{"total":32,"rows":[{"id":"2403.14608","title":"Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey","created":"2024-03-21","cited_by_count":656,"area":null},{"id":"2403.01439","title":"Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud Analysis","created":"2024-03-03","cited_by_count":53,"area":null},{"id":"2410.19878","title":"Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies","created":"2024-10-24","cited_by_count":47,"area":null},{"id":"2410.08114","title":"Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning","created":"2024-10-10","cited_by_count":43,"area":null},{"id":"2411.01327","title":"Visual Fourier Prompt Tuning","created":"2024-11-02","cited_by_count":30,"area":null},{"id":"2405.05615","title":"Memory-Space Visual Prompting for Efficient Vision-Language Fine-Tuning","created":"2024-05-09","cited_by_count":20,"area":null},{"id":"2404.17245","title":"Parameter Efficient Fine-tuning of Self-supervised ViTs without Catastrophic Forgetting","created":"2024-04-26","cited_by_count":17,"area":null},{"id":"2406.03051","title":"Adapter-X: A Novel General Parameter-Efficient Fine-Tuning Framework for Vision","created":"2024-06-05","cited_by_count":13,"area":null},{"id":"2407.09946","title":"Low-Rank Interconnected Adaptation across Layers","created":"2024-07-13","cited_by_count":10,"area":null},{"id":"2510.16670","title":"All You Need is One: Capsule Prompt Tuning with a Single Vector","created":"2025-10-19","cited_by_count":8,"area":null},{"id":"2503.18368","title":"MoST: Efficient Monarch Sparse Tuning for 3D Representation Learning","created":"2025-03-24","cited_by_count":6,"area":null},{"id":"2404.05207","title":"iVPT: Improving Task-relevant Information Sharing in Visual Prompt Tuning by Cross-layer Dynamic Connection","created":"2024-04-08","cited_by_count":4,"area":null},{"id":"2407.06964","title":"Parameter-Efficient and Memory-Efficient Tuning for Vision Transformer: A Disentangled Approach","created":"2024-07-09","cited_by_count":4,"area":null},{"id":"2402.17406","title":"LSPT: Long-term Spatial Prompt Tuning for Visual Representation Learning","created":"2024-02-27","cited_by_count":3,"area":null},{"id":"2404.11475","title":"AdaIR: Exploiting Underlying Similarities of Image Restoration Tasks with Adapters","created":"2024-04-17","cited_by_count":3,"area":null},{"id":"2503.20748","title":"UniSTD: Towards Unified Spatio-Temporal Learning across Diverse Disciplines","created":"2025-03-26","cited_by_count":3,"area":null},{"id":"2607.29048","title":"Parameter-Efficient Fine-Tuning for Spiking Point Cloud Models","created":"2026-07-31","cited_by_count":3,"area":null},{"id":"2404.08894","title":"Rethinking Low-Rank Adaptation in Vision: Exploring Head-Level Responsiveness across Diverse Tasks","created":"2024-04-13","cited_by_count":2,"area":null},{"id":"2412.16654","title":"IV-tuning: Parameter-Efficient Transfer Learning for Infrared-Visible Tasks","created":"2024-12-21","cited_by_count":1,"area":null},{"id":"2412.16956","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","created":"2024-12-22","cited_by_count":1,"area":null},{"id":"2601.08499","title":"EfficientFSL: Enhancing Few-Shot Classification via Query-Only Tuning in Vision Transformers","created":"2026-01-13","cited_by_count":1,"area":null},{"id":"2602.22938","title":"pMoE: Prompting Diverse Experts Together Wins More in Visual Adaptation","created":"2026-02-26","cited_by_count":1,"area":null},{"id":"2603.10160","title":"ReMix: Reinforcement routing for mixtures of LoRAs in LLM finetuning","created":"2026-03-10","cited_by_count":1,"area":null},{"id":"2409.19733","title":"Pear: Pruning and Sharing Adapters in Visual Parameter-Efficient Fine-Tuning","created":"2024-09-29","cited_by_count":0,"area":null},{"id":"2412.16459","title":"Rethinking Model Redundancy for Low-light Image Enhancement","created":"2024-12-21","cited_by_count":0,"area":null},{"id":"2505.21895","title":"SineLoRA$\\Delta$: Sine-Activated Delta Compression","created":"2025-05-28","cited_by_count":0,"area":null},{"id":"2509.04398","title":"IPA: An Information-Reconstructive Input Projection Framework for Efficient Foundation Model Adaptation","created":"2025-09-04","cited_by_count":0,"area":null},{"id":"2512.24603","title":"Collaborative Low-Rank Adaptation for Pre-Trained Vision Transformers","created":"2025-12-31","cited_by_count":0,"area":null},{"id":"2604.08368","title":"SOLAR: Communication-Efficient Model Adaptation via Subspace-Oriented Latent Adapter Reparametrization","created":"2026-04-09","cited_by_count":0,"area":null},{"id":"2609.18056","title":"Position Anchor Tuning: Towards Efficient Adaptation of Pre-Trained Point Cloud Transformers","created":"2026-09-16","cited_by_count":0,"area":null},{"id":"2609.19716","title":"GAPrompt++: Multi-Granular Geometry-Aware Point Cloud Prompt for 3D Vision Model","created":"2026-09-17","cited_by_count":0,"area":null},{"id":"2609.20340","title":"FreqDINO++: A Frequency-Guided Multi-Task Routing Vision Foundation Model for Universal Ultrasound Analysis","created":"2026-09-17","cited_by_count":0,"area":null}],"next_cursor":null}