To improve the sample efficiency of vision-based deep reinforcement learning
(RL), we propose a novel method, called SPIRL, to automatically extract
important patches from input images. Following Masked Auto-Encoders, SPIRL is
based on Vision Transformer models pre-trained in a self-supervised fashion to
reconstruct images from randomly-sampled patches. These pre-trained models can
then be exploited to detect and select salient patches, defined as hard to
reconstruct from neighboring patches. In RL, the SPIRL agent processes selected
salient patches via an attention module. We empirically validate SPIRL on Atari
games to test its data-efficiency against relevant state-of-the-art methods,
including some traditional model-based methods and keypoint-based models. In
addition, we analyze our model's interpretability capabilities.

为了提高基于视觉的深度强化学习的样本效率，我们提出了一种名为 SPIRL 的新方法，用于自动提取输入图像中的重要区域。SPIRL 基于自编码器模型，在自监督训练的基础上，通过从随机采样的区域重建图像，再利用这些预训练模型检测和选择显著区域。在强化学习中，SPIRL 代理通过注意力机制处理选定的显著区域。我们在 Atari 游戏上经验证明 SPIRL 在数据效率方面优于相关先进方法，包括一些传统的基于模型和基于关键点的模型。此外，我们还分析了我们模型的可解释性能力。

数据高效增强学习中的无监督显著路径选择

Unsupervised Salient Patch Selection for Data-Efficient Reinforcement  Learning

Extraction of discriminative features from salient facial patches plays a
vital role in effective facial expression recognition. The accurate detection
of facial landmarks improves the localization of the salient patches on face
images. This paper proposes a novel framework for expression recognition by
using appearance features of selected facial patches. A few prominent facial
patches, depending on the position of facial landmarks, are extracted which are
active during emotion elicitation. These active patches are further processed
to obtain the salient patches which contain discriminative features for
classification of each pair of expressions, thereby selecting different facial
patches as salient for different pair of expression classes. One-against-one
classification method is adopted using these features. In addition, an
automated learning-free facial landmark detection technique has been proposed,
which achieves similar performances as that of other state-of-art landmark
detection methods, yet requires significantly less execution time. The proposed
method is found to perform well consistently in different resolutions, hence,
providing a solution for expression recognition in low resolution images.
Experiments on CK+ and JAFFE facial expression databases show the effectiveness
of the proposed system.

本文提出了一种用于面部表情识别的新框架，通过使用选择面部补丁的外观特征，使用一对一分类方法来分类每对表情类，提取包含区分特征的显著补丁，并针对不同表情类选择不同的显著补丁，提出了一个自动学习 - 无费用的面部标记检测技术，该技术在不同分辨率下表现稳定，在 CK + 和 JAFFE 面部表情数据库上展示了该系统的有效性。