Automated and robust portrait quality assessment (PQA) is of paramount
importance in high-impact applications such as smartphone photography. This
paper presents FHIQA, a learning-based approach to PQA that introduces a simple
but effective quality score rescaling method based on image semantics, to
enhance the precision of fine-grained image quality metrics while ensuring
robust generalization to various scene settings beyond the training dataset.
The proposed approach is validated by extensive experiments on the PIQ23
benchmark and comparisons with the current state of the art. The source code of
FHIQA will be made publicly available on the PIQ23 GitHub repository at
this https URL

FHIQA 是一种基于学习的自动肖像质量评估方法，通过图像语义的简单但有效的质量分数重新缩放方法来增强细粒度图像质量度量的准确性，同时保证在训练数据集之外的各种场景设置下能够具有稳健的泛化能力。

广义肖像质量评估

Generalized Portrait Quality Assessment

Extracting image semantics effectively and assigning corresponding labels to
multiple objects or attributes for natural images is challenging due to the
complex scene contents and confusing label dependencies. Recent works have
focused on modeling label relationships with graph and understanding object
regions using class activation maps (CAM). However, these methods ignore the
complex intra- and inter-category relationships among specific semantic
features, and CAM is prone to generate noisy information. To this end, we
propose a novel semantic-aware dual contrastive learning framework that
incorporates sample-to-sample contrastive learning (SSCL) as well as
prototype-to-sample contrastive learning (PSCL). Specifically, we leverage
semantic-aware representation learning to extract category-related local
discriminative features and construct category prototypes. Then based on SSCL,
label-level visual representations of the same category are aggregated
together, and features belonging to distinct categories are separated.
Meanwhile, we construct a novel PSCL module to narrow the distance between
positive samples and category prototypes and push negative samples away from
the corresponding category prototypes. Finally, the discriminative label-level
features related to the image content are accurately captured by the joint
training of the above three parts. Experiments on five challenging large-scale
public datasets demonstrate that our proposed method is effective and
outperforms the state-of-the-art methods. Code and supplementary materials are
released on this https URL

通过引入样本对间对比学习和原型 - 样本对比学习，提出了一种新颖的语义感知双对比学习框架，通过联合训练三个模块，准确捕捉与图像内容相关的判别性标签级特征，并在五个具有挑战性的大规模公共数据集上实验证明了该方法的有效性优于现有技术。

多标签图像分类的语义感知双对比学习

Semantic-Aware Dual Contrastive Learning for Multi-label Image  Classification

Rich semantics inside an image result in its ambiguous relationship with
others, i.e., two images could be similar in one condition but dissimilar in
another. Given triplets like "aircraft" is similar to "bird" than "train",
Weakly Supervised Conditional Similarity Learning (WS-CSL) learns multiple
embeddings to match semantic conditions without explicit condition labels such
as "can fly". However, similarity relationships in a triplet are uncertain
except providing a condition. For example, the previous comparison becomes
invalid once the conditional label changes to "is vehicle". To this end, we
introduce a novel evaluation criterion by predicting the comparison's
correctness after assigning the learned embeddings to their optimal conditions,
which measures how much WS-CSL could cover latent semantics as the supervised
model. Furthermore, we propose the Distance Induced Semantic COndition
VERification Network (DiscoverNet), which characterizes the instance-instance
and triplets-condition relations in a "decompose-and-fuse" manner. To make the
learned embeddings cover all semantics, DiscoverNet utilizes a set module or an
additional regularizer over the correspondence between a triplet and a
condition. DiscoverNet achieves state-of-the-art performance on benchmarks like
UT-Zappos-50k and Celeb-A w.r.t. different criteria.

这篇研究论文提出了一种基于弱监督条件相似性学习（WS-CSL）的新型评估准则，结合 Distance Induced Semantic Condition Verification Network (DiscoverNet) 用于图像语义比较，全面覆盖潜在语义以达到最先进的性能。

通过语义匹配识别模糊相似的情况

Identifying Ambiguous Similarity Conditions via Semantic Matching

Semantic inpainting is the task of inferring missing pixels in an image given
surrounding pixels and high level image semantics. Most semantic inpainting
algorithms are deterministic: given an image with missing regions, a single
inpainted image is generated. However, there are often several plausible
inpaintings for a given missing region. In this paper, we propose a method to
perform probabilistic semantic inpainting by building a model, based on
PixelCNNs, that learns a distribution of images conditioned on a subset of
visible pixels. Experiments on the MNIST and CelebA datasets show that our
method produces diverse and realistic inpaintings.

该研究论文提出了一种基于 PixelCNNs 的概率语义修复方法，该方法可以学习有关给定少量像素的图像分布，从而产生多样化和逼真的修复结果，实验证明该方法在 MNIST 和 CelebA 数据集上的表现。