Video motion magnification amplifies invisible small motions to be
perceptible, which provides humans with spatially dense and holistic
understanding about small motions from the scene of interest. This is based on
the premise that magnifying small motions enhances the legibility of the
motion. In the real world, however, vibrating objects often possess complex
systems, having complex natural frequencies, modes, and directions. Existing
motion magnification often fails to improve the legibility since the intricate
motions still retain complex characteristics even when magnified, which
distracts us from analyzing them. In this work, we focus on improving the
legibility by proposing a new concept, axial motion magnification, which
magnifies decomposed motions along the user-specified direction. Axial motion
magnification can be applied to various applications where motions of specific
axes are critical, by providing simplified and easily readable motion
information. We propose a novel learning-based axial motion magnification
method with the Motion Separation Module that enables to disentangle and
magnify the motion representation along axes of interest. Further, we build a
new synthetic training dataset for the axial motion magnification task. Our
proposed method improves the legibility of resulting motions along certain
axes, while adding additional user controllability. Our method can be directly
adopted to the generic motion magnification and achieves favorable performance
against competing methods. Our project page is available at
this https URL

改进了通过提出一种名为轴向动作放大的新概念来提高视频动作放大的可读性，该方法可以放大沿用户指定方向的分解动作，并通过 Motion Separation 模块进行学习，以实现对特定轴的运动的放大和简化，从而提供更容易阅读的动作信息，并可应用于多种应用中具有特定运动轴的关键性运动。

基于学习的轴向运动放大

Learning-based Axial Motion Magnification

Modern recommender systems utilize users' historical behaviors to generate
personalized recommendations. However, these systems often lack user
controllability, leading to diminished user satisfaction and trust in the
systems. Acknowledging the recent advancements in explainable recommender
systems that enhance users' understanding of recommendation mechanisms, we
propose leveraging these advancements to improve user controllability. In this
paper, we present a user-controllable recommender system that seamlessly
integrates explainability and controllability within a unified framework. By
providing both retrospective and prospective explanations through
counterfactual reasoning, users can customize their control over the system by
interacting with these explanations.
Furthermore, we introduce and assess two attributes of controllability in
recommendation systems: the complexity of controllability and the accuracy of
controllability. Experimental evaluations on MovieLens and Yelp datasets
substantiate the effectiveness of our proposed framework. Additionally, our
experiments demonstrate that offering users control options can potentially
enhance recommendation accuracy in the future. Source code and data are
available at https://github.com/chrisjtan/ucr.

我们提出了一个用户可控的推荐系统，通过整合可解释性和可控性，通过提供回顾性和预测性的解释，用户可以与这些解释进行交互以自定义对系统的控制。实验证明，我们的框架在 MovieLens 和 Yelp 数据集上具有有效性，并且提供用户控制选项可以潜在地提高推荐的准确性。