Due to the uneven scattering and absorption of different light wavelengths in
aquatic environments, underwater images suffer from low visibility and clear
color deviations. With the advancement of autonomous underwater vehicles,
extensive research has been conducted on learning-based underwater enhancement
algorithms. These works can generate visually pleasing enhanced images and
mitigate the adverse effects of degraded images on subsequent perception tasks.
However, learning-based methods are susceptible to the inherent fragility of
adversarial attacks, causing significant disruption in results. In this work,
we introduce a collaborative adversarial resilience network, dubbed CARNet, for
underwater image enhancement and subsequent detection tasks. Concretely, we
first introduce an invertible network with strong perturbation-perceptual
abilities to isolate attacks from underwater images, preventing interference
with image enhancement and perceptual tasks. Furthermore, we propose a
synchronized attack training strategy with both visual-driven and
perception-driven attacks enabling the network to discern and remove various
types of attacks. Additionally, we incorporate an attack pattern discriminator
to heighten the robustness of the network against different attacks. Extensive
experiments demonstrate that the proposed method outputs visually appealing
enhancement images and perform averagely 6.71% higher detection mAP than
state-of-the-art methods.

本论文介绍了一种协作对抗鲁棒性网络（CARNet），用于水下图像增强和后续检测任务。通过引入具有强大扰动感知能力的可逆网络来隔离水下图像中的攻击，防止对图像增强和感知任务的干扰，并结合视觉驱动和感知驱动攻击的同步训练策略，使网络能够识别和去除各种类型的攻击。此外，还结合攻击模式判别器提高网络对不同攻击的鲁棒性。实验证明，该方法输出视觉上令人满意的增强图像，并相比最先进方法具有平均 6.71% 更高的检测 mAP。

协作强韧水下图像增强与感知的双重对抗性韧性

Dual Adversarial Resilience for Collaborating Robust Underwater Image  Enhancement and Perception

Layout is a fundamental component of any graphic design. Creating large
varieties of plausible document layouts can be a tedious task, requiring
numerous constraints to be satisfied, including local ones relating different
semantic elements and global constraints on the general appearance and spacing.
In this paper, we present a novel framework, coined READ, for REcursive
Autoencoders for Document layout generation, to generate plausible 2D layouts
of documents in large quantities and varieties. First, we devise an exploratory
recursive method to extract a structural decomposition of a single document.
Leveraging a dataset of documents annotated with labeled bounding boxes, our
recursive neural network learns to map the structural representation, given in
the form of a simple hierarchy, to a compact code, the space of which is
approximated by a Gaussian distribution. Novel hierarchies can be sampled from
this space, obtaining new document layouts. Moreover, we introduce a
combinatorial metric to measure structural similarity among document layouts.
We deploy it to show that our method is able to generate highly variable and
realistic layouts. We further demonstrate the utility of our generated layouts
in the context of standard detection tasks on documents, showing that detection
performance improves when the training data is augmented with generated
documents whose layouts are produced by READ.

本文提出了一种名为 READ 的新框架，利用递归自动编码器生成大量和多样的文档 2D 布局，其中采用了一种递归的方法来提取文档的结构分解，并利用标注边界框的文档数据集对此进行了学习，从而获得了结构表示形式，并将其映射到高斯空间，从而生成新的文本布局，同时，还引入了一种组合度量来测量文档布局之间的结构相似性，并证明了所生成文档布局的高可变性和实际性，在文本检测任务中证明了所生成布局的作用。

递归自编码器用于文档布局生成

READ: Recursive Autoencoders for Document Layout Generation

This paper focuses on detection tasks in information extraction, where
positive instances are sparsely distributed and models are usually evaluated
using F-measure on positive classes. These characteristics often result in
deficient performance of neural network based detection models. In this paper,
we propose adaptive scaling, an algorithm which can handle the positive
sparsity problem and directly optimize over F-measure via dynamic
cost-sensitive learning. To this end, we borrow the idea of marginal utility
from economics and propose a theoretical framework for instance importance
measuring without introducing any additional hyper-parameters. Experiments show
that our algorithm leads to a more effective and stable training of neural
network based detection models.

本文提出了一种自适应缩放算法，通过动态代价敏感性学习实现直接优化 F 值，处理信息提取中稀疏正实例的问题，并采用边际效用的概念对实例重要性进行测量，在不引入额外超参数的情况下，有效解决了神经网络检测模型训练不稳定性的问题。