Parsing human regions into semantic parts, e.g., body, head and arms etc., from a random natural image is challenging while fundamental for computer vision and widely applicable in industries. One major difficulty to handle such a problem is the high flexibility of scale and location of a human instance and its corresponding parts, making the parsing task either lack of boundary details or suffering from local confusions. To tackle such problems, in this work, we propose the Auto-Zoom Net (AZN) for human part parsing, which is a unified fully convolutional neural network structure that: (1) parses each human instance into detailed parts. (2) predicts the location and scale of human instances and their corresponding parts. In our unified network, the two tasks are mutually beneficial. The score maps obtained for parsing helps estimate the locations and scales for human instances and their parts. With the predicted locations and scales, our model zooms the region into a right scale to further refine the parsing. In practice, we perform the two tasks iteratively so that detailed human parts are gradually recovered. We conduct extensive experiments over the challenging PASCAL-Person-Part segmentation, and show our approach significantly outperforms the state-of-the-art parsing techniques especially at small scale instances and parts, which demonstrate the effectiveness of our network.

本文介绍了一种基于自动缩放卷积神经网络的解析物体部分的方法并在人、马和牛的PASCAL数据集上进行了实验，结果表明本文提出的策略先缩放到对象，然后再缩放到部件非常有效，能更好地适应不同区域的图像，并且能够在人类、牛类和马类等数据集中获得良好的图像分割结果。

Zoom Better to See Clearer: 基于分层自适应缩放网络的人体和物体解析