Due to their highly structured characteristics, faces are easier to recover
than natural scenes for blind image super-resolution. Therefore, we can extract
the degradation representation of an image from the low-quality and recovered
face pairs. Using the degradation representation, realistic low-quality images
can then be synthesized to fine-tune the super-resolution model for the
real-world low-quality image. However, such a procedure is time-consuming and
laborious, and the gaps between recovered faces and the ground-truths further
increase the optimization uncertainty. To facilitate efficient model adaptation
towards image-specific degradations, we propose a method dubbed MetaF2N, which
leverages the contained Faces to fine-tune model parameters for adapting to the
whole Natural image in a Meta-learning framework. The degradation extraction
and low-quality image synthesis steps are thus circumvented in our MetaF2N, and
it requires only one fine-tuning step to get decent performance. Considering
the gaps between the recovered faces and ground-truths, we further deploy a
MaskNet for adaptively predicting loss weights at different positions to reduce
the impact of low-confidence areas. To evaluate our proposed MetaF2N, we have
collected a real-world low-quality dataset with one or multiple faces in each
image, and our MetaF2N achieves superior performance on both synthetic and
real-world datasets. Source code, pre-trained models, and collected datasets
are available at this https URL

通过 MetaF2N 方法，利用包含的人脸来微调超分辨率模型参数以适应整个自然图像，在去除降级表征和低质量图像合成步骤的同时，只需一次微调步骤即可获得良好的性能。

MetaF2N：基于学习高效模型自适应的盲目人脸图像超分辨率

MetaF2N: Blind Image Super-Resolution by Learning Efficient Model  Adaptation from Faces

Sentence fusion is the task of joining related sentences into coherent text.
Current training and evaluation schemes for this task are based on single
reference ground-truths and do not account for valid fusion variants. We show
that this hinders models from robustly capturing the semantic relationship
between input sentences. To alleviate this, we present an approach in which
ground-truth solutions are automatically expanded into multiple references via
curated equivalence classes of connective phrases. We apply this method to a
large-scale dataset and use the augmented dataset for both model training and
evaluation. To improve the learning of semantic representation using multiple
references, we enrich the model with auxiliary discourse classification tasks
under a multi-tasking framework. Our experiments highlight the improvements of
our approach over state-of-the-art models.

本文提出一种基于多重参考的句子融合方法，通过使用连接短语的等价类将基准解扩展到多个参考，结合多任务框架来增强模型的语义表示学习以及进行模型训练和评估，实验结果表明该方法优于现有模型。

语义驱动的语句融合：建模和评估

Semantically Driven Sentence Fusion: Modeling and Evaluation

In a previous work, it was shown that there is a curious problem with the
benchmark ColorChecker dataset for illuminant estimation. To wit, this dataset
has at least 3 different sets of ground-truths. Typically, for a single
algorithm a single ground-truth is used. But then different algorithms, whose
performance is measured with respect to different ground-truths, are compared
against each other and then ranked. This makes no sense. We show in this paper
that there are also errors in how each ground-truth set was calculated. As a
result, all performance rankings based on the ColorChecker dataset - and there
are scores of these - are inaccurate.
In this paper, we re-generate a new 'recommended' set of ground-truth based
on the calculation methodology described by Shi and Funt. We then review the
performance evaluation of a range of illuminant estimation algorithms. Compared
with the legacy ground-truths, we find that the difference in how algorithms
perform can be large, with many local rankings of algorithms being reversed.
Finally, we draw the readers attention to our new 'open' data repository
which, we hope, will allow the ColorChecker set to be rehabilitated and once
again to become a useful benchmark for illuminant estimation algorithms.

本文针对 ColorChecker 数据集中不同的 ground-truth 数据集，重新生成了一个基于 Shi 和 Funt 描述的推荐 ground-truth，对多种光源估计算法进行了性能评估并发现了许多本地算法排名的差别。此外，我们还提供了一个开放式的数据存储库，希望 ColorChecker 数据集能够再次成为估计光源算法的一个有用基准。