We present HandGAN (H-GAN), a cycle-consistent adversarial learning approach
implementing multi-scale perceptual discriminators. It is designed to translate
synthetic images of hands to the real domain. Synthetic hands provide complete
ground-truth annotations, yet they are not representative of the target
distribution of real-world data. We strive to provide the perfect blend of a
realistic hand appearance with synthetic annotations. Relying on image-to-image
translation, we improve the appearance of synthetic hands to approximate the
statistical distribution underlying a collection of real images of hands. H-GAN
tackles not only the cross-domain tone mapping but also structural differences
in localized areas such as shading discontinuities. Results are evaluated on a
qualitative and quantitative basis improving previous works. Furthermore, we
relied on the hand classification task to claim our generated hands are
statistically similar to the real domain of hands.

本论文提出 HandGAN，一种采用多尺度感知判别器的循环一致性对抗性学习方法，旨在将合成的手部图像转换为真实领域中的图像，从而提高人工合成手部图像的逼真程度。

H-GAN: 手中的 GAN 力量

H-GAN: the power of GANs in your Hands

We bring together ideas from recent work on feature design for egocentric
action recognition under one framework by exploring the use of deep
convolutional neural networks (CNN). Recent work has shown that features such
as hand appearance, object attributes, local hand motion and camera ego-motion
are important for characterizing first-person actions. To integrate these ideas
under one framework, we propose a twin stream network architecture, where one
stream analyzes appearance information and the other stream analyzes motion
information. Our appearance stream encodes prior knowledge of the egocentric
paradigm by explicitly training the network to segment hands and localize
objects. By visualizing certain neuron activation of our network, we show that
our proposed architecture naturally learns features that capture object
attributes and hand-object configurations. Our extensive experiments on
benchmark egocentric action datasets show that our deep architecture enables
recognition rates that significantly outperform state-of-the-art techniques --
an average $6.6\%$ increase in accuracy over all datasets. Furthermore, by
learning to recognize objects, actions and activities jointly, the performance
of individual recognition tasks also increase by $30\%$ (actions) and $14\%$
(objects). We also include the results of extensive ablative analysis to
highlight the importance of network design decisions..

本研究提出了一种双流网络架构，其中一个流分析外观信息，另一个流分析动作信息，并利用卷积神经网络在手势外观、对象属性、本地手势运动和相机自我运动等方面的知识信息，可以有效地提高第一人称视角下动作识别的准确率。