Existing methods attempt to improve models' generalization ability on
real-world hazy images by exploring well-designed training schemes (e.g.,
cycleGAN, prior loss). However, most of them need very complicated training
procedures to achieve satisfactory results. In this work, we present a totally
novel testing pipeline called Prompt-based Test-Time Dehazing (PTTD) to help
generate visually pleasing results of real-captured hazy images during the
inference phase. We experimentally find that given a dehazing model trained on
synthetic data, by fine-tuning the statistics (i.e., mean and standard
deviation) of encoding features, PTTD is able to narrow the domain gap,
boosting the performance of real image dehazing. Accordingly, we first apply a
prompt generation module (PGM) to generate a visual prompt, which is the source
of appropriate statistical perturbations for mean and standard deviation. And
then, we employ the feature adaptation module (FAM) into the existing dehazing
models for adjusting the original statistics with the guidance of the generated
prompt. Note that, PTTD is model-agnostic and can be equipped with various
state-of-the-art dehazing models trained on synthetic hazy-clean pairs.
Extensive experimental results demonstrate that our PTTD is flexible meanwhile
achieves superior performance against state-of-the-art dehazing methods in
real-world scenarios.

测试环节具有在真实场景中生成逼真结果的全新方法 —— 基于提示的测试时去雾（PTTD）周期，该方法通过微调编码特征的统计值来缩小领域差距，提高真实图像去雾的性能，并且在灵活性方面取得了优越的性能表现。

基于提示的实时图像去雾测试流程：一种新的流水线

Prompt-based test-time real image dehazing: a novel pipeline

Learning-based image compression was shown to achieve a competitive
performance with state-of-the-art transform-based codecs. This motivated the
development of new learning-based visual compression standards such as JPEG-AI.
Of particular interest to these emerging standards is the development of
learning-based image compression systems targeting both humans and machines.
This paper is concerned with learning-based compression schemes whose
compressed-domain representations can be utilized to perform visual processing
and computer vision tasks directly in the compressed domain. In our work, we
adopt a learning-based compressed-domain classification framework for
performing visual recognition using the compressed-domain latent representation
at varying bit-rates. We propose a novel feature adaptation module integrating
a lightweight attention model to adaptively emphasize and enhance the key
features within the extracted channel-wise information. Also, we design an
adaptation training strategy to utilize the pretrained pixel-domain weights.
For comparison, in addition to the performance results that are obtained using
our proposed latent-based compressed-domain method, we also present performance
results using compressed but fully decoded images in the pixel domain as well
as original uncompressed images. The obtained performance results show that our
proposed compressed-domain classification model can distinctly outperform the
existing compressed-domain classification models, and that it can also yield
similar accuracy results with a much higher computational efficiency as
compared to the pixel-domain models that are trained using fully decoded
images.

本文提出一种基于学习的压缩域分类框架，可以在不同比特率下利用压缩域潜在表示执行视觉识别，包括引入轻量级注意模型的新型特征适应模块，它可以自适应地强调和增强提取的逐通道信息的关键特征；还介绍了一种适配训练策略，使用预训练的像素域权重。与使用像素域完全解码的压缩图像以及未压缩图像相比，研究结果表明，提出的压缩域分类模型不仅可以显著优于现有的压缩域分类模型，而且在计算效率方面也可以获得与使用完全解码图像的像素域模型相似的精确度结果。

基于压缩域的深度神经网络可视识别技术与特征适应

DNN-Compressed Domain Visual Recognition with Feature Adaptation

Adapting pre-trained language models (PrLMs) (e.g., BERT) to new domains has
gained much attention recently. Instead of fine-tuning PrLMs as done in most
previous work, we investigate how to adapt the features of PrLMs to new domains
without fine-tuning. We explore unsupervised domain adaptation (UDA) in this
paper. With the features from PrLMs, we adapt the models trained with labeled
data from the source domain to the unlabeled target domain. Self-training is
widely used for UDA which predicts pseudo labels on the target domain data for
training. However, the predicted pseudo labels inevitably include noise, which
will negatively affect training a robust model. To improve the robustness of
self-training, in this paper we present class-aware feature self-distillation
(CFd) to learn discriminative features from PrLMs, in which PrLM features are
self-distilled into a feature adaptation module and the features from the same
class are more tightly clustered. We further extend CFd to a cross-language
setting, in which language discrepancy is studied. Experiments on two
monolingual and multilingual Amazon review datasets show that CFd can
consistently improve the performance of self-training in cross-domain and
cross-language settings.

本研究中，我们探究了将预训练语言模型（PrLMs）特征适应新域的方法，而非像以往一样对其进行精细调整。我们提出了面向无监督域适应 (UDA) 的类感知特征自蒸馏（CFd）算法，能够有效地提高跨领域和跨语言设置下的模型稳健性。