Test-time adaptation with pre-trained vision-language models has attracted
increasing attention for tackling distribution shifts during the test time.
Though prior studies have achieved very promising performance, they involve
intensive computation which is severely unaligned with test-time adaptation. We
design TDA, a training-free dynamic adapter that enables effective and
efficient test-time adaptation with vision-language models. TDA works with a
lightweight key-value cache that maintains a dynamic queue with few-shot pseudo
labels as values and the corresponding test-sample features as keys. Leveraging
the key-value cache, TDA allows adapting to test data gradually via progressive
pseudo label refinement which is super-efficient without incurring any
backpropagation. In addition, we introduce negative pseudo labeling that
alleviates the adverse impact of pseudo label noises by assigning pseudo labels
to certain negative classes when the model is uncertain about its pseudo label
predictions. Extensive experiments over two benchmarks demonstrate TDA's
superior effectiveness and efficiency as compared with the state-of-the-art.
The code has been released in https://kdiaaa.github.io/tda/.

TDA 是一种训练 - free 的动态适配器，通过轻量级的键值缓存和逐步的伪标签细化，以及引入负伪标签，实现了有效和高效的测试时自适应。

高效的视觉语言模型测试时间自适应

Efficient Test-Time Adaptation of Vision-Language Models

Point cloud analysis has achieved outstanding performance by transferring
point cloud pre-trained models. However, existing methods for model adaptation
usually update all model parameters, i.e., full fine-tuning paradigm, which is
inefficient as it relies on high computational costs (e.g., training GPU
memory) and massive storage space. In this paper, we aim to study
parameter-efficient transfer learning for point cloud analysis with an ideal
trade-off between task performance and parameter efficiency. To achieve this
goal, we freeze the parameters of the default pre-trained models and then
propose the Dynamic Adapter, which generates a dynamic scale for each token,
considering the token significance to the downstream task. We further
seamlessly integrate Dynamic Adapter with Prompt Tuning (DAPT) by constructing
Internal Prompts, capturing the instance-specific features for interaction.
Extensive experiments conducted on five challenging datasets demonstrate that
the proposed DAPT achieves superior performance compared to the full
fine-tuning counterparts while significantly reducing the trainable parameters
and training GPU memory by 95% and 35%, respectively. Code is available at
this https URL

本文的目标是研究点云分析中的参数高效迁移学习，以在任务性能和参数效率之间取得理想的平衡，通过冻结默认预训练模型的参数，并使用动态适配器和提示调整相结合的方法，捕捉每个标记的重要程度，要点功能以及实例特定特征的交互。通过在五个具有挑战性的数据集上进行的实验表明，所提出的方法在明显减少可训练参数和训练 GPU 存储空间（分别减少 95％和 35％) 的同时，实现了优于完全微调对应方法的性能。