{"total":46,"rows":[{"id":"2204.14198","title":"Flamingo: a Visual Language Model for Few-Shot Learning","created":"2022-04-29","cited_by_count":4386,"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":"2403.11549","title":"Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters","created":"2024-03-18","cited_by_count":199,"area":"mllm"},{"id":"2401.16822","title":"EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domain","created":"2024-01-30","cited_by_count":184,"area":"mllm"},{"id":"2405.18541","title":"Low-Rank Few-Shot Adaptation of Vision-Language Models","created":"2024-05-28","cited_by_count":104,"area":"mllm"},{"id":"2204.03574","title":"Learning to Compose Soft Prompts for Compositional Zero-Shot Learning","created":"2022-04-07","cited_by_count":67,"area":"mllm"},{"id":"2402.15896","title":"Multimodal Instruction Tuning with Conditional Mixture of LoRA","created":"2024-02-24","cited_by_count":36,"area":"mllm"},{"id":"2306.02080","title":"Benchmarking Robustness of Adaptation Methods on Pre-trained Vision-Language Models","created":"2023-06-03","cited_by_count":31,"area":"mllm"},{"id":"2303.15230","title":"Troika: Multi-Path Cross-Modal Traction for Compositional Zero-Shot Learning","created":"2023-03-27","cited_by_count":27,"area":"mllm"},{"id":"2303.11866","title":"Contrastive Alignment of Vision to Language Through Parameter-Efficient Transfer Learning","created":"2023-03-21","cited_by_count":16,"area":"mllm"},{"id":"2409.19696","title":"Vision-Language Models are Strong Noisy Label Detectors","created":"2024-09-29","cited_by_count":16,"area":"mllm"},{"id":"2305.07490","title":"ArtGPT-4: Towards Artistic-understanding Large Vision-Language Models with Enhanced Adapter","created":"2023-05-12","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":"2210.10841","title":"Prompting through Prototype: A Prototype-based Prompt Learning on Pretrained Vision-Language Models","created":"2022-10-19","cited_by_count":12,"area":"mllm"},{"id":"2408.02192","title":"Unsupervised Domain Adaption Harnessing Vision-Language Pre-training","created":"2024-08-05","cited_by_count":12,"area":"mllm"},{"id":"2409.19425","title":"Harnessing Frozen Unimodal Encoders for Flexible Multimodal Alignment","created":"2024-09-28","cited_by_count":12,"area":"mllm"},{"id":"2511.12917","title":"Explore How to Inject Beneficial Noise in MLLMs","created":"2025-11-17","cited_by_count":10,"area":"mllm"},{"id":"2407.03604","title":"Modality-Specialized Synergizers for Interleaved Vision-Language Generalists","created":"2024-07-04","cited_by_count":9,"area":"mllm"},{"id":"2212.02936","title":"M-VADER: A Model for Diffusion with Multimodal Context","created":"2022-12-06","cited_by_count":6,"area":"mllm"},{"id":"2303.01239","title":"MixPHM: Redundancy-Aware Parameter-Efficient Tuning for Low-Resource Visual Question Answering","created":"2023-03-02","cited_by_count":5,"area":"mllm"},{"id":"2309.01479","title":"Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained Models","created":"2023-09-04","cited_by_count":5,"area":"mllm"},{"id":"2308.01532","title":"MA-FSAR: Multimodal Adaptation of CLIP for Few-Shot Action Recognition","created":"2023-08-03","cited_by_count":4,"area":"mllm"},{"id":"2411.11912","title":"F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics","created":"2024-11-17","cited_by_count":4,"area":"mllm"},{"id":"2511.06225","title":"MoRA: Missing Modality Low-Rank Adaptation for Visual Recognition","created":"2025-11-09","cited_by_count":4,"area":"mllm"},{"id":"2407.08787","title":"Data Adaptive Traceback for Vision-Language Foundation Models in Image Classification","created":"2024-07-11","cited_by_count":3,"area":"mllm"},{"id":"2509.03740","title":"CLIP-SVD: Efficient and Interpretable Vision-Language Adaptation via Singular Values","created":"2025-09-03","cited_by_count":3,"area":"mllm"},{"id":"2510.24942","title":"Finding Culture-Sensitive Neurons in Vision-Language Models","created":"2025-10-28","cited_by_count":3,"area":"mllm"},{"id":"2512.13855","title":"Improvise, Adapt, Overcome -- Telescopic Adapters for Efficient Fine-tuning of Vision Language Models in Medical Imaging","created":"2025-12-15","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":"2312.12458","title":"When Parameter-efficient Tuning Meets General-purpose Vision-language Models","created":"2023-12-16","cited_by_count":1,"area":"mllm"},{"id":"2407.15731","title":"The Inter-Intra Modal Measure: A Predictive Lens on Fine-Tuning Outcomes in Vision-Language Models","created":"2024-07-22","cited_by_count":1,"area":"mllm"},{"id":"2410.05270","title":"CLIP's Visual Embedding Projector is a Few-shot Cornucopia","created":"2024-10-07","cited_by_count":1,"area":"mllm"},{"id":"2504.12436","title":"Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation","created":"2025-04-16","cited_by_count":1,"area":"mllm"},{"id":"2508.10171","title":"SynSpill: Improved Industrial Spill Detection With Synthetic Data","created":"2025-08-13","cited_by_count":1,"area":"mllm"},{"id":"2602.20409","title":"CLIPoint3D: Language-Grounded Few-Shot Unsupervised 3D Point Cloud Domain Adaptation","created":"2026-02-23","cited_by_count":1,"area":"mllm"},{"id":"2606.15821","title":"The Truth Stays in the Family: Enhancing Contextual Grounding via Inherited Truthful Heads in Model Lineages","created":"2026-06-14","cited_by_count":1,"area":"mllm"},{"id":"2409.16136","title":"HA-FGOVD: Highlighting Fine-grained Attributes via Explicit Linear Composition for Open-Vocabulary Object Detection","created":"2024-09-24","cited_by_count":0,"area":"mllm"},{"id":"2503.06003","title":"Integrating Frequency-Domain Representations with Low-Rank Adaptation in Vision-Language Models","created":"2025-03-08","cited_by_count":0,"area":"mllm"},{"id":"2509.00305","title":"Language-Aware Information Maximization for Transductive Few-Shot CLIP","created":"2025-08-30","cited_by_count":0,"area":"mllm"},{"id":"2512.04305","title":"How (Mis)calibrated is your Federated CLIP and what to do about it?","created":"2025-12-03","cited_by_count":0,"area":"mllm"},{"id":"2512.19219","title":"Selective LoRA for Visual Tokens and Attention Heads","created":"2025-12-22","cited_by_count":0,"area":"mllm"},{"id":"2603.21077","title":"CoVFT: Context-aware Visual Fine-tuning for Multimodal Large Language Models","created":"2026-03-22","cited_by_count":0,"area":"mllm"},{"id":"2604.15703","title":"P3T: Prototypical Point-level Prompt Tuning with Enhanced Generalization for 3D Vision-Language Models","created":"2026-04-17","cited_by_count":0,"area":"mllm"},{"id":"2604.17629","title":"BioVLM: Routing Prompts, Not Parameters, for Cross-Modality Generalization in Biomedical VLMs","created":"2026-04-19","cited_by_count":0,"area":"mllm"},{"id":"2605.25479","title":"MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models","created":"2026-05-25","cited_by_count":0,"area":"mllm"},{"id":"2610.14976","title":"Metric-Driven Reinforcement Learning for Object Hallucination Reduction in LVLM-Based Image Captioning","created":"2026-10-10","cited_by_count":0,"area":"mllm"}],"next_cursor":null}