Recent image restoration methods have produced significant advancements using
deep learning. However, existing methods tend to treat the whole image as a
single entity, failing to account for the distinct objects in the image that
exhibit individual texture properties. Existing methods also typically generate
a single result, which may not suit the preferences of different users. In this
paper, we introduce the Restore Anything Pipeline (RAP), a novel interactive
and per-object level image restoration approach that incorporates a
controllable model to generate different results that users may choose from.
RAP incorporates image segmentation through the recent Segment Anything Model
(SAM) into a controllable image restoration model to create a user-friendly
pipeline for several image restoration tasks. We demonstrate the versatility of
RAP by applying it to three common image restoration tasks: image deblurring,
image denoising, and JPEG artifact removal. Our experiments show that RAP
produces superior visual results compared to state-of-the-art methods. RAP
represents a promising direction for image restoration, providing users with
greater control, and enabling image restoration at an object level.

该论文引入了 Restore Anything Pipeline (RAP)，一种基于交互和对象级别的图像修复方法，包括可控模型和图像分割，使用户能够进行个性化选择，实验结果表明 RAP 方法比现有方法具有更强的视觉效果，是一种有希望的图像修复方向。

Restore Anything Pipeline: 分割和图像修复相遇

Restore Anything Pipeline: Segment Anything Meets Image Restoration

Sentence simplification aims to make sentences easier to read and understand.
Recent approaches have shown promising results with sequence-to-sequence models
which have been developed assuming homogeneous target audiences. In this paper
we argue that different users have different simplification needs (e.g.
dyslexics vs. non-native speakers), and propose CROSS, ContROllable Sentence
Simplification model, which allows to control both the level of simplicity and
the type of the simplification. We achieve this by enriching a
Transformer-based architecture with syntactic and lexical constraints (which
can be set or learned from data). Empirical results on two benchmark datasets
show that constraints are key to successful simplification, offering flexible
generation output.

通过对句子语法和词汇约束的控制，本文提出了一种可以满足不同用户简化需求的 CROSS 句子简化模型，使用 Transformer-based 结构，在两个基准数据集上实验表明，语法和词汇约束对于成功的简化至关重要。

可控的句子简化：应用句法和词汇约束

Controllable Sentence Simplification: Employing Syntactic and Lexical  Constraints

We are given a video of a person performing a certain activity, from which we
extract a controllable model. The model generates novel image sequences of that
person, according to arbitrary user-defined control signals, typically marking
the displacement of the moving body. The generated video can have an arbitrary
background, and effectively capture both the dynamics and appearance of the
person.
The method is based on two networks. The first network maps a current pose,
and a single-instance control signal to the next pose. The second network maps
the current pose, the new pose, and a given background, to an output frame.
Both networks include multiple novelties that enable high-quality performance.
This is demonstrated on multiple characters extracted from various videos of
dancers and athletes.

该研究介绍了一种使用可控模型和神经网络生成具有自定义运动控制、背景及外观的人物影像序列的方法，展示了该方法在多个舞蹈家和运动员视频中的高质量表现。