Backpropagation-free Network for 3D Test-time Adaptation
Yanshuo Wang Ali Cheraghian Zeeshan Hayder Jie Hong Sameera Ramasinghe Shafin Rahman David Ahmedt-Aristizabal Xuesong Li Lars Petersson Mehrtash Harandi
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
https://github.com/abie-e/BFTT3D.