Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SITA: Single Image Test-time Adaptation   In Test-time Adaptation (TTA), given a model trained on some source data, the
goal is to adapt it to make better predictions for test instances from a
different distribution. Crucially, TTA assumes no access to the source data or
even any additional labeled/unlabeled samples from the target distribution to
finetune the source model. In this work, we consider TTA in a more pragmatic
setting which we refer to as SITA (Single Image Test-time Adaptation). Here,
when making each prediction, the model has access only to the given single test
instance, rather than a batch of instances, as has typically been considered in
the literature. This is motivated by the realistic scenarios where inference is
needed in an on-demand fashion that may not be delayed to "batch-ify" incoming
requests or the inference is happening on an edge device (like mobile phone)
where there is no scope for batching. The entire adaptation process in SITA
should be extremely fast as it happens at inference time. To address this, we
propose a novel approach AugBN for the SITA setting that requires only forward
propagation. The approach can adapt any off-the-shelf trained model to
individual test instances for both classification and segmentation tasks. AugBN
estimates normalisation statistics of the unseen test distribution from the
given test image using only one forward pass with label-preserving
transformations. Since AugBN does not involve any back-propagation, it is
significantly faster compared to other recent methods. To the best of our
knowledge, this is the first work that addresses this hard adaptation problem
using only a single test image. Despite being very simple, our framework is
able to achieve significant performance gains compared to directly applying the
source model on the target instances, as reflected in our extensive experiments
and ablation studies.

5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
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The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: astro-ph
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The categories of this paper are: adap-org
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The categories of this paper are: physics
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The categories of this paper are: funct-an
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The categories of this paper are: alg-geom
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The categories of this paper are: chem-ph
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The categories of this paper are: stat