{"total":97,"rows":[{"id":"2303.15361","title":"A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts","created":"2023-03-27","cited_by_count":359,"area":null},{"id":"2303.13899","title":"Robust Test-Time Adaptation in Dynamic Scenarios","created":"2023-03-24","cited_by_count":189,"area":null},{"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":"2302.03024","title":"AIM: Adapting Image Models for Efficient Video Action Recognition","created":"2023-02-06","cited_by_count":148,"area":null},{"id":"2306.13090","title":"PromptIR: Prompting for All-in-One Blind Image Restoration","created":"2023-06-22","cited_by_count":114,"area":null},{"id":"2308.09710","title":"SimDA: Simple Diffusion Adapter for Efficient Video Generation","created":"2023-08-18","cited_by_count":101,"area":null},{"id":"2211.11635","title":"Understanding and Improving Visual Prompting: A Label-Mapping Perspective","created":"2022-11-21","cited_by_count":87,"area":null},{"id":"2307.13770","title":"E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning","created":"2023-07-25","cited_by_count":70,"area":null},{"id":"2411.03687","title":"Beyond Model Adaptation at Test Time: A Survey","created":"2024-11-06","cited_by_count":59,"area":null},{"id":"2306.04344","title":"ViDA: Homeostatic Visual Domain Adapter for Continual Test Time Adaptation","created":"2023-06-07","cited_by_count":58,"area":null},{"id":"2310.20199","title":"In Search of Lost Online Test-time Adaptation: A Survey","created":"2023-10-31","cited_by_count":44,"area":null},{"id":"2305.06061","title":"Visual Tuning","created":"2023-05-10","cited_by_count":34,"area":null},{"id":"2406.10737","title":"DPCore: Dynamic Prompt Coreset for Continual Test-Time Adaptation","created":"2024-06-15","cited_by_count":33,"area":null},{"id":"2312.03502","title":"Improving the Generalization of Segmentation Foundation Model under Distribution Shift via Weakly Supervised Adaptation","created":"2023-12-06","cited_by_count":31,"area":null},{"id":"2401.12902","title":"Facing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning?","created":"2024-01-23","cited_by_count":31,"area":null},{"id":"2306.01669","title":"Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt Tuning","created":"2023-06-02","cited_by_count":30,"area":"mllm"},{"id":"2311.18363","title":"Each Test Image Deserves A Specific Prompt: Continual Test-Time Adaptation for 2D Medical Image Segmentation","created":"2023-11-30","cited_by_count":29,"area":null},{"id":"2303.09792","title":"Exploring Sparse Visual Prompt for Domain Adaptive Dense Prediction","created":"2023-03-17","cited_by_count":28,"area":null},{"id":"2309.13847","title":"Tuning Multi-mode Token-level Prompt Alignment across Modalities","created":"2023-09-25","cited_by_count":26,"area":"mllm"},{"id":"2402.08712","title":"BECoTTA: Input-dependent Online Blending of Experts for Continual Test-time Adaptation","created":"2024-02-13","cited_by_count":25,"area":null},{"id":"2409.13401","title":"PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Images","created":"2024-09-20","cited_by_count":24,"area":null},{"id":"2402.02242","title":"Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey and Benchmark","created":"2024-02-03","cited_by_count":23,"area":null},{"id":"2405.02797","title":"Adapting to Distribution Shift by Visual Domain Prompt Generation","created":"2024-05-05","cited_by_count":23,"area":null},{"id":"2510.13219","title":"Prompt-based Adaptation in Large-scale Vision Models: A Survey","created":"2025-10-15","cited_by_count":21,"area":null},{"id":"2506.13750","title":"Test3R: Learning to Reconstruct 3D at Test Time","created":"2025-06-16","cited_by_count":20,"area":null},{"id":"2407.09498","title":"OT-VP: Optimal Transport-guided Visual Prompting for Test-Time Adaptation","created":"2024-06-12","cited_by_count":17,"area":null},{"id":"2505.23694","title":"DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers","created":"2025-05-29","cited_by_count":17,"area":null},{"id":"2406.10973","title":"ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts","created":"2024-06-16","cited_by_count":16,"area":null},{"id":"2302.08102","title":"Prompt Tuning of Deep Neural Networks for Speaker-adaptive Visual Speech Recognition","created":"2023-02-16","cited_by_count":14,"area":null},{"id":"2303.08557","title":"Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey","created":"2023-03-15","cited_by_count":14,"area":null},{"id":"2303.12267","title":"AUTO: Adaptive Outlier Optimization for Test-Time OOD Detection","created":"2023-03-22","cited_by_count":13,"area":null},{"id":"2309.06799","title":"When Geoscience Meets Foundation Models: Towards General Geoscience Artificial Intelligence System","created":"2023-09-13","cited_by_count":13,"area":null},{"id":"2403.17369","title":"CoDA: Instructive Chain-of-Domain Adaptation with Severity-Aware Visual Prompt Tuning","created":"2024-03-26","cited_by_count":13,"area":null},{"id":"2301.12576","title":"Uncovering Adversarial Risks of Test-Time Adaptation","created":"2023-01-29","cited_by_count":12,"area":null},{"id":"2307.12980","title":"A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models","created":"2023-07-24","cited_by_count":12,"area":"mllm"},{"id":"2307.09787","title":"DVPT: Dynamic Visual Prompt Tuning of Large Pre-trained Models for Medical Image Analysis","created":"2023-07-19","cited_by_count":11,"area":null},{"id":"2309.15251","title":"VPA: Fully Test-Time Visual Prompt Adaptation","created":"2023-09-26","cited_by_count":11,"area":null},{"id":"2404.04452","title":"Vision transformers in domain adaptation and domain generalization: a study of robustness","created":"2024-04-05","cited_by_count":11,"area":null},{"id":"2309.13604","title":"Distribution-Aware Continual Test-Time Adaptation for Semantic Segmentation","created":"2023-09-24","cited_by_count":10,"area":null},{"id":"2403.05856","title":"POV: Prompt-Oriented View-Agnostic Learning for Egocentric Hand-Object Interaction in the Multi-View World","created":"2024-03-09","cited_by_count":10,"area":null},{"id":"2412.09895","title":"Building a Multi-modal Spatiotemporal Expert for Zero-shot Action Recognition with CLIP","created":"2024-12-13","cited_by_count":9,"area":null},{"id":"2505.14511","title":"ReservoirTTA: Prolonged Test-time Adaptation for Evolving and Recurring Domains","created":"2025-05-20","cited_by_count":9,"area":null},{"id":"2210.15996","title":"Towards Generalized Few-Shot Open-Set Object Detection","created":"2022-10-28","cited_by_count":8,"area":null},{"id":"2507.09183","title":"Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning","created":"2025-07-12","cited_by_count":8,"area":null},{"id":"2406.19341","title":"Learning Visual Conditioning Tokens to Correct Domain Shift for Fully Test-time Adaptation","created":"2024-06-27","cited_by_count":7,"area":null},{"id":"2504.08010","title":"Self-Bootstrapping for Versatile Test-Time Adaptation","created":"2025-04-10","cited_by_count":7,"area":null},{"id":"2306.10494","title":"Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction:A Multi-Dataset Study","created":"2023-06-18","cited_by_count":6,"area":null},{"id":"2405.20141","title":"OpenDAS: Open-Vocabulary Domain Adaptation for 2D and 3D Segmentation","created":"2024-05-30","cited_by_count":6,"area":"mllm"},{"id":"2503.18695","title":"OCRT: Boosting Foundation Models in the Open World with Object-Concept-Relation Triad","created":"2025-03-24","cited_by_count":6,"area":null},{"id":"2310.05341","title":"From Question to Exploration: Test-Time Adaptation in Semantic Segmentation?","created":"2023-10-09","cited_by_count":5,"area":null},{"id":"2403.10568","title":"MoPE: Mixture of Prompt Experts for Parameter-Efficient and Scalable Multimodal Fusion","created":"2024-03-14","cited_by_count":5,"area":null},{"id":"2409.08566","title":"Hybrid-TTA: Continual Test-time Adaptation via Dynamic Domain Shift Detection","created":"2024-09-13","cited_by_count":5,"area":null},{"id":"2303.04998","title":"Rethinking Visual Prompt Learning as Masked Visual Token Modeling","created":"2023-03-09","cited_by_count":4,"area":null},{"id":"2310.07632","title":"Prompt Backdoors in Visual Prompt Learning","created":"2023-10-11","cited_by_count":4,"area":"safety"},{"id":"2312.04066","title":"Combining inherent knowledge of vision-language models with unsupervised domain adaptation through strong-weak guidance","created":"2023-12-07","cited_by_count":4,"area":"mllm"},{"id":"2405.15161","title":"Are You Copying My Prompt? Protecting the Copyright of Vision Prompt for VPaaS via Watermark","created":"2024-05-24","cited_by_count":4,"area":null},{"id":"2408.13983","title":"Dual-Path Adversarial Lifting for Domain Shift Correction in Online Test-time Adaptation","created":"2024-08-26","cited_by_count":4,"area":null},{"id":"2408.16310","title":"Bootstrap Segmentation Foundation Model under Distribution Shift via Object-Centric Learning","created":"2024-08-29","cited_by_count":4,"area":"synth"},{"id":"2410.04682","title":"On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoning","created":"2024-10-07","cited_by_count":4,"area":"safety"},{"id":"2504.09990","title":"Correlative and Discriminative Label Grouping for Multi-Label Visual Prompt Tuning","created":"2025-04-14","cited_by_count":4,"area":"moe"},{"id":"2505.09927","title":"DDFP: Data-dependent Frequency Prompt for Source Free Domain Adaptation of Medical Image Segmentation","created":"2025-05-15","cited_by_count":4,"area":null},{"id":"2303.13898","title":"Remind of the Past: Incremental Learning with Analogical Prompts","created":"2023-03-24","cited_by_count":3,"area":null},{"id":"2307.13897","title":"AViT: Adapting Vision Transformers for Small Skin Lesion Segmentation Datasets","created":"2023-07-26","cited_by_count":3,"area":null},{"id":"2308.15273","title":"Cross-Modal Retrieval Meets Inference:Improving Zero-Shot Classification with Cross-Modal Retrieval","created":"2023-08-29","cited_by_count":3,"area":null},{"id":"2310.04714","title":"Generalized Robust Test-Time Adaptation in Continuous Dynamic Scenarios","created":"2023-10-07","cited_by_count":3,"area":null},{"id":"2311.07877","title":"Test-Time Training for Semantic Segmentation with Output Contrastive Loss","created":"2023-11-14","cited_by_count":3,"area":null},{"id":"2312.01835","title":"Few Clicks Suffice: Active Test-Time Adaptation for Semantic Segmentation","created":"2023-12-04","cited_by_count":3,"area":null},{"id":"2504.04982","title":"Transforming Future Data Center Operations and Management via Physical AI","created":"2025-04-07","cited_by_count":3,"area":null},{"id":"2510.12150","title":"Class-aware Domain Knowledge Fusion and Fission for Continual Test-Time Adaptation","created":"2025-10-14","cited_by_count":3,"area":null},{"id":"2511.09809","title":"Test-Time Spectrum-Aware Latent Steering for Zero-Shot Generalization in Vision-Language Models","created":"2025-11-12","cited_by_count":3,"area":"mllm"},{"id":"2307.00313","title":"PM-DETR: Domain Adaptive Prompt Memory for Object Detection with Transformers","created":"2023-07-01","cited_by_count":2,"area":null},{"id":"2403.17423","title":"Test-time Adaptation Meets Image Enhancement: Improving Accuracy via Uncertainty-aware Logit Switching","created":"2024-03-26","cited_by_count":2,"area":null},{"id":"2410.10442","title":"Domain-Conditioned Transformer for Fully Test-time Adaptation","created":"2024-10-14","cited_by_count":2,"area":null},{"id":"2412.09706","title":"Diffusion-Enhanced Test-time Adaptation with Text and Image Augmentation","created":"2024-12-12","cited_by_count":2,"area":null},{"id":"2412.09899","title":"TTAQ: Towards Stable Post-training Quantization in Continuous Domain Adaptation","created":"2024-12-13","cited_by_count":2,"area":null},{"id":"2503.06084","title":"Exploring Interpretability for Visual Prompt Tuning with Cross-layer Concepts","created":"2025-03-08","cited_by_count":2,"area":null},{"id":"2506.01247","title":"Beyond Interpretability: When, Why, and How Sparse Autoencoders Enable Label-Free Visual Steering","created":"2025-06-02","cited_by_count":2,"area":"mechinterp"},{"id":"2506.02453","title":"PAID: Pairwise Angular-Invariant Decomposition for Continual Test-Time Adaptation","created":"2025-06-03","cited_by_count":2,"area":null},{"id":"2603.26486","title":"ClipTTT: CLIP-Guided Test-Time Training Helps LVLMs See Better","created":"2026-03-27","cited_by_count":2,"area":"mllm"},{"id":"2409.09753","title":"DARDA: Domain-Aware Real-Time Dynamic Neural Network Adaptation","created":"2024-09-15","cited_by_count":1,"area":null},{"id":"2504.06220","title":"Earth-Adapter: Bridge the Geospatial Domain Gaps with Mixture of Frequency Adaptation","created":"2025-04-08","cited_by_count":1,"area":null},{"id":"2505.12912","title":"Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption","created":"2025-05-19","cited_by_count":1,"area":"mllm"},{"id":"2506.19022","title":"Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation","created":"2025-06-23","cited_by_count":1,"area":null},{"id":"2509.15225","title":"Lost in Translation? Vocabulary Alignment for Source-Free Adaptation in Open-Vocabulary Semantic Segmentation","created":"2025-09-18","cited_by_count":1,"area":null},{"id":"2509.17598","title":"COLA: Context-aware Language-driven Test-time Adaptation","created":"2025-09-22","cited_by_count":1,"area":null},{"id":"2510.01660","title":"VirDA: Reusing Backbone for Unsupervised Domain Adaptation with Visual Reprogramming","created":"2025-10-02","cited_by_count":1,"area":null},{"id":"2511.21606","title":"ReSAM: Refine, Requery, and Reinforce: Self-Prompting Point-Supervised Segmentation for Remote Sensing Images","created":"2025-11-26","cited_by_count":1,"area":null},{"id":"2512.08569","title":"Instance-Aware Test-Time Segmentation for Continual Domain Shifts","created":"2025-12-09","cited_by_count":1,"area":null},{"id":"2309.09196","title":"Efficient Pyramid Channel Attention Network for Pathological Myopia Recognition","created":"2023-09-17","cited_by_count":0,"area":null},{"id":"2312.03734","title":"Conditional Prompt Tuning for Multimodal Fusion","created":"2023-11-28","cited_by_count":0,"area":null},{"id":"2401.04130","title":"Plug-and-Play Transformer Modules for Test-Time Adaptation","created":"2024-01-06","cited_by_count":0,"area":null},{"id":"2507.02437","title":"F^2TTA: Free-Form Test-Time Adaptation on Cross-Domain Medical Image Classification via Image-Level Disentangled Prompt Tuning","created":"2025-07-03","cited_by_count":0,"area":null},{"id":"2511.20302","title":"CrossEarth-Gate: Fisher-Guided Adaptive Tuning Engine for Efficient Adaptation of Cross-Domain Remote Sensing Semantic Segmentation","created":"2025-11-25","cited_by_count":0,"area":null},{"id":"2512.12673","title":"Progressive Conditioned Scale-Shift Recalibration of Self-Attention for Online Test-time Adaptation","created":"2025-12-14","cited_by_count":0,"area":null},{"id":"2603.07926","title":"IMSE: Intrinsic Mixture of Spectral Experts Fine-tuning for Test-Time Adaptation","created":"2026-03-09","cited_by_count":0,"area":null},{"id":"2606.19944","title":"Timage: A Generative Text-in-Image Paradigm for Fine-Tuning Vision-Language Models","created":"2026-06-18","cited_by_count":0,"area":"lr"},{"id":"2609.13654","title":"Multimodal Foundation Models Adaptation based on Domain-Aware Relaxed Orthogonal Subspace for Remote Sensing","created":"2026-09-12","cited_by_count":0,"area":null}],"next_cursor":null}