{"total":170,"rows":[{"id":"2403.09611","title":"MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training","created":"2024-03-14","cited_by_count":232,"area":"mllm"},{"id":"2407.12442","title":"ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference","created":"2024-07-17","cited_by_count":113,"area":"mllm"},{"id":"2408.04883","title":"ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation","created":"2024-08-09","cited_by_count":111,"area":null},{"id":"2503.06287","title":"Your Large Vision-Language Model Only Needs A Few Attention Heads For Visual Grounding","created":"2025-03-08","cited_by_count":83,"area":"mllm"},{"id":"2412.16334","title":"DINOv2 Meets Text: A Unified Framework for Image- and Pixel-Level Vision-Language Alignment","created":"2024-12-20","cited_by_count":79,"area":null},{"id":"2404.08181","title":"Pay Attention to Your Neighbours: Training-Free Open-Vocabulary Semantic Segmentation","created":"2024-04-12","cited_by_count":77,"area":null},{"id":"2407.08268","title":"Explore the Potential of CLIP for Training-Free Open Vocabulary Semantic Segmentation","created":"2024-07-11","cited_by_count":61,"area":"mllm"},{"id":"2412.03561","title":"FLAIR: VLM with Fine-grained Language-informed Image Representations","created":"2024-12-04","cited_by_count":48,"area":"mllm"},{"id":"2411.19331","title":"Talking to DINO: Bridging Self-Supervised Vision Backbones with Language for Open-Vocabulary Segmentation","created":"2024-11-28","cited_by_count":44,"area":null},{"id":"2405.14858","title":"Mamba-R: Vision Mamba ALSO Needs Registers","created":"2024-05-23","cited_by_count":41,"area":"ssm"},{"id":"2411.09219","title":"Harnessing Vision Foundation Models for High-Performance, Training-Free Open Vocabulary Segmentation","created":"2024-11-14","cited_by_count":39,"area":null},{"id":"2505.18022","title":"RemoteSAM: Towards Segment Anything for Earth Observation","created":"2025-05-23","cited_by_count":39,"area":null},{"id":"2408.04961","title":"In Defense of Lazy Visual Grounding for Open-Vocabulary Semantic Segmentation","created":"2024-08-09","cited_by_count":38,"area":null},{"id":"2408.12957","title":"Image Segmentation in Foundation Model Era: A Survey","created":"2024-08-23","cited_by_count":35,"area":"diffusion"},{"id":"2411.10086","title":"CorrCLIP: Reconstructing Patch Correlations in CLIP for Open-Vocabulary Semantic Segmentation","created":"2024-11-15","cited_by_count":34,"area":null},{"id":"2411.15869","title":"Self-Calibrated CLIP for Training-Free Open-Vocabulary Segmentation","created":"2024-11-24","cited_by_count":34,"area":"mllm"},{"id":"2505.04410","title":"DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception","created":"2025-05-07","cited_by_count":33,"area":null},{"id":"2412.04616","title":"Assessing and Learning Alignment of Unimodal Vision and Language Models","created":"2024-12-05","cited_by_count":32,"area":null},{"id":"2411.15851","title":"ResCLIP: Residual Attention for Training-free Dense Vision-language Inference","created":"2024-11-24","cited_by_count":29,"area":"mllm"},{"id":"2502.03738","title":"Scaling Laws in Patchification: An Image Is Worth 50,176 Tokens And More","created":"2025-02-06","cited_by_count":28,"area":null},{"id":"2410.01768","title":"SegEarth-OV: Towards Training-Free Open-Vocabulary Segmentation for Remote Sensing Images","created":"2024-10-02","cited_by_count":25,"area":null},{"id":"2411.17150","title":"Distilling Spectral Graph for Object-Context Aware Open-Vocabulary Semantic Segmentation","created":"2024-11-26","cited_by_count":24,"area":"mllm"},{"id":"2501.12931","title":"DynamicEarth: How Far are We from Open-Vocabulary Change Detection?","created":"2025-01-22","cited_by_count":21,"area":null},{"id":"2503.08723","title":"Is CLIP ideal? No. Can we fix it? Yes!","created":"2025-03-10","cited_by_count":21,"area":null},{"id":"2503.19777","title":"LPOSS: Label Propagation Over Patches and Pixels for Open-vocabulary Semantic Segmentation","created":"2025-03-25","cited_by_count":21,"area":null},{"id":"2506.11136","title":"JAFAR: Jack up Any Feature at Any Resolution","created":"2025-06-10","cited_by_count":21,"area":null},{"id":"2412.04533","title":"Mask-Adapter: The Devil is in the Masks for Open-Vocabulary Segmentation","created":"2024-12-05","cited_by_count":20,"area":"mllm"},{"id":"2401.03870","title":"Gramformer: Learning Crowd Counting via Graph-Modulated Transformer","created":"2024-01-08","cited_by_count":19,"area":null},{"id":"2411.13836","title":"CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation","created":"2024-11-21","cited_by_count":17,"area":null},{"id":"2511.07923","title":"Exploring the Underwater World Segmentation without Extra Training","created":"2025-11-11","cited_by_count":16,"area":null},{"id":"2512.08730","title":"SegEarth-OV3: Exploring SAM 3 for Open-Vocabulary Semantic Segmentation in Remote Sensing Images","created":"2025-12-09","cited_by_count":16,"area":null},{"id":"2412.01814","title":"COSMOS: Cross-Modality Self-Distillation for Vision Language Pre-training","created":"2024-12-02","cited_by_count":15,"area":"mllm"},{"id":"2602.22394","title":"Vision Transformers Need More Than Registers","created":"2026-02-25","cited_by_count":15,"area":null},{"id":"2410.05266","title":"Brain Mapping with Dense Features: Grounding Cortical Semantic Selectivity in Natural Images With Vision Transformers","created":"2024-10-07","cited_by_count":13,"area":null},{"id":"2507.23300","title":"Training-free Geometric Image Editing on Diffusion Models","created":"2025-07-31","cited_by_count":13,"area":"diffusion"},{"id":"2505.21501","title":"Vision Transformers with Self-Distilled Registers","created":"2025-05-27","cited_by_count":12,"area":null},{"id":"2508.20265","title":"Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation","created":"2025-08-27","cited_by_count":12,"area":null},{"id":"2511.13211","title":"3DAlign-DAER: Dynamic Attention Policy and Efficient Retrieval Strategy for Fine-grained 3D-Text Alignment at Scale","created":"2025-11-17","cited_by_count":12,"area":null},{"id":"2409.03209","title":"iSeg: An Iterative Refinement-based Framework for Training-free Segmentation","created":"2024-09-05","cited_by_count":10,"area":"diffusion"},{"id":"2505.21844","title":"Test-Time Adaptation of Vision-Language Models for Open-Vocabulary Semantic Segmentation","created":"2025-05-28","cited_by_count":10,"area":"mllm"},{"id":"2508.11256","title":"Generalized Decoupled Learning for Enhancing Open-Vocabulary Dense Perception","created":"2025-08-15","cited_by_count":10,"area":null},{"id":"2410.07599","title":"Adventurer: Optimizing Vision Mamba Architecture Designs for Efficiency","created":"2024-10-10","cited_by_count":9,"area":"ssm"},{"id":"2412.06244","title":"Unbiased Region-Language Alignment for Open-Vocabulary Dense Prediction","created":"2024-12-09","cited_by_count":9,"area":"mllm"},{"id":"2505.05892","title":"Register and [CLS] tokens yield a decoupling of local and global features in large ViTs","created":"2025-05-09","cited_by_count":9,"area":null},{"id":"2506.21233","title":"ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation","created":"2025-06-26","cited_by_count":9,"area":null},{"id":"2507.07620","title":"ViLU: Learning Vision-Language Uncertainties for Failure Prediction","created":"2025-07-10","cited_by_count":9,"area":"mllm"},{"id":"2508.00557","title":"Training-Free Class Purification for Open-Vocabulary Semantic Segmentation","created":"2025-08-01","cited_by_count":9,"area":"mllm"},{"id":"2511.20223","title":"V-Attack: Targeting Disentangled Value Features for Controllable Adversarial Attacks on LVLMs","created":"2025-11-25","cited_by_count":9,"area":"mllm"},{"id":"2602.08071","title":"ViT-5: Vision Transformers for The Mid-2020s","created":"2026-02-08","cited_by_count":9,"area":null},{"id":"2408.06747","title":"ReCLIP++: Learn to Rectify the Bias of CLIP for Unsupervised Semantic Segmentation","created":"2024-08-13","cited_by_count":8,"area":null},{"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":"2508.18067","title":"Annotation-Free Open-Vocabulary Segmentation for Remote-Sensing Images","created":"2025-08-25","cited_by_count":8,"area":null},{"id":"2410.23370","title":"Multilingual Vision-Language Pre-training for the Remote Sensing Domain","created":"2024-10-30","cited_by_count":7,"area":"mllm"},{"id":"2411.12044","title":"ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements","created":"2024-11-18","cited_by_count":7,"area":"mllm"},{"id":"2505.23769","title":"TextRegion: Text-Aligned Region Tokens from Frozen Image-Text Models","created":"2025-05-29","cited_by_count":7,"area":null},{"id":"2405.14294","title":"Tuning-free Universally-Supervised Semantic Segmentation","created":"2024-05-23","cited_by_count":6,"area":null},{"id":"2508.07877","title":"Selective Contrastive Learning for Weakly Supervised Affordance Grounding","created":"2025-08-11","cited_by_count":6,"area":null},{"id":"2509.15772","title":"Vision-Language Models as Differentiable Semantic and Spatial Rewards for Text-to-3D Generation","created":"2025-09-19","cited_by_count":6,"area":"mllm"},{"id":"2511.18382","title":"ViMix-14M: A Curated Multi-Source Video-Text Dataset with Long-Form, High-Quality Captions and Crawl-Free Access","created":"2025-11-23","cited_by_count":6,"area":null},{"id":"2412.11464","title":"High-Quality Mask Tuning Matters for Open-Vocabulary Segmentation","created":"2024-12-16","cited_by_count":5,"area":"mllm"},{"id":"2501.09138","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","created":"2025-01-15","cited_by_count":5,"area":null},{"id":"2504.00037","title":"ViT-Linearizer: Distilling Quadratic Knowledge into Linear-Time Vision Models","created":"2025-03-30","cited_by_count":5,"area":null},{"id":"2505.19498","title":"Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models","created":"2025-05-26","cited_by_count":5,"area":"mllm"},{"id":"2506.01539","title":"G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models","created":"2025-06-02","cited_by_count":5,"area":"diffusion"},{"id":"2507.11061","title":"Robust 3D-Masked Part-level Editing in 3D Gaussian Splatting with Regularized Score Distillation Sampling","created":"2025-07-15","cited_by_count":5,"area":null},{"id":"2511.16170","title":"Target Refocusing via Attention Redistribution for Open-Vocabulary Semantic Segmentation: An Explainability Perspective","created":"2025-11-20","cited_by_count":5,"area":null},{"id":"2511.19704","title":"RADSeg: Unleashing Parameter and Compute Efficient Zero-Shot Open-Vocabulary Segmentation Using Agglomerative Models","created":"2025-11-24","cited_by_count":5,"area":null},{"id":"2501.16222","title":"SPECIAL: Zero-shot Hyperspectral Image Classification With CLIP","created":"2025-01-27","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":"2504.10487","title":"FLOSS: Free Lunch in Open-vocabulary Semantic Segmentation","created":"2025-04-14","cited_by_count":4,"area":null},{"id":"2505.20444","title":"HoPE: Hybrid of Position Embedding for Long Context Vision-Language Models","created":"2025-05-26","cited_by_count":4,"area":"mllm"},{"id":"2508.04211","title":"What Holds Back Open-Vocabulary Segmentation?","created":"2025-08-06","cited_by_count":4,"area":null},{"id":"2601.13895","title":"OmniOVCD: Streamlining Open-Vocabulary Change Detection with SAM 3","created":"2026-01-20","cited_by_count":4,"area":null},{"id":"2602.06333","title":"Taming SAM3 in the Wild: A Concept Bank for Open-Vocabulary Segmentation","created":"2026-02-06","cited_by_count":4,"area":null},{"id":"2602.18853","title":"Open-Vocabulary Domain Generalization in Urban-Scene Segmentation","created":"2026-02-21","cited_by_count":4,"area":null},{"id":"2406.16085","title":"A Simple Framework for Open-Vocabulary Zero-Shot Segmentation","created":"2024-06-23","cited_by_count":3,"area":null},{"id":"2506.05439","title":"LLMs Can Compensate for Deficiencies in Visual Representations","created":"2025-06-05","cited_by_count":3,"area":"mllm"},{"id":"2507.10118","title":"DEARLi: Decoupled Enhancement of Recognition and Localization for Semi-supervised Panoptic Segmentation","created":"2025-07-14","cited_by_count":3,"area":null},{"id":"2602.04202","title":"VTok: A Unified Video Tokenizer with Decoupled Spatial-Temporal Latents","created":"2026-02-04","cited_by_count":3,"area":null},{"id":"2603.21528","title":"PEARL: Geometry Aligns Semantics for Training-Free Open-Vocabulary Semantic Segmentation","created":"2026-03-23","cited_by_count":3,"area":null},{"id":"2603.29271","title":"ConInfer: Context-Aware Inference for Training-Free Open-Vocabulary Remote Sensing Segmentation","created":"2026-03-31","cited_by_count":3,"area":"mllm"},{"id":"2403.09307","title":"Annotation Free Semantic Segmentation with Vision Foundation Models","created":"2024-03-14","cited_by_count":2,"area":null},{"id":"2406.18197","title":"Human-Free Automated Prompting for Vision-Language Anomaly Detection: Prompt Optimization with Meta-guiding Prompt Scheme","created":"2024-06-26","cited_by_count":2,"area":"mllm"},{"id":"2407.20034","title":"MaskInversion: Localized Embeddings via Optimization of Explainability Maps","created":"2024-07-29","cited_by_count":2,"area":"mllm"},{"id":"2409.16723","title":"EAGLE: Towards Efficient Arbitrary Referring Visual Prompts Comprehension for Multimodal Large Language Models","created":"2024-09-25","cited_by_count":2,"area":"mllm"},{"id":"2501.12632","title":"TeD-Loc: Text Distillation for Weakly Supervised Object Localization","created":"2025-01-22","cited_by_count":2,"area":null},{"id":"2502.06818","title":"Rethinking the Global Knowledge of CLIP in Training-Free Open-Vocabulary Semantic Segmentation","created":"2025-02-05","cited_by_count":2,"area":null},{"id":"2505.24517","title":"un$^2$CLIP: Improving CLIP's Visual Detail Capturing Ability via Inverting unCLIP","created":"2025-05-30","cited_by_count":2,"area":null},{"id":"2506.08678","title":"ATAS: Any-to-Any Self-Distillation for Enhanced Open-Vocabulary Dense Prediction","created":"2025-06-10","cited_by_count":2,"area":"mllm"},{"id":"2511.08248","title":"NERVE: Neighbourhood & Entropy-guided Random-walk for training free open-Vocabulary sEgmentation","created":"2025-11-11","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":"2603.19026","title":"Rethinking MLLM Itself as a Segmenter with a Single Segmentation Token","created":"2026-03-19","cited_by_count":2,"area":"mllm"},{"id":"2603.21386","title":"Mitigating Objectness Bias and Region-to-Text Misalignment for Open-Vocabulary Panoptic Segmentation","created":"2026-03-22","cited_by_count":2,"area":"mllm"},{"id":"2603.22042","title":"Uncertainty-guided Compositional Alignment with Part-to-Whole Semantic Representativeness in Hyperbolic Vision-Language Models","created":"2026-03-23","cited_by_count":2,"area":"mllm"},{"id":"2603.23030","title":"Looking Beyond the Window: Global-Local Aligned CLIP for Training-free Open-Vocabulary Semantic Segmentation","created":"2026-03-24","cited_by_count":2,"area":null},{"id":"2604.15652","title":"Perturbation-Regularized Open-Vocabulary Remote Sensing Segmentation with Unified Multi-Domain Evaluation","created":"2026-04-17","cited_by_count":2,"area":null},{"id":"2604.18573","title":"T-REN: Learning Text-Aligned Region Tokens Improves Dense Vision-Language Alignment and Scalability","created":"2026-04-20","cited_by_count":2,"area":null},{"id":"2605.12325","title":"VIP: Visual-guided Prompt Evolution for Efficient Dense Vision-Language Inference","created":"2026-05-12","cited_by_count":2,"area":null},{"id":"2606.21863","title":"Prompt-Calibrated SAM 3 for Open-Vocabulary Remote Sensing Semantic Segmentation","created":"2026-06-20","cited_by_count":2,"area":null}],"next_cursor":"100"}