{"total":279,"rows":[{"id":"2310.03744","title":"Improved Baselines with Visual Instruction Tuning","created":"2023-10-05","cited_by_count":4418,"area":"mllm"},{"id":"2109.01134","title":"Learning to Prompt for Vision-Language Models","created":"2021-09-02","cited_by_count":2281,"area":"mllm"},{"id":"2203.05557","title":"Conditional Prompt Learning for Vision-Language Models","created":"2022-03-10","cited_by_count":1433,"area":"mllm"},{"id":"2304.00685","title":"Vision-Language Models for Vision Tasks: A Survey","created":"2023-04-03","cited_by_count":839,"area":"mllm"},{"id":"2401.06209","title":"Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs","created":"2024-01-11","cited_by_count":735,"area":"mllm"},{"id":"2210.01936","title":"When and why vision-language models behave like bags-of-words, and what to do about it?","created":"2022-10-04","cited_by_count":610,"area":"mllm"},{"id":"2311.05608","title":"FigStep: Jailbreaking Large Vision-Language Models via Typographic Visual Prompts","created":"2023-11-09","cited_by_count":398,"area":"mllm"},{"id":"2209.07511","title":"Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models","created":"2022-09-15","cited_by_count":382,"area":"mllm"},{"id":"2306.11029","title":"RemoteCLIP: A Vision Language Foundation Model for Remote Sensing","created":"2023-06-19","cited_by_count":363,"area":"mllm"},{"id":"2303.13283","title":"Visual-Language Prompt Tuning with Knowledge-guided Context Optimization","created":"2023-03-23","cited_by_count":308,"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":"2205.03340","title":"Prompt Distribution Learning","created":"2022-05-06","cited_by_count":235,"area":"mllm"},{"id":"2202.10401","title":"Vision-Language Pre-Training with Triple Contrastive Learning","created":"2022-02-21","cited_by_count":212,"area":"mllm"},{"id":"2308.06038","title":"Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning","created":"2023-08-11","cited_by_count":154,"area":"mllm"},{"id":"2204.03649","title":"Unsupervised Prompt Learning for Vision-Language Models","created":"2022-04-07","cited_by_count":127,"area":"mllm"},{"id":"2403.02469","title":"Vision-Language Models for Medical Report Generation and Visual Question Answering: A Review","created":"2024-03-04","cited_by_count":125,"area":"mllm"},{"id":"2402.12336","title":"Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models","created":"2024-02-19","cited_by_count":120,"area":"mllm"},{"id":"2309.10313","title":"Investigating the Catastrophic Forgetting in Multimodal Large Language Models","created":"2023-09-19","cited_by_count":119,"area":"mllm"},{"id":"2108.09980","title":"TACo: Token-aware Cascade Contrastive Learning for Video-Text Alignment","created":"2021-08-23","cited_by_count":102,"area":"mllm"},{"id":"2312.00878","title":"Grounding Everything: Emerging Localization Properties in Vision-Language Transformers","created":"2023-12-01","cited_by_count":91,"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":"2308.07898","title":"A Foundation Language-Image Model of the Retina (FLAIR): Encoding Expert Knowledge in Text Supervision","created":"2023-08-15","cited_by_count":78,"area":"mllm"},{"id":"2303.11313","title":"CLIP goes 3D: Leveraging Prompt Tuning for Language Grounded 3D Recognition","created":"2023-03-20","cited_by_count":77,"area":"mllm"},{"id":"2411.02059","title":"TableGPT2: A Large Multimodal Model with Tabular Data Integration","created":"2024-11-04","cited_by_count":72,"area":"mllm"},{"id":"2303.10431","title":"DeAR: Debiasing Vision-Language Models with Additive Residuals","created":"2023-03-18","cited_by_count":70,"area":"mllm"},{"id":"2310.07699","title":"VeCLIP: Improving CLIP Training via Visual-enriched Captions","created":"2023-10-11","cited_by_count":70,"area":"mllm"},{"id":"2311.16194","title":"BadCLIP: Trigger-Aware Prompt Learning for Backdoor Attacks on CLIP","created":"2023-11-26","cited_by_count":67,"area":"mllm"},{"id":"2310.13355","title":"SILC: Improving Vision Language Pretraining with Self-Distillation","created":"2023-10-20","cited_by_count":66,"area":"mllm"},{"id":"2507.22062","title":"Meta CLIP 2: A Worldwide Scaling Recipe","created":"2025-07-29","cited_by_count":64,"area":"mllm"},{"id":"2504.01901","title":"Ross3D: Reconstructive Visual Instruction Tuning with 3D-Awareness","created":"2025-04-02","cited_by_count":63,"area":"mllm"},{"id":"2306.11925","title":"LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching","created":"2023-06-20","cited_by_count":60,"area":"mllm"},{"id":"2209.13430","title":"UniCLIP: Unified Framework for Contrastive Language-Image Pre-training","created":"2022-09-27","cited_by_count":54,"area":"mllm"},{"id":"2306.03899","title":"Towards Label-free Scene Understanding by Vision Foundation Models","created":"2023-06-06","cited_by_count":54,"area":"mllm"},{"id":"2211.11720","title":"Multitask Vision-Language Prompt Tuning","created":"2022-11-21","cited_by_count":51,"area":"mllm"},{"id":"2307.11661","title":"Enhancing CLIP with GPT-4: Harnessing Visual Descriptions as Prompts","created":"2023-07-21","cited_by_count":50,"area":"mllm"},{"id":"2111.14745","title":"A Simple Long-Tailed Recognition Baseline via Vision-Language Model","created":"2021-11-29","cited_by_count":49,"area":"mllm"},{"id":"2401.04350","title":"Pre-trained Model Guided Fine-Tuning for Zero-Shot Adversarial Robustness","created":"2024-01-09","cited_by_count":48,"area":"mllm"},{"id":"2306.04811","title":"Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation","created":"2023-06-07","cited_by_count":47,"area":"mllm"},{"id":"2309.01151","title":"EdaDet: Open-Vocabulary Object Detection Using Early Dense Alignment","created":"2023-09-03","cited_by_count":44,"area":"mllm"},{"id":"2307.08347","title":"M-FLAG: Medical Vision-Language Pre-training with Frozen Language Models and Latent Space Geometry Optimization","created":"2023-07-17","cited_by_count":43,"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":"2305.12223","title":"What Makes for Good Visual Tokenizers for Large Language Models?","created":"2023-05-20","cited_by_count":42,"area":"mllm"},{"id":"2405.00740","title":"Modeling Caption Diversity in Contrastive Vision-Language Pretraining","created":"2024-04-30","cited_by_count":42,"area":"mllm"},{"id":"2103.08849","title":"Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models","created":"2021-03-16","cited_by_count":41,"area":"mllm"},{"id":"2210.15138","title":"Open-vocabulary Semantic Segmentation with Frozen Vision-Language Models","created":"2022-10-27","cited_by_count":39,"area":"mllm"},{"id":"2403.14774","title":"Few-Shot Adversarial Prompt Learning on Vision-Language Models","created":"2024-03-21","cited_by_count":39,"area":"mllm"},{"id":"2305.18010","title":"Test-Time Adaptation with CLIP Reward for Zero-Shot Generalization in Vision-Language Models","created":"2023-05-29","cited_by_count":38,"area":"mllm"},{"id":"2404.09654","title":"Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection","created":"2024-04-15","cited_by_count":36,"area":"mllm"},{"id":"2308.03793","title":"ReCLIP: Refine Contrastive Language Image Pre-Training with Source Free Domain Adaptation","created":"2023-08-04","cited_by_count":35,"area":"mllm"},{"id":"2311.01723","title":"Towards Calibrated Robust Fine-Tuning of Vision-Language Models","created":"2023-11-03","cited_by_count":35,"area":"mllm"},{"id":"2510.25616","title":"Don't Blind Your VLA: Aligning Visual Representations for OOD Generalization","created":"2025-10-29","cited_by_count":35,"area":"mllm"},{"id":"2210.03794","title":"SVL-Adapter: Self-Supervised Adapter for Vision-Language Pretrained Models","created":"2022-10-07","cited_by_count":34,"area":"mllm"},{"id":"2211.16191","title":"SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image Classification","created":"2022-11-28","cited_by_count":34,"area":"mllm"},{"id":"2305.01040","title":"CLIP-S$^4$: Language-Guided Self-Supervised Semantic Segmentation","created":"2023-05-01","cited_by_count":34,"area":"mllm"},{"id":"2312.14494","title":"Revisiting Few-Shot Object Detection with Vision-Language Models","created":"2023-12-22","cited_by_count":32,"area":"mllm"},{"id":"2303.06854","title":"Robust Contrastive Language-Image Pre-training against Data Poisoning and Backdoor Attacks","created":"2023-03-13","cited_by_count":31,"area":"mllm"},{"id":"2404.10501","title":"Self-Supervised Visual Preference Alignment","created":"2024-04-16","cited_by_count":31,"area":"mllm"},{"id":"2305.14095","title":"S-CLIP: Semi-supervised Vision-Language Learning using Few Specialist Captions","created":"2023-05-23","cited_by_count":30,"area":"mllm"},{"id":"2403.07241","title":"Calibrating Multi-modal Representations: A Pursuit of Group Robustness without Annotations","created":"2024-03-12","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":"2210.04287","title":"Learning to Decompose Visual Features with Latent Textual Prompts","created":"2022-10-09","cited_by_count":28,"area":"mllm"},{"id":"2303.18232","title":"DIME-FM: DIstilling Multimodal and Efficient Foundation Models","created":"2023-03-31","cited_by_count":28,"area":"mllm"},{"id":"2503.15621","title":"LLaVA-MORE: A Comparative Study of LLMs and Visual Backbones for Enhanced Visual Instruction Tuning","created":"2025-03-19","cited_by_count":28,"area":"mllm"},{"id":"2301.02241","title":"CiT: Curation in Training for Effective Vision-Language Data","created":"2023-01-05","cited_by_count":27,"area":"mllm"},{"id":"2405.09771","title":"Harmonizing Generalization and Personalization in Federated Prompt Learning","created":"2024-05-16","cited_by_count":27,"area":"mllm"},{"id":"2411.00997","title":"Identifying Implicit Social Biases in Vision-Language Models","created":"2024-11-01","cited_by_count":27,"area":"mllm"},{"id":"2112.09331","title":"Contrastive Vision-Language Pre-training with Limited Resources","created":"2021-12-17","cited_by_count":25,"area":"mllm"},{"id":"2407.01067","title":"Human-like object concept representations emerge naturally in multimodal large language models","created":"2024-07-01","cited_by_count":25,"area":"mllm"},{"id":"2312.06224","title":"Medical Vision Language Pretraining: A survey","created":"2023-12-11","cited_by_count":24,"area":"mllm"},{"id":"2304.08587","title":"Grounding Classical Task Planners via Vision-Language Models","created":"2023-04-17","cited_by_count":23,"area":"mllm"},{"id":"2506.02020","title":"Improve Multi-Modal Embedding Learning via Explicit Hard Negative Gradient Amplifying","created":"2025-05-28","cited_by_count":23,"area":"mllm"},{"id":"2510.11693","title":"Scaling Language-Centric Omnimodal Representation Learning","created":"2025-10-13","cited_by_count":22,"area":"mllm"},{"id":"2312.11038","title":"UniChest: Conquer-and-Divide Pre-training for Multi-Source Chest X-Ray Classification","created":"2023-12-18","cited_by_count":21,"area":"mllm"},{"id":"2408.09441","title":"CLIP-CID: Efficient CLIP Distillation via Cluster-Instance Discrimination","created":"2024-08-18","cited_by_count":21,"area":"mllm"},{"id":"2505.05528","title":"X-Transfer Attacks: Towards Super Transferable Adversarial Attacks on CLIP","created":"2025-05-08","cited_by_count":21,"area":"mllm"},{"id":"2210.04135","title":"VoLTA: Vision-Language Transformer with Weakly-Supervised Local-Feature Alignment","created":"2022-10-09","cited_by_count":20,"area":"mllm"},{"id":"2305.08675","title":"Improved baselines for vision-language pre-training","created":"2023-05-15","cited_by_count":20,"area":"mllm"},{"id":"2312.02021","title":"Strong but simple: A Baseline for Domain Generalized Dense Perception by CLIP-based Transfer Learning","created":"2023-12-04","cited_by_count":20,"area":"mllm"},{"id":"2409.18991","title":"Surveying the MLLM Landscape: A Meta-Review of Current Surveys","created":"2024-09-17","cited_by_count":20,"area":"mllm"},{"id":"2505.20612","title":"Roboflow100-VL: A Multi-Domain Object Detection Benchmark for Vision-Language Models","created":"2025-05-27","cited_by_count":20,"area":"mllm"},{"id":"2203.09067","title":"UNIMO-2: End-to-End Unified Vision-Language Grounded Learning","created":"2022-03-17","cited_by_count":19,"area":"mllm"},{"id":"2312.01564","title":"APoLLo: Unified Adapter and Prompt Learning for Vision Language Models","created":"2023-12-04","cited_by_count":19,"area":"mllm"},{"id":"2409.19846","title":"Towards Open-Vocabulary Semantic Segmentation Without Semantic Labels","created":"2024-09-30","cited_by_count":19,"area":"mllm"},{"id":"2502.07617","title":"Scaling Pre-training to One Hundred Billion Data for Vision Language Models","created":"2025-02-11","cited_by_count":19,"area":"mllm"},{"id":"2112.07515","title":"CoCo-BERT: Improving Video-Language Pre-training with Contrastive Cross-modal Matching and Denoising","created":"2021-12-14","cited_by_count":18,"area":"mllm"},{"id":"2204.13867","title":"Vision-Language Pre-Training for Boosting Scene Text Detectors","created":"2022-04-29","cited_by_count":18,"area":"mllm"},{"id":"2403.07750","title":"Synth$^2$: Boosting Visual-Language Models with Synthetic Captions and Image Embeddings","created":"2024-03-12","cited_by_count":18,"area":"mllm"},{"id":"2405.04788","title":"SemiCD-VL: Visual-Language Model Guidance Makes Better Semi-supervised Change Detector","created":"2024-05-08","cited_by_count":18,"area":"mllm"},{"id":"2505.14361","title":"Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives","created":"2025-05-20","cited_by_count":18,"area":"mllm"},{"id":"2510.01711","title":"Contrastive Representation Regularization for Vision-Language-Action Models","created":"2025-10-02","cited_by_count":17,"area":"mllm"},{"id":"2306.10169","title":"Meta-Personalizing Vision-Language Models to Find Named Instances in Video","created":"2023-06-16","cited_by_count":16,"area":"mllm"},{"id":"2308.12636","title":"Exploring Transferability of Multimodal Adversarial Samples for Vision-Language Pre-training Models with Contrastive Learning","created":"2023-08-24","cited_by_count":16,"area":"mllm"},{"id":"2410.15364","title":"Scene Graph Generation with Role-Playing Large Language Models","created":"2024-10-20","cited_by_count":16,"area":"mllm"},{"id":"2303.17644","title":"Vision-Language Modelling For Radiological Imaging and Reports In The Low Data Regime","created":"2023-03-30","cited_by_count":15,"area":"mllm"},{"id":"2409.06809","title":"DetailCLIP: Detail-Oriented CLIP for Fine-Grained Tasks","created":"2024-09-10","cited_by_count":15,"area":"mllm"},{"id":"2409.07353","title":"Securing Vision-Language Models with a Robust Encoder Against Jailbreak and Adversarial Attacks","created":"2024-09-11","cited_by_count":15,"area":"mllm"},{"id":"2410.22187","title":"Active Learning for Vision-Language Models","created":"2024-10-29","cited_by_count":15,"area":"mllm"},{"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":"2505.17670","title":"Towards General Continuous Memory for Vision-Language Models","created":"2025-05-23","cited_by_count":15,"area":"mllm"},{"id":"2302.11084","title":"Test-Time Distribution Normalization for Contrastively Learned Vision-language Models","created":"2023-02-22","cited_by_count":14,"area":"mllm"}],"next_cursor":"100"}