Technical Report for ICCV 2023 Visual Continual Learning Challenge:
  Continuous Test-time Adaptation for Semantic Segmentation
Damian Sójka Yuyang Liu Dipam Goswami Sebastian Cygert Bartłomiej Twardowski Joost van de Weijer
The goal of the challenge is to develop a test-time adaptation (TTA) method,
which could adapt the model to gradually changing domains in video sequences
for semantic segmentation task. It is based on a synthetic driving video
dataset - SHIFT. The source model is trained on images taken during daytime in
clear weather. Domain changes at test-time are mainly caused by varying weather
conditions and times of day. The TTA methods are evaluated in each image
sequence (video) separately, meaning the model is reset to the source model
state before the next sequence. Images come one by one and a prediction has to
be made at the arrival of each frame. Each sequence is composed of 401 images
and starts with the source domain, then gradually drifts to a different one
(changing weather or time of day) until the middle of the sequence. In the
second half of the sequence, the domain gradually shifts back to the source
one. Ground truth data is available only for the validation split of the SHIFT
dataset, in which there are only six sequences that start and end with the
source domain. We conduct an analysis specifically on those sequences. Ground
truth data for test split, on which the developed TTA methods are evaluated for
leader board ranking, are not publicly available.
  The proposed solution secured a 3rd place in a challenge and received an
innovation award. Contrary to the solutions that scored better, we did not use
any external pretrained models or specialized data augmentations, to keep the
solutions as general as possible. We have focused on analyzing the
distributional shift and developing a method that could adapt to changing data
dynamics and generalize across different scenarios.