This paper introduces ConStyle v2, a strong plug-and-play prompter designed
to output clean visual prompts and assist U-Net Image Restoration models in
handling multiple degradations. The joint training process of IRConStyle, an
Image Restoration framework consisting of ConStyle and a general restoration
network, is divided into two stages: first, pre-training ConStyle alone, and
then freezing its weights to guide the training of the general restoration
network. Three improvements are proposed in the pre-training stage to train
ConStyle: unsupervised pre-training, adding a pretext task (i.e.
classification), and adopting knowledge distillation. Without bells and
whistles, we can get ConStyle v2, a strong prompter for all-in-one Image
Restoration, in less than two GPU days and doesn't require any fine-tuning.
Extensive experiments on Restormer (transformer-based), NAFNet (CNN-based),
MAXIM-1S (MLP-based), and a vanilla CNN network demonstrate that ConStyle v2
can enhance any U-Net style Image Restoration models to all-in-one Image
Restoration models. Furthermore, models guided by the well-trained ConStyle v2
exhibit superior performance in some specific degradation compared to ConStyle.

该论文介绍了 ConStyle v2—— 一个强大的即插即用提示器，旨在输出干净的视觉提示并帮助 U-Net 图像恢复模型处理多种退化。深度训练证明 ConStyle v2 能够增强任何 U-Net 图像恢复模型，成为全能图像恢复模型的优秀引导者。

ConStyle v2：一个强大的全方位图像修复提示器

ConStyle v2: A Strong Prompter for All-in-One Image Restoration

A challenge in the Dialogue State Tracking (DST) field is adapting models to
new domains without using any supervised data, zero-shot domain adaptation.
Parameter-Efficient Transfer Learning (PETL) has the potential to address this
problem due to its robustness. However, it has yet to be applied to the
zero-shot scenarios, as it is not clear how to apply it unsupervisedly.
Our method, Prompter, uses descriptions of target domain slots to generate
dynamic prefixes that are concatenated to the key and values at each layer's
self-attention mechanism. This allows for the use of prefix-tuning in
zero-shot. Prompter outperforms previous methods on both the MultiWOZ and SGD
benchmarks. In generating prefixes, our analyses find that Prompter not only
utilizes the semantics of slot descriptions but also how often the slots appear
together in conversation. Moreover, Prompter's gains are due to its improved
ability to distinguish "none"-valued dialogue slots, compared against
baselines.

Prompter 提出了一种使用目标领域槽的描述生成动态前缀的方法，从而使得可以在零样本情况下使用前缀调整。该方法在 MultiWOZ 和 SGD 基准测试中均优于以前的方法。