论文标题
人物形象产生具有人重新识别的语义注意网络
Person image generation with semantic attention network for person re-identification
论文作者
论文摘要
姿势变化是阻止网络学习强大的人重新识别（RE-ID）模型的关键因素之一。为了解决这个问题，我们提出了一种新颖的人姿势指导的图像生成方法，该方法称为语义注意网络。该网络由几个语义注意块组成，每个块都会参加以保存和更新姿势代码和服装纹理。引入二进制分割掩码和语义解析对于无缝缝合前景和姿势引导图像生成中的背景很重要。与其他方法相比，我们的网络可以同时表征更好的身体形状并保持衣服属性。我们的合成图像可以获得与原始图像相关的更好的外观和形状一致性。实验结果表明，我们的方法在市场1501和Deepfashion上的定量和定性结果方面具有竞争力。此外，我们通过使用人员重新识别（RE-ID）系统进行大量评估，该系统接受了基于姿势转让的人的增强数据。该实验表明，我们的方法可以显着提高人的重新准确性。
Pose variation is one of the key factors which prevents the network from learning a robust person re-identification (Re-ID) model. To address this issue, we propose a novel person pose-guided image generation method, which is called the semantic attention network. The network consists of several semantic attention blocks, where each block attends to preserve and update the pose code and the clothing textures. The introduction of the binary segmentation mask and the semantic parsing is important for seamlessly stitching foreground and background in the pose-guided image generation. Compared with other methods, our network can characterize better body shape and keep clothing attributes, simultaneously. Our synthesized image can obtain better appearance and shape consistency related to the original image. Experimental results show that our approach is competitive with respect to both quantitative and qualitative results on Market-1501 and DeepFashion. Furthermore, we conduct extensive evaluations by using person re-identification (Re-ID) systems trained with the pose-transferred person based augmented data. The experiment shows that our approach can significantly enhance the person Re-ID accuracy.
