Text style transfer aims to alter the style (e.g., sentiment) of a sentence
while preserving its content. A common approach is to map a given sentence to
content representation that is free of style, and the content representation is
fed to a decoder with a target style. Previous methods in filtering style
completely remove tokens with style at the token level, which incurs the loss
of content information. In this paper, we propose to enhance content
preservation by implicitly removing the style information of each token with
reverse attention, and thereby retain the content. Furthermore, we fuse content
information when building the target style representation, making it dynamic
with respect to the content. Our method creates not only style-independent
content representation, but also content-dependent style representation in
transferring style. Empirical results show that our method outperforms the
state-of-the-art baselines by a large margin in terms of content preservation.
In addition, it is also competitive in terms of style transfer accuracy and
fluency.

本文提出一种改进的文本风格转换方法，在保留内容的同时，通过反向关注机制从每个单词中隐含地去除其风格信息，保证了内容的完整性，并在建立目标风格表示时融合了内容信息，使其对内容具有动态性，从而既创建了风格无关的内容表示，又创建了与内容相关的风格表示，实验结果表明，该方法在保持内容的准确性方面优于现有的基准方法。

使用反向注意力和条件层归一化增强文本风格转移中的内容保留

Enhancing Content Preservation in Text Style Transfer Using Reverse  Attention and Conditional Layer Normalization

Colonoscopy is an effective technique for detecting colorectal polyps, which
are highly related to colorectal cancer. In clinical practice, segmenting
polyps from colonoscopy images is of great importance since it provides
valuable information for diagnosis and surgery. However, accurate polyp
segmentation is a challenging task, for two major reasons: (i) the same type of
polyps has a diversity of size, color and texture; and (ii) the boundary
between a polyp and its surrounding mucosa is not sharp. To address these
challenges, we propose a parallel reverse attention network (PraNet) for
accurate polyp segmentation in colonoscopy images. Specifically, we first
aggregate the features in high-level layers using a parallel partial decoder
(PPD). Based on the combined feature, we then generate a global map as the
initial guidance area for the following components. In addition, we mine the
boundary cues using a reverse attention (RA) module, which is able to establish
the relationship between areas and boundary cues. Thanks to the recurrent
cooperation mechanism between areas and boundaries, our PraNet is capable of
calibrating any misaligned predictions, improving the segmentation accuracy.
Quantitative and qualitative evaluations on five challenging datasets across
six metrics show that our PraNet improves the segmentation accuracy
significantly, and presents a number of advantages in terms of
generalizability, and real-time segmentation efficiency.

本文提出了一种基于反向注意力网络（PraNet）的用于结肠镜图像中结肠息肉准确分割的方法。在两种主要困难的挑战下，PraNet 通过并行反向注意力模块和部分的解码器提供了更准确的分割结果，并在五个数据集上进行了评估。

PraNet: 并行反向注意力网络用于息肉分割

PraNet: Parallel Reverse Attention Network for Polyp Segmentation

Benefit from the quick development of deep learning techniques, salient
object detection has achieved remarkable progresses recently. However, there
still exists following two major challenges that hinder its application in
embedded devices, low resolution output and heavy model weight. To this end,
this paper presents an accurate yet compact deep network for efficient salient
object detection. More specifically, given a coarse saliency prediction in the
deepest layer, we first employ residual learning to learn side-output residual
features for saliency refinement, which can be achieved with very limited
convolutional parameters while keep accuracy. Secondly, we further propose
reverse attention to guide such side-output residual learning in a top-down
manner. By erasing the current predicted salient regions from side-output
features, the network can eventually explore the missing object parts and
details which results in high resolution and accuracy. Experiments on six
benchmark datasets demonstrate that the proposed approach compares favorably
against state-of-the-art methods, and with advantages in terms of simplicity,
efficiency (45 FPS) and model size (81 MB).

本文提出了一种深度学习技术来实现高效的显著性目标检测，采用了残差学习和反向注意力等方法来解决现有技术中存在的低分辨率和模型复杂性等问题。实验结果表明提出的方法比现有技术更为简单高效、同时在精度和分辨率等方面也表现出优势。