{"total":31,"rows":[{"id":"2304.00685","title":"Vision-Language Models for Vision Tasks: A Survey","created":"2023-04-03","cited_by_count":839,"area":"mllm"},{"id":"2211.16198","title":"SuS-X: Training-Free Name-Only Transfer of Vision-Language Models","created":"2022-11-28","cited_by_count":119,"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":"2303.17561","title":"SoftCLIP: Softer Cross-modal Alignment Makes CLIP Stronger","created":"2023-03-30","cited_by_count":56,"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":"2310.07355","title":"IMITATE: Clinical Prior Guided Hierarchical Vision-Language Pre-training","created":"2023-10-11","cited_by_count":23,"area":"mllm"},{"id":"2504.01916","title":"FineLIP: Extending CLIP's Reach via Fine-Grained Alignment with Longer Text Inputs","created":"2025-04-02","cited_by_count":23,"area":"mllm"},{"id":"2305.08675","title":"Improved baselines for vision-language pre-training","created":"2023-05-15","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":"2412.01814","title":"COSMOS: Cross-Modality Self-Distillation for Vision Language Pre-training","created":"2024-12-02","cited_by_count":15,"area":"mllm"},{"id":"2407.14412","title":"DEAL: Disentangle and Localize Concept-level Explanations for VLMs","created":"2024-07-19","cited_by_count":14,"area":"mllm"},{"id":"2312.01522","title":"G2D: From Global to Dense Radiography Representation Learning via Vision-Language Pre-training","created":"2023-12-03","cited_by_count":12,"area":"mllm"},{"id":"2405.15668","title":"What Do You See? Enhancing Zero-Shot Image Classification with Multimodal Large Language Models","created":"2024-05-24","cited_by_count":10,"area":"mllm"},{"id":"2403.18260","title":"Toward Interactive Regional Understanding in Vision-Large Language Models","created":"2024-03-27","cited_by_count":9,"area":"mllm"},{"id":"2404.09387","title":"RankCLIP: Ranking-Consistent Language-Image Pretraining","created":"2024-04-15","cited_by_count":9,"area":"mllm"},{"id":"2312.17345","title":"3VL: Using Trees to Improve Vision-Language Models' Interpretability","created":"2023-12-28","cited_by_count":7,"area":"mllm"},{"id":"2407.14117","title":"Rethinking Visual Content Refinement in Low-Shot CLIP Adaptation","created":"2024-07-19","cited_by_count":5,"area":"mllm"},{"id":"2312.00674","title":"LightCLIP: Learning Multi-Level Interaction for Lightweight Vision-Language Models","created":"2023-12-01","cited_by_count":3,"area":"mllm"},{"id":"2502.00618","title":"DesCLIP: Robust Continual Learning via General Attribute Descriptions for VLM-Based Visual Recognition","created":"2025-02-02","cited_by_count":2,"area":"mllm"},{"id":"2503.06626","title":"DiffCLIP: Differential Attention Meets CLIP","created":"2025-03-09","cited_by_count":2,"area":"mllm"},{"id":"2306.00813","title":"UniDiff: Advancing Vision-Language Models with Generative and Discriminative Learning","created":"2023-06-01","cited_by_count":1,"area":"mllm"},{"id":"2409.08206","title":"ComAlign: Compositional Alignment in Vision-Language Models","created":"2024-09-12","cited_by_count":1,"area":"mllm"},{"id":"2505.07675","title":"Simple yet Effective Semi-supervised Knowledge Distillation from Vision-Language Models via Dual-Head Optimization","created":"2025-05-12","cited_by_count":1,"area":"mllm"},{"id":"2506.09691","title":"Adding simple structure at inference improves Vision-Language Compositionality","created":"2025-06-11","cited_by_count":1,"area":"mllm"},{"id":"2506.23502","title":"LLM-enhanced Action-aware Multi-modal Prompt Tuning for Image-Text Matching","created":"2025-06-30","cited_by_count":1,"area":"mllm"},{"id":"2507.03434","title":"Unlearning the Noisy Correspondence Makes CLIP More Robust","created":"2025-07-04","cited_by_count":1,"area":"mllm"},{"id":"2508.04136","title":"UniFGVC: Universal Training-Free Few-Shot Fine-Grained Vision Classification via Attribute-Aware Multimodal Retrieval","created":"2025-08-06","cited_by_count":1,"area":"mllm"},{"id":"2604.11496","title":"Revisiting Compositionality in Dual-Encoder Vision-Language Models: The Role of Inference","created":"2026-04-13","cited_by_count":1,"area":"mllm"},{"id":"2406.11252","title":"Mining Open Semantics from CLIP: A Relation Transition Perspective for Few-Shot Learning","created":"2024-06-17","cited_by_count":0,"area":"mllm"},{"id":"2512.02566","title":"From Panel to Pixel: Zoom-In Vision-Language Pretraining from Biomedical Scientific Literature","created":"2025-12-02","cited_by_count":0,"area":"mllm"},{"id":"2601.13622","title":"CARPE: Context-Aware Image Representation Prioritization via Ensemble for Large Vision-Language Models","created":"2026-01-20","cited_by_count":0,"area":"mllm"}],"next_cursor":null}