel-Grounded Understanding","created":"2025-04-14","cited_by_count":22,"area":"mllm"},{"id":"2508.00265","title":"Multimodal Referring Segmentation: A Survey","created":"2025-08-01","cited_by_count":22,"area":null},{"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":"2511.01571","title":"PixelVLA: Advancing Pixel-level Understanding in Vision-Language-Action Model","created":"2025-11-03","cited_by_count":18,"area":"vla"},{"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":"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":"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":"2507.23755","title":"Slot Attention with Re-Initialization and Self-Distillation","created":"2025-07-31","cited_by_count":14,"area":"distill"},{"id":"2510.03853","title":"UGround: Towards Unified Visual Grounding with Unrolled Transformers","created":"2025-10-04","cited_by_count":14,"area":null},{"id":"2407.07412","title":"Pseudo-RIS: Distinctive Pseudo-supervision Generation for Referring Image Segmentation","created":"2024-07-10","cited_by_count":13,"area":null},{"id":"2507.20025","title":"Region-based Cluster Discrimination for Visual Representation Learning","created":"2025-07-26","cited_by_count":13,"area":null},{"id":"2502.20263","title":"Vector-Quantized Vision Foundation Models for Object-Centric Learning","created":"2025-02-27","cited_by_count":11,"area":null},{"id":"2601.16093","title":"SAMTok: Representing Any Mask with Two Words","created":"2026-01-22","cited_by_count":11,"area":null},{"id":"2503.24379","title":"Any2Caption:Interpreting Any Condition to Caption for Controllable Video Generation","created":"2025-03-31","cited_by_count":10,"area":null},{"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":"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":"2510.16714","title":"SceneCOT: Eliciting Grounded Chain-of-Thought Reasoning in 3D Scenes","created":"2025-10-19","cited_by_count":8,"area":"lr"},{"id":"2502.09447","title":"Pixel-Level Reasoning Segmentation via Multi-turn Conversations","created":"2025-02-13","cited_by_count":7,"area":"lr"},{"id":"2502.09906","title":"Insect-Foundation: A Foundation Model and Large Multimodal Dataset for Vision-Language Insect Understanding","created":"2025-02-14","cited_by_count":7,"area":null},{"id":"2509.16972","title":"The 1st Solution for 7th LSVOS RVOS Track: SaSaSa2VA","created":"2025-09-21","cited_by_count":7,"area":null},{"id":"2503.15019","title":"Learning 4D Panoptic Scene Graph Generation from Rich 2D Visual Scene","created":"2025-03-19","cited_by_count":6,"area":null},{"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":"2510.13796","title":"The Mechanistic Emergence of Symbol Grounding in Language Models","created":"2025-10-15","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":"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":"2510.06243","title":"CoT Referring: Improving Referring Expression Tasks with Grounded Reasoning","created":"2025-10-03","cited_by_count":5,"area":"lr"},{"id":"2510.20803","title":"ARGenSeg: Image Segmentation with Autoregressive Image Generation Model","created":"2025-10-23","cited_by_count":5,"area":null},{"id":"2512.01843","title":"PhyDetEx: Detecting and Explaining the Physical Plausibility of T2V Models","created":"2025-12-01","cited_by_count":5,"area":null},{"id":"2601.07290","title":"VideoLoom: A Video Large Language Model for Joint Spatial-Temporal Understanding","created":"2026-01-12","cited_by_count":5,"area":null},{"id":"2601.19228","title":"Towards Pixel-Level VLM Perception via Simple Points Prediction","created":"2026-01-27","cited_by_count":5,"area":"mllm"},{"id":"2412.11409","title":"Multi-modal and Multi-scale Spatial Environment Understanding for Immersive Visual Text-to-Speech","created":"2024-12-16","cited_by_count":4,"area":null},{"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":"2503.23508","title":"Re-Aligning Language to Visual Objects with an Agentic Workflow","created":"2025-03-30","cited_by_count":4,"area":"agents"},{"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":"2505.15172","title":"Harnessing Caption Detailness for Data-Efficient Text-to-Image Generation","created":"2025-05-21","cited_by_count":4,"area":null},{"id":"2505.17994","title":"Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation","created":"2025-05-23","cited_by_count":4,"area":null},{"id":"2505.19422","title":"LlamaSeg: Image Segmentation via Autoregressive Mask Generation","created":"2025-05-26","cited_by_count":4,"area":null},{"id":"2506.04650","title":"Neural Network Reprogrammability: A Unified Theme on Model Reprogramming, Prompt Tuning, and Prompt Instruction","created":"2025-06-05","cited_by_count":4,"area":null},{"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":"2510.09274","title":"MomentSeg: Moment-Centric Sampling for Enhanced Video Pixel Understanding","created":"2025-10-10","cited_by_count":4,"area":"lr"},{"id":"2605.20676","title":"VISTAQA: Benchmarking Joint Visual Question Answering and Pixel-Level Evidence","created":"2026-05-20","cited_by_count":4,"area":"lr"},{"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":"2411.19106","title":"Detailed Object Description with Controllable Dimensions","created":"2024-11-28","cited_by_count":3,"area":null},{"id":"2503.10225","title":"Unveiling the Invisible: Reasoning Complex Occlusions Amodally with AURA","created":"2025-03-13","cited_by_count":3,"area":"lr"},{"id":"2507.16213","title":"Advancing Visual Large Language Model for Multi-granular Versatile Perception","created":"2025-07-22","cited_by_count":3,"area":null},{"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":"2504.00476","title":"4th PVUW MeViS 3rd Place Report: Sa2VA","created":"2025-04-01","cited_by_count":2,"area":null},{"id":"2506.13552","title":"A Comprehensive Survey on Video Scene Parsing:Advances, Challenges, and Prospects","created":"2025-06-16","cited_by_count":2,"area":null},{"id":"2506.22880","title":"Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and Grounder","created":"2025-06-28","cited_by_count":2,"area":"lr"},{"id":"2507.12883","title":"HRSeg: High-Resolution Visual Perception and Enhancement for Reasoning Segmentation","created":"2025-07-17","cited_by_count":2,"area":null},{"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":"2605.18018","title":"See What I Mean: Aligning Vision and Language Representations for Video Fine-grained Object Understanding","created":"2026-05-18","cited_by_count":2,"area":null},{"id":"2605.20110","title":"SetCon: Towards Open-Ended Referring Segmentation via Set-Level Concept Prediction","created":"2026-05-19","cited_by_count":2,"area":null},{"id":"2607.00115","title":"PixelEyes: Decoupling Perception and Reasoning for Pinpoint Visual Evidence Seeking","created":"2026-06-30","cited_by_count":2,"area":"lr"},{"id":"2410.15312","title":"Synergistic Dual Spatial-aware Generation of Image-to-Text and Text-to-Image","created":"2024-10-20","cited_by_count":1,"area":null},{"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":"2509.24109","title":"SVAC: Scaling Is All You Need For Referring Video Object Segmentation","created":"2025-09-28","cited_by_count":1,"area":null},{"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":"2603.06973","title":"T2SGrid: Temporal-to-Spatial Gridification for Video Temporal Grounding","created":"2026-03-07","cited_by_count":1,"area":null},{"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"}],"next_cursor":"100"}