Nighttime image dehazing is a challenging task due to the presence of
multiple types of adverse degrading effects including glow, haze, blurry,
noise, color distortion, and so on. However, most previous studies mainly focus
on daytime image dehazing or partial degradations presented in nighttime hazy
scenes, which may lead to unsatisfactory restoration results. In this paper, we
propose an end-to-end transformer-based framework for nighttime haze removal,
called NightHazeFormer. Our proposed approach consists of two stages:
supervised pre-training and semi-supervised fine-tuning. During the
pre-training stage, we introduce two powerful priors into the transformer
decoder to generate the non-learnable prior queries, which guide the model to
extract specific degradations. For the fine-tuning, we combine the generated
pseudo ground truths with input real-world nighttime hazy images as paired
images and feed into the synthetic domain to fine-tune the pre-trained model.
This semi-supervised fine-tuning paradigm helps improve the generalization to
real domain. In addition, we also propose a large-scale synthetic dataset
called UNREAL-NH, to simulate the real-world nighttime haze scenarios
comprehensively. Extensive experiments on several synthetic and real-world
datasets demonstrate the superiority of our NightHazeFormer over
state-of-the-art nighttime haze removal methods in terms of both visually and
quantitatively.

提出了一种基于变压器的夜间图像去雾框架 NightHazeFormer，结合了半监督的微调策略和先验查询，同时创造了大规模的合成数据集 UNREAL-NH，表现优于现有的夜间去雾方法。

Prior Query Transformer 单昼夜的夜间雾霾移除

NightHazeFormer: Single Nighttime Haze Removal Using Prior Query  Transformer

Recent studies have revealed that deep neural networks (DNNs) are vulnerable
to backdoor attacks, where attackers embed hidden backdoors in the DNN model by
poisoning a few training samples. The attacked model behaves normally on benign
samples, whereas its prediction will be maliciously changed when the backdoor
is activated. We reveal that poisoned samples tend to cluster together in the
feature space of the attacked DNN model, which is mostly due to the end-to-end
supervised training paradigm. Inspired by this observation, we propose a novel
backdoor defense via decoupling the original end-to-end training process into
three stages. Specifically, we first learn the backbone of a DNN model via
\emph{self-supervised learning} based on training samples without their labels.
The learned backbone will map samples with the same ground-truth label to
similar locations in the feature space. Then, we freeze the parameters of the
learned backbone and train the remaining fully connected layers via standard
training with all (labeled) training samples. Lastly, to further alleviate
side-effects of poisoned samples in the second stage, we remove labels of some
`low-credible' samples determined based on the learned model and conduct a
\emph{semi-supervised fine-tuning} of the whole model. Extensive experiments on
multiple benchmark datasets and DNN models verify that the proposed defense is
effective in reducing backdoor threats while preserving high accuracy in
predicting benign samples. Our code is available at
https://github.com/SCLBD/DBD.

该论文研究发现深度神经网络易受后门攻击影响，通过自监督学习和半监督微调等方法提出了一种解决方案，通过将原来的训练过程分解成三个阶段，有效地减轻了后门攻击带来的威胁。