State-of-the-art face recognition (FR) models often experience a significant
performance drop when dealing with facial images in surveillance scenarios
where images are in low quality and often corrupted with noise. Leveraging
facial characteristics, such as freckles, scars, gender, and ethnicity, becomes
highly beneficial in improving FR performance in such scenarios. In this paper,
we introduce text-guided face recognition (TGFR) to analyze the impact of
integrating facial attributes in the form of natural language descriptions. We
hypothesize that adding semantic information into the loop can significantly
improve the image understanding capability of an FR algorithm compared to other
soft biometrics. However, learning a discriminative joint embedding within the
multimodal space poses a considerable challenge due to the semantic gap in the
unaligned image-text representations, along with the complexities arising from
ambiguous and incoherent textual descriptions of the face. To address these
challenges, we introduce a face-caption alignment module (FCAM), which
incorporates cross-modal contrastive losses across multiple granularities to
maximize the mutual information between local and global features of the
face-caption pair. Within FCAM, we refine both facial and textual features for
learning aligned and discriminative features. We also design a face-caption
fusion module (FCFM) that applies fine-grained interactions and coarse-grained
associations among cross-modal features. Through extensive experiments
conducted on three face-caption datasets, proposed TGFR demonstrates remarkable
improvements, particularly on low-quality images, over existing FR models and
outperforms other related methods and benchmarks.

通过引入面部属性和语义信息，以提高在低质量图像中进行面部识别的性能为目标，本文介绍了一种名为文本引导的面部识别（TGFR）方法，通过跨模态对齐和融合模块，以及面部特征和文本特征的优化，实现了显著的性能提升。

多粒度跨模态对比学习下的文本引导人脸识别

Text-Guided Face Recognition using Multi-Granularity Cross-Modal  Contrastive Learning

Spinal curvature estimation is important to the diagnosis and treatment of
the scoliosis. Existing methods face several issues such as the need of
expensive annotations on the vertebral landmarks and being sensitive to the
image quality. It is challenging to achieve robust estimation and obtain
interpretable results, especially for low-quality images which are blurry and
hazy. In this paper, we propose B-Spine, a novel deep learning pipeline to
learn B-spline curve representation of the spine and estimate the Cobb angles
for spinal curvature estimation from low-quality X-ray images. Given a
low-quality input, a novel SegRefine network which employs the unpaired
image-to-image translation is proposed to generate a high quality spine mask
from the initial segmentation result. Next, a novel mask-based B-spline
prediction model is proposed to predict the B-spline curve for the spine
centerline. Finally, the Cobb angles are estimated by a hybrid approach which
combines the curve slope analysis and a curve-based regression model. We
conduct quantitative and qualitative comparisons with the representative and
SOTA learning-based methods on the public AASCE2019 dataset and our new
proposed CJUH-JLU dataset which contains more challenging low-quality images.
The superior performance on both datasets shows our method can achieve both
robustness and interpretability for spinal curvature estimation.

本文提出了 B-Spine，这是一个用于学习脊柱的 B 样条曲线表示并从低质量 X 射线图像中估计脊柱曲率的新型深度学习流程。通过在初始分割结果上生成高质量脊柱掩模的 SegRefine 网络，该方法对低质量输入图像进行预测，并利用曲线斜率分析和基于曲线的回归模型估计 Cobb 角度，从而在公开的 AASCE2019 数据集和我们提出的 CJUH-JLU 数据集上展示了其在稳健性和解释性方面的优越性能。

B-Spine: 学习 B 样条曲线表示的鲁棒和可解释的脊柱曲率估计

B-Spine: Learning B-Spline Curve Representation for Robust and  Interpretable Spinal Curvature Estimation

Restoring facial details from low-quality (LQ) images has remained a
challenging problem due to its ill-posedness induced by various degradations in
the wild. The existing codebook prior mitigates the ill-posedness by leveraging
an autoencoder and learned codebook of high-quality (HQ) features, achieving
remarkable quality. However, existing approaches in this paradigm frequently
depend on a single encoder pre-trained on HQ data for restoring HQ images,
disregarding the domain gap between LQ and HQ images. As a result, the encoding
of LQ inputs may be insufficient, resulting in suboptimal performance. To
tackle this problem, we propose a novel dual-branch framework named DAEFR. Our
method introduces an auxiliary LQ branch that extracts crucial information from
the LQ inputs. Additionally, we incorporate association training to promote
effective synergy between the two branches, enhancing code prediction and
output quality. We evaluate the effectiveness of DAEFR on both synthetic and
real-world datasets, demonstrating its superior performance in restoring facial
details.

通过引入辅助 LQ 分支和关联训练，我们提出了一种名为 DAEFR 的新型双分支框架，这种方法在还原面部细节方面表现出卓越的性能。

人脸恢复的双重关联编码器

Dual Associated Encoder for Face Restoration

Face detection is a well-explored problem. Many challenges on face detectors
like extreme pose, illumination, low resolution and small scales are studied in
the previous work. However, previous proposed models are mostly trained and
tested on good-quality images which are not always the case for practical
applications like surveillance systems. In this paper, we first review the
current state-of-the-art face detectors and their performance on benchmark
dataset FDDB, and compare the design protocols of the algorithms. Secondly, we
investigate their performance degradation while testing on low-quality images
with different levels of blur, noise, and contrast. Our results demonstrate
that both hand-crafted and deep-learning based face detectors are not robust
enough for low-quality images. It inspires researchers to produce more robust
design for face detection in the wild.

这篇论文回顾了目前最先进的人脸检测器在高质量图像上的表现，并评估了它们在低质量图像上的鲁棒性，结果表明，手工设计或基于深度学习的人脸检测器在低质量图像上的性能下降，需要研究更加鲁棒的设计方案。