{"total":50,"rows":[{"id":"2310.07704","title":"Ferret: Refer and Ground Anything Anywhere at Any Granularity","created":"2023-10-11","cited_by_count":509,"area":"mllm"},{"id":"2112.02413","title":"PointCLIP: Point Cloud Understanding by CLIP","created":"2021-12-04","cited_by_count":463,"area":"mllm"},{"id":"2111.03930","title":"Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling","created":"2021-11-06","cited_by_count":431,"area":"mllm"},{"id":"2207.09519","title":"Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classification","created":"2022-07-19","cited_by_count":362,"area":"mllm"},{"id":"2303.02153","title":"Unleashing Text-to-Image Diffusion Models for Visual Perception","created":"2023-03-03","cited_by_count":269,"area":"mllm"},{"id":"2111.08276","title":"Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts","created":"2021-11-16","cited_by_count":256,"area":"mllm"},{"id":"2304.06712","title":"What does CLIP know about a red circle? Visual prompt engineering for VLMs","created":"2023-04-13","cited_by_count":218,"area":"mllm"},{"id":"2109.11797","title":"CPT: Colorful Prompt Tuning for Pre-trained Vision-Language Models","created":"2021-09-24","cited_by_count":183,"area":"mllm"},{"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":"2312.10103","title":"GSVA: Generalized Segmentation via Multimodal Large Language Models","created":"2023-12-15","cited_by_count":154,"area":"mllm"},{"id":"2206.09391","title":"Towards Adversarial Attack on Vision-Language Pre-training Models","created":"2022-06-19","cited_by_count":144,"area":"mllm"},{"id":"2111.12085","title":"UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language Modeling","created":"2021-11-23","cited_by_count":122,"area":"mllm"},{"id":"2210.09263","title":"Vision-Language Pre-training: Basics, Recent Advances, and Future Trends","created":"2022-10-17","cited_by_count":117,"area":"mllm"},{"id":"2206.07643","title":"Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone","created":"2022-06-15","cited_by_count":109,"area":"mllm"},{"id":"2403.14598","title":"PSALM: Pixelwise SegmentAtion with Large Multi-Modal Model","created":"2024-03-21","cited_by_count":101,"area":"mllm"},{"id":"2403.13600","title":"VL-Mamba: Exploring State Space Models for Multimodal Learning","created":"2024-03-20","cited_by_count":93,"area":"mllm"},{"id":"2306.04356","title":"Fine-Grained Visual Prompting","created":"2023-06-07","cited_by_count":82,"area":"mllm"},{"id":"2311.04498","title":"NExT-Chat: An LMM for Chat, Detection and Segmentation","created":"2023-11-08","cited_by_count":80,"area":"mllm"},{"id":"2409.10542","title":"SAM4MLLM: Enhance Multi-Modal Large Language Model for Referring Expression Segmentation","created":"2024-09-01","cited_by_count":69,"area":"mllm"},{"id":"2312.12423","title":"Jack of All Tasks, Master of Many: Designing General-purpose Coarse-to-Fine Vision-Language Model","created":"2023-12-19","cited_by_count":53,"area":"mllm"},{"id":"2403.12488","title":"DetToolChain: A New Prompting Paradigm to Unleash Detection Ability of MLLM","created":"2024-03-19","cited_by_count":46,"area":"mllm"},{"id":"2403.12445","title":"Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial Trajectory","created":"2024-03-19","cited_by_count":36,"area":"mllm"},{"id":"2405.03685","title":"Language-Image Models with 3D Understanding","created":"2024-05-06","cited_by_count":31,"area":"mllm"},{"id":"2205.11169","title":"PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models","created":"2022-05-23","cited_by_count":30,"area":"mllm"},{"id":"2412.19326","title":"Task Preference Optimization: Improving Multimodal Large Language Models with Vision Task Alignment","created":"2024-12-26","cited_by_count":29,"area":"mllm"},{"id":"2206.15462","title":"Improving Visual Grounding by Encouraging Consistent Gradient-based Explanations","created":"2022-06-30","cited_by_count":24,"area":"mllm"},{"id":"2312.15043","title":"GroundVLP: Harnessing Zero-shot Visual Grounding from Vision-Language Pre-training and Open-Vocabulary Object Detection","created":"2023-12-22","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":"2503.15426","title":"Visual Position Prompt for MLLM based Visual Grounding","created":"2025-03-19","cited_by_count":16,"area":"mllm"},{"id":"2208.09374","title":"VLMAE: Vision-Language Masked Autoencoder","created":"2022-08-19","cited_by_count":11,"area":"mllm"},{"id":"2407.01131","title":"M2IST: Multi-Modal Interactive Side-Tuning for Efficient Referring Expression Comprehension","created":"2024-07-01","cited_by_count":11,"area":"mllm"},{"id":"2409.13407","title":"Instruction-guided Multi-Granularity Segmentation and Captioning with Large Multimodal Model","created":"2024-09-20","cited_by_count":8,"area":"mllm"},{"id":"2505.03788","title":"Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding","created":"2025-04-30","cited_by_count":8,"area":"mllm"},{"id":"2211.11492","title":"ClipCrop: Conditioned Cropping Driven by Vision-Language Model","created":"2022-11-21","cited_by_count":7,"area":"mllm"},{"id":"2409.10419","title":"HiFi-CS: Towards Open Vocabulary Visual Grounding For Robotic Grasping Using Vision-Language Models","created":"2024-09-16","cited_by_count":7,"area":"mllm"},{"id":"2210.13591","title":"Learning by Hallucinating: Vision-Language Pre-training with Weak Supervision","created":"2022-10-24","cited_by_count":6,"area":"mllm"},{"id":"2307.03254","title":"Vision Language Transformers: A Survey","created":"2023-07-06","cited_by_count":5,"area":"mllm"},{"id":"2506.17901","title":"PostAlign: Multimodal Grounding as a Corrective Lens for MLLMs","created":"2025-06-22","cited_by_count":5,"area":"mllm"},{"id":"2509.21576","title":"Vision Language Models Cannot Plan, but Can They Formalize?","created":"2025-09-25","cited_by_count":3,"area":"mllm"},{"id":"2508.08066","title":"ExpVG: Investigating the Design Space of Visual Grounding in Multimodal Large Language Model","created":"2025-08-11","cited_by_count":2,"area":"mllm"},{"id":"2305.11497","title":"TreePrompt: Learning to Compose Tree Prompts for Explainable Visual Grounding","created":"2023-05-19","cited_by_count":1,"area":"mllm"},{"id":"2405.18770","title":"Multimodal Adversarial Defense for Vision-Language Models by Leveraging One-To-Many Relationships","created":"2024-05-29","cited_by_count":1,"area":"mllm"},{"id":"2503.07413","title":"REF-VLM: Triplet-Based Referring Paradigm for Unified Visual Decoding","created":"2025-03-10","cited_by_count":1,"area":"mllm"},{"id":"2505.19242","title":"Deformable Attentive Visual Enhancement for Referring Segmentation Using Vision-Language Model","created":"2025-05-25","cited_by_count":1,"area":"mllm"},{"id":"2608.16805","title":"Diagnosing Dense Same-Class Attribute Misbinding in Large Vision-Language Models","created":"2026-08-17","cited_by_count":1,"area":"mllm"},{"id":"2303.05313","title":"Refined Vision-Language Modeling for Fine-grained Multi-modal Pre-training","created":"2023-03-09","cited_by_count":0,"area":"mllm"},{"id":"2412.08110","title":"The ART of Composition: Attention-Regularized Training for Compositional Visual Grounding","created":"2024-12-11","cited_by_count":0,"area":"mllm"},{"id":"2607.02490","title":"Visually Grounded Self-Reflection for Vision-Language Models via Reinforcement Learning","created":"2026-07-02","cited_by_count":0,"area":"mllm"},{"id":"2608.17234","title":"COMIC: Reference-Aware Safety Gating for Multimodal Large Language Models","created":"2026-08-18","cited_by_count":0,"area":"mllm"},{"id":"2608.25299","title":"PointRL: Learning Point-Level Vision-Language Grounding from Verifiable Annotation Evidence","created":"2026-08-26","cited_by_count":0,"area":"mllm"}],"next_cursor":null}