{"total":34,"rows":[{"id":"2306.11029","title":"RemoteCLIP: A Vision Language Foundation Model for Remote Sensing","created":"2023-06-19","cited_by_count":363,"area":"mllm"},{"id":"2207.11514","title":"Semantic Abstraction: Open-World 3D Scene Understanding from 2D Vision-Language Models","created":"2022-07-23","cited_by_count":95,"area":"mllm"},{"id":"2304.00788","title":"Open-Vocabulary Point-Cloud Object Detection without 3D Annotation","created":"2023-04-03","cited_by_count":81,"area":"mllm"},{"id":"2209.15517","title":"Medical Image Understanding with Pretrained Vision Language Models: A Comprehensive Study","created":"2022-09-30","cited_by_count":66,"area":"mllm"},{"id":"2410.07112","title":"VHELM: A Holistic Evaluation of Vision Language Models","created":"2024-10-09","cited_by_count":53,"area":"mllm"},{"id":"2502.04263","title":"Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion","created":"2025-02-06","cited_by_count":43,"area":"mllm"},{"id":"2406.05491","title":"One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models","created":"2024-06-08","cited_by_count":40,"area":"mllm"},{"id":"2206.01127","title":"VL-BEiT: Generative Vision-Language Pretraining","created":"2022-06-02","cited_by_count":39,"area":"mllm"},{"id":"2305.14014","title":"CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model","created":"2023-05-23","cited_by_count":30,"area":"mllm"},{"id":"2410.22456","title":"Image2Struct: Benchmarking Structure Extraction for Vision-Language Models","created":"2024-10-29","cited_by_count":19,"area":"mllm"},{"id":"2407.03056","title":"Improving Zero-shot Generalization of Learned Prompts via Unsupervised Knowledge Distillation","created":"2024-07-03","cited_by_count":18,"area":"mllm"},{"id":"2302.06891","title":"UKnow: A Unified Knowledge Protocol with Multimodal Knowledge Graph Datasets for Reasoning and Vision-Language Pre-Training","created":"2023-02-14","cited_by_count":14,"area":"mllm"},{"id":"2409.16718","title":"Vision-Language Model Fine-Tuning via Simple Parameter-Efficient Modification","created":"2024-09-25","cited_by_count":14,"area":"mllm"},{"id":"2412.00440","title":"Advancing Myopia To Holism: Fully Contrastive Language-Image Pre-training","created":"2024-11-30","cited_by_count":13,"area":"mllm"},{"id":"2312.07408","title":"Turbo: Informativity-Driven Acceleration Plug-In for Vision-Language Models","created":"2023-12-12","cited_by_count":11,"area":"mllm"},{"id":"2303.04497","title":"Exploiting the Textual Potential from Vision-Language Pre-training for Text-based Person Search","created":"2023-03-08","cited_by_count":7,"area":"mllm"},{"id":"2311.17315","title":"Explaining CLIP's performance disparities on data from blind/low vision users","created":"2023-11-29","cited_by_count":7,"area":"mllm"},{"id":"2403.10492","title":"Mitigating Dialogue Hallucination for Large Vision Language Models via Adversarial Instruction Tuning","created":"2024-03-15","cited_by_count":7,"area":"mllm"},{"id":"2412.09353","title":"Causal Graphical Models for Vision-Language Compositional Understanding","created":"2024-12-12","cited_by_count":6,"area":"mllm"},{"id":"2410.11639","title":"Efficient and Effective Universal Adversarial Attack against Vision-Language Pre-training Models","created":"2024-10-15","cited_by_count":4,"area":"mllm"},{"id":"2412.07119","title":"DiffCLIP: Few-shot Language-driven Multimodal Classifier","created":"2024-12-10","cited_by_count":4,"area":"mllm"},{"id":"2502.11969","title":"Learning Generalizable Prompt for CLIP with Class Similarity Knowledge","created":"2025-02-17","cited_by_count":3,"area":"mllm"},{"id":"2507.06210","title":"CultureCLIP: Empowering CLIP with Cultural Awareness through Synthetic Images and Contextualized Captions","created":"2025-07-08","cited_by_count":3,"area":"mllm"},{"id":"2508.16707","title":"Sparse and Dense Retrievers Learn Better Together: Joint Sparse-Dense Optimization for Text-Image Retrieval","created":"2025-08-22","cited_by_count":3,"area":"mllm"},{"id":"2508.17290","title":"MEENA (PersianMMMU): Multimodal-Multilingual Educational Exams for N-level Assessment","created":"2025-08-24","cited_by_count":3,"area":"mllm"},{"id":"2603.19862","title":"IsoCLIP: Decomposing CLIP Projectors for Efficient Intra-modal Alignment","created":"2026-03-20","cited_by_count":3,"area":"mllm"},{"id":"2406.12638","title":"Efficient and Long-Tailed Generalization for Pre-trained Vision-Language Model","created":"2024-06-18","cited_by_count":2,"area":"mllm"},{"id":"2509.15695","title":"ORIC: Benchmarking Object Recognition under Contextual Incongruity in Large Vision-Language Models","created":"2025-09-19","cited_by_count":2,"area":"mllm"},{"id":"2306.11400","title":"MuDPT: Multi-modal Deep-symphysis Prompt Tuning for Large Pre-trained Vision-Language Models","created":"2023-06-20","cited_by_count":1,"area":"mllm"},{"id":"2312.10613","title":"p-Laplacian Adaptation for Generative Pre-trained Vision-Language Models","created":"2023-12-17","cited_by_count":1,"area":"mllm"},{"id":"2606.06938","title":"When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness","created":"2026-06-05","cited_by_count":1,"area":"mllm"},{"id":"2307.15983","title":"Instance-Wise Adaptive Tuning and Caching for Vision-Language Models","created":"2023-07-29","cited_by_count":0,"area":"mllm"},{"id":"2605.14893","title":"Your CLIP has 164 dimensions of noise: Exploring the embeddings covariance eigenspectrum of contrastively pretrained vision-language transformers","created":"2026-05-14","cited_by_count":0,"area":"mllm"},{"id":"2605.22484","title":"Supervised Classification Heads as Semantic Prototypes: Unlocking Vision-Language Alignment via Weight Recycling","created":"2026-05-21","cited_by_count":0,"area":"mllm"}],"next_cursor":null}