Learning view-invariant representation is a key to improving feature
discrimination power for skeleton-based action recognition. Existing approaches
cannot effectively remove the impact of viewpoint due to the implicit
view-dependent representations. In this work, we propose a self-supervised
framework called Focalized Contrastive View-invariant Learning (FoCoViL), which
significantly suppresses the view-specific information on the representation
space where the viewpoints are coarsely aligned. By maximizing mutual
information with an effective contrastive loss between multi-view sample pairs,
FoCoViL associates actions with common view-invariant properties and
simultaneously separates the dissimilar ones. We further propose an adaptive
focalization method based on pairwise similarity to enhance contrastive
learning for a clearer cluster boundary in the learned space. Different from
many existing self-supervised representation learning work that rely heavily on
supervised classifiers, FoCoViL performs well on both unsupervised and
supervised classifiers with superior recognition performance. Extensive
experiments also show that the proposed contrastive-based focalization
generates a more discriminative latent representation.

该研究提出了一种名为 FoCoViL 的自监督框架，使用对比损失函数学习共同的视图不变属性，进而获得更具判别能力的潜在表示，并通过自适应聚焦方法来增强对比学习以获得更清晰的聚类界限，该方法在无监督和有监督分类器上都表现良好。

基于对比度聚焦的视角不变自监督骨架动作识别学习

Focalized Contrastive View-invariant Learning for Self-supervised  Skeleton-based Action Recognition

We propose a new deep architecture for person re-identification (re-id).
While re-id has seen much recent progress, spatial localization and
view-invariant representation learning for robust cross-view matching remain
key, unsolved problems. We address these questions by means of a new
attention-driven Siamese learning architecture, called the Consistent Attentive
Siamese Network. Our key innovations compared to existing, competing methods
include (a) a flexible framework design that produces attention with only
identity labels as supervision, (b) explicit mechanisms to enforce attention
consistency among images of the same person, and (c) a new Siamese framework
that integrates attention and attention consistency, producing principled
supervisory signals as well as the first mechanism that can explain the
reasoning behind the Siamese framework's predictions. We conduct extensive
evaluations on the CUHK03-NP, DukeMTMC-ReID, and Market-1501 datasets and
report competitive performance.

本论文提出了一个用于人物再识别的深度学习网络 ——Consistent Attentive Siamese Network。该网络通过学习获得空间物体特征，使得对于跨视图的图像匹配具有鲁棒性，并展现了在保证同一身份图像的一致性的情况下，尽可能的利用图像内容进行监督与特征提取的方法，从而获得了具备解释性的预测能力。在 CUHK03-NP、DukeMTMC-ReID 和 Market-1501 数据集上进行的实验表明，该网络具有竞争力的性能表现。

一致关注孪生网络进行重新识别

Re-Identification with Consistent Attentive Siamese Networks

In the task of Object Recognition, there exists a dichotomy between the
categorization of objects and estimating object pose, where the former
necessitates a view-invariant representation, while the latter requires a
representation capable of capturing pose information over different categories
of objects. With the rise of deep architectures, the prime focus has been on
object category recognition. Deep learning methods have achieved wide success
in this task. In contrast, object pose regression using these approaches has
received relatively much less attention. In this paper we show how deep
architectures, specifically Convolutional Neural Networks (CNN), can be adapted
to the task of simultaneous categorization and pose estimation of objects. We
investigate and analyze the layers of various CNN models and extensively
compare between them with the goal of discovering how the layers of distributed
representations of CNNs represent object pose information and how this
contradicts with object category representations. We extensively experiment on
two recent large and challenging multi-view datasets. Our models achieve better
than state-of-the-art performance on both datasets.

本文探讨了深度学习方法在物体识别和姿态估计方面的应用，发现卷积神经网络（CNN）可以同时进行物体分类和姿态估计，并在两个大型多视点数据集上实现了优于最先进方法的表现。