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.

开发一种测试时自适应方法，用于语义分割任务中针对视频序列中逐渐变化的领域进行模型适应，主要基于合成自驾车视频数据集 SHIFT，目标是分析分布变化并开发一个能够适应数据动态变化和在不同场景中泛化的方法。

ICCV 2023 视觉持续学习挑战的技术报告：语义分割的连续测试时适应

Technical Report for ICCV 2023 Visual Continual Learning Challenge:  Continuous Test-time Adaptation for Semantic Segmentation

Although face anti-spoofing (FAS) methods have achieved remarkable
performance on specific domains or attack types, few studies have focused on
the simultaneous presence of domain changes and unknown attacks, which is
closer to real application scenarios. To handle domain-generalized unknown
attacks, we introduce a new method, DGUA-FAS, which consists of a
Transformer-based feature extractor and a synthetic unknown attack sample
generator (SUASG). The SUASG network simulates unknown attack samples to assist
the training of the feature extractor. Experimental results show that our
method achieves superior performance on domain generalization FAS with known or
unknown attacks.

通过引入一种新方法 DGUA-FAS，该方法包括基于 Transformer 的特征提取器和合成未知攻击样本生成器（SUASG），我们针对领域通用的未知攻击解决方案，实验结果表明我们的方法在已知或未知攻击的领域泛化 FAS 方面取得了卓越的性能。

领域通用的未知攻击下的人脸反欺诈

Domain-Generalized Face Anti-Spoofing with Unknown Attacks

Deep learning has improved vanishing point detection in images. Yet, deep
networks require expensive annotated datasets trained on costly hardware and do
not generalize to even slightly different domains, and minor problem variants.
Here, we address these issues by injecting deep vanishing point detection
networks with prior knowledge. This prior knowledge no longer needs to be
learned from data, saving valuable annotation efforts and compute, unlocking
realistic few-sample scenarios, and reducing the impact of domain changes.
Moreover, the interpretability of the priors allows to adapt deep networks to
minor problem variations such as switching between Manhattan and non-Manhattan
worlds. We seamlessly incorporate two geometric priors: (i) Hough Transform --
mapping image pixels to straight lines, and (ii) Gaussian sphere -- mapping
lines to great circles whose intersections denote vanishing points.
Experimentally, we ablate our choices and show comparable accuracy to existing
models in the large-data setting. We validate our model's improved data
efficiency, robustness to domain changes, adaptability to non-Manhattan
settings.

通过注入先验知识，减少数据注释和计算并缓解域变化，提高数据效率，强健性，并适应非曼哈顿设置的深度消失点检测网络。