Real-world systems often encounter new data over time, which leads to
experiencing target domain shifts. Existing Test-Time Adaptation (TTA) methods
tend to apply computationally heavy and memory-intensive backpropagation-based
approaches to handle this. Here, we propose a novel method that uses a
backpropagation-free approach for TTA for the specific case of 3D data. Our
model uses a two-stream architecture to maintain knowledge about the source
domain as well as complementary target-domain-specific information. The
backpropagation-free property of our model helps address the well-known
forgetting problem and mitigates the error accumulation issue. The proposed
method also eliminates the need for the usually noisy process of
pseudo-labeling and reliance on costly self-supervised training. Moreover, our
method leverages subspace learning, effectively reducing the distribution
variance between the two domains. Furthermore, the source-domain-specific and
the target-domain-specific streams are aligned using a novel entropy-based
adaptive fusion strategy. Extensive experiments on popular benchmarks
demonstrate the effectiveness of our method. The code will be available at
this https URL

我们提出了一种新颖的方法，使用无反向传播法来处理 3D 数据的测试时适应问题，采用两流架构来维护源领域和目标领域的知识，并通过子空间学习减少两个领域之间的分布差异，使用基于熵的自适应融合策略对源领域和目标领域进行对齐。在流行的基准测试上进行了大量实验，证明了我们方法的有效性。