Current state-of-the-art methods for image segmentation form a dense image
representation where the color, shape and texture information are all processed
together inside a deep CNN. This however may not be ideal as they contain very
different type of information relevant for recognition. Here, we propose a new
two-stream CNN architecture for semantic segmentation that explicitly wires
shape information as a separate processing branch, i.e. shape stream, that
processes information in parallel to the classical stream. Key to this
architecture is a new type of gates that connect the intermediate layers of the
two streams. Specifically, we use the higher-level activations in the classical
stream to gate the lower-level activations in the shape stream, effectively
removing noise and helping the shape stream to only focus on processing the
relevant boundary-related information. This enables us to use a very shallow
architecture for the shape stream that operates on the image-level resolution.
Our experiments show that this leads to a highly effective architecture that
produces sharper predictions around object boundaries and significantly boosts
performance on thinner and smaller objects. Our method achieves
state-of-the-art performance on the Cityscapes benchmark, in terms of both mask
(mIoU) and boundary (F-score) quality, improving by 2% and 4% over strong
baselines.

本文提出了一种新的双流 CNN 架构，其中显式地将形状信息作为单独的处理分支，即形状流，以并行方式处理信息。此架构的关键是一种连接两个流的中间层的新型门，用于连接经典流的高级激活以控制形状流的低级激活，有效地消除噪声并帮助形状流仅关注处理相关边界信息。实验表明，该方法在 Cityscapes 基准上可以显着提高细小物体的性能，并取得了最先进的性能。

门控形状卷积神经网络用于语义分割

Gated-SCNN: Gated Shape CNNs for Semantic Segmentation

We propose a two-stage convolutional neural network (CNN) architecture for
robust recognition of hand gestures, called HGR-Net, where the first stage
performs accurate semantic segmentation to determine hand regions, and the
second stage identifies the gesture. The segmentation stage architecture is
based on the combination of fully convolutional residual network and atrous
spatial pyramid pooling. Although the segmentation sub-network is trained
without depth information, it is particularly robust against challenges such as
illumination variations and complex backgrounds. The recognition stage deploys
a two-stream CNN, which fuses the information from the red-green-blue and
segmented images by combining their deep representations in a fully connected
layer before classification. Extensive experiments on public datasets show that
our architecture achieves almost as good as state-of-the-art performance in
segmentation and recognition of static hand gestures, at a fraction of training
time, run time, and model size. Our method can operate at an average of 23 ms
per frame.

HGR-Net 是一个使用两个 CNN 阶段的手势识别模型。第一阶段执行精确的语义分割以确定手部区域，第二阶段在深度表示的融合分类之前识别手势。通过在公共数据集上进行的大量实验，该模型在静态手势的分割和识别方面几乎达到了与最先进性能相当的效果。

HGR-Net：一种用于手势分割和识别的融合网络

HGR-Net: A Fusion Network for Hand Gesture Segmentation and Recognition

Accurate detection of fingertips in depth image is critical for
human-computer interaction. In this paper, we present a novel two-stream
convolutional neural network (CNN) for RGB-D fingertip detection. Firstly edge
image is extracted from raw depth image using random forest. Then the edge
information is combined with depth information in our CNN structure. We study
several fusion approaches and suggest a slow fusion strategy as a promising way
of fingertip detection. As shown in our experiments, our real-time algorithm
outperforms state-of-the-art fingertip detection methods on the public dataset
HandNet with an average 3D error of 9.9mm, and shows comparable accuracy of
fingertip estimation on NYU hand dataset.

本研究提出了一种基于两流 CNN 和慢融合策略的 RGB-D 指尖检测算法，实验结果表明此算法对于人机交互有着较高的精度。