Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Instance-Specific Adaptation for Cross-Domain Segmentation   We propose a test-time adaptation method for cross-domain image segmentation.
Our method is simple: Given a new unseen instance at test time, we adapt a
pre-trained model by conducting instance-specific BatchNorm (statistics)
calibration. Our approach has two core components. First, we replace the
manually designed BatchNorm calibration rule with a learnable module. Second,
we leverage strong data augmentation to simulate random domain shifts for
learning the calibration rule. In contrast to existing domain adaptation
methods, our method does not require accessing the target domain data at
training time or conducting computationally expensive test-time model
training/optimization. Equipping our method with models trained by standard
recipes achieves significant improvement, comparing favorably with several
state-of-the-art domain generalization and one-shot unsupervised domain
adaptation approaches. Combining our method with the domain generalization
methods further improves performance, reaching a new state of the art.

20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
2
The categories of this paper are: math
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th