Consistent-Separable Feature Representation for Semantic Segmentation | |
He XJ(何兴建)1,2; Liu J(刘静)1,2; Fu J(付君)1; Wang JQ(王金桥)1; Lu HQ(卢汉清)1,2 | |
2021-05-08 | |
会议日期 | 2021-2-2 |
会议地点 | online |
关键词 | Consistent-Separable Feature Class-Aware Consistency loss Semantic Segmentation |
卷号 | 35 |
期号 | 2 |
页码 | 1531-1539 |
英文摘要 | Cross-entropy loss combined with softmax is one of the most commonly used supervision components in most existing segmentation methods. The softmax loss is typically good at optimizing the inter-class difference, but not good at reducing the intra-class variation, which can be suboptimal for semantic segmentation task. In this paper, we propose a Consistent-Separable Feature Representation Network to model the Consistent-Separable (C-S) features, which are intra-class consistent and inter-class separable, improving the discriminative power of the deep features. Specifically, we develop a Consistent-Separable Feature Learning Module to obtain C-S features through a new loss, called Class-Aware Consistency loss. This loss function is proposed to force the deep features to be consistent among the same class and apart between different classes. Moreover, we design an Adaptive feature Aggregation Module to fuse the C-S features and original features from backbone for the better semantic prediction. We show that compared with various baselines, the proposed method brings consistent performance improvement. Our proposed approach achieves state-of-the-art performance on Cityscapes (82.6% mIoU in test set), ADE20K (46.65% mIoU in validation set), COCO Stuff (41.3% mIoU in validation set) and PASCAL Context (55.9% mIoU in test set). |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/48887] |
专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
通讯作者 | Liu J(刘静) |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | He XJ,Liu J,Fu J,et al. Consistent-Separable Feature Representation for Semantic Segmentation[C]. 见:. online. 2021-2-2. |
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