Foreseeing the future is one of the key factors of intelligence. It involves
understanding of the past and current environment as well as decent experience
of its possible dynamics. In this work, we address future prediction at the
abstract level of activities. We propose a network module for learning
embeddings of the environment's dynamics in a self-supervised way. To take the
ambiguities and high variances in the future activities into account, we use a
multi-hypotheses scheme that can represent multiple futures. We demonstrate the
approach by classifying future activities on the Epic-Kitchens and Breakfast
datasets. Moreover, we generate captions that describe the future activities

本文提出了一种基于网络模块的自监督学习方法，旨在预测未来活动，并通过多假设方案及描述生成器阐述其意义，也在广泛的数据集上进行了活动分类实验。

学习表示以预测未来活动

Learning Representations for Predicting Future Activities

Image cropping aims to improve the composition as well as aesthetic quality
of an image by removing extraneous content from it. Existing image cropping
databases provide only one or several human-annotated bounding boxes as the
groundtruth, which cannot reflect the non-uniqueness and flexibility of image
cropping in practice. The employed evaluation metrics such as
intersection-over-union cannot reliably reflect the real performance of
cropping models, either. This work revisits the problem of image cropping, and
presents a grid anchor based formulation by considering the special properties
and requirements (e.g., local redundancy, content preservation, aspect ratio)
of image cropping. Our formulation reduces the searching space of candidate
crops from millions to less than one hundred. Consequently, a grid anchor based
cropping benchmark is constructed, where all crops of each image are annotated
and more reliable evaluation metrics are defined. We also design an effective
and lightweight network module, which simultaneously considers the region of
interest and region of discard for more accurate image cropping. Our model can
stably output visually pleasing crops for images of different scenes and run at
a speed of 125 FPS. Code and dataset are available at:
this https URL

本文提出了基于 grid anchor 的图像裁剪公式，并设计了有效的轻量级神经网络，同时考虑 ROI 和 ROD，从而在不同场景下运行并稳定输出具有高视觉感受性的图像截图。