Multi-Scale Feature Aggregation by Cross-Scale Pixel-to-Region Relation Operation for Semantic Segmentation

• URL: http://arxiv.org/abs/2106.01744v1
• Date: Thu, 3 Jun 2021 10:49:48 GMT
• Title: Multi-Scale Feature Aggregation by Cross-Scale Pixel-to-Region Relation Operation for Semantic Segmentation
• Authors: Yechao Bai, Ziyuan Huang, Lyuyu Shen, Hongliang Guo, Marcelo H. Ang Jr and Daniela Rus
• Abstract summary: We aim to enable the low-level feature to aggregate the complementary context from adjacent high-level feature maps by a cross-scale pixel-to-region operation. We employ an efficient feature pyramid network to obtain multi-scale features. Experiment results show that the RSP head performs competitively on both semantic segmentation and panoptic segmentation with high efficiency.
• Score: 44.792859259093085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Exploiting multi-scale features has shown great potential in tackling semantic segmentation problems. The aggregation is commonly done with sum or concatenation (concat) followed by convolutional (conv) layers. However, it fully passes down the high-level context to the following hierarchy without considering their interrelation. In this work, we aim to enable the low-level feature to aggregate the complementary context from adjacent high-level feature maps by a cross-scale pixel-to-region relation operation. We leverage cross-scale context propagation to make the long-range dependency capturable even by the high-resolution low-level features. To this end, we employ an efficient feature pyramid network to obtain multi-scale features. We propose a Relational Semantics Extractor (RSE) and Relational Semantics Propagator (RSP) for context extraction and propagation respectively. Then we stack several RSP into an RSP head to achieve the progressive top-down distribution of the context. Experiment results on two challenging datasets Cityscapes and COCO demonstrate that the RSP head performs competitively on both semantic segmentation and panoptic segmentation with high efficiency. It outperforms DeeplabV3 [1] by 0.7% with 75% fewer FLOPs (multiply-adds) in the semantic segmentation task.

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