Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the Road to Online Adaptation for Semantic Image Segmentation   We propose a new problem formulation and a corresponding evaluation framework
to advance research on unsupervised domain adaptation for semantic image
segmentation. The overall goal is fostering the development of adaptive
learning systems that will continuously learn, without supervision, in
ever-changing environments. Typical protocols that study adaptation algorithms
for segmentation models are limited to few domains, adaptation happens offline,
and human intervention is generally required, at least to annotate data for
hyper-parameter tuning. We argue that such constraints are incompatible with
algorithms that can continuously adapt to different real-world situations. To
address this, we propose a protocol where models need to learn online, from
sequences of temporally correlated images, requiring continuous, frame-by-frame
adaptation. We accompany this new protocol with a variety of baselines to
tackle the proposed formulation, as well as an extensive analysis of their
behaviors, which can serve as a starting point for future research.

12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an