Dilated SpineNet for Semantic Segmentation

• URL: http://arxiv.org/abs/2103.12270v1
• Date: Tue, 23 Mar 2021 02:39:04 GMT
• Title: Dilated SpineNet for Semantic Segmentation
• Authors: Abdullah Rashwan and Xianzhi Du and Xiaoqi Yin and Jing Li
• Abstract summary: Scale-permuted networks have shown promising results on object bounding box detection and instance segmentation. In this work, we evaluate this meta-architecture design on semantic segmentation. We propose SpineNet-Seg, a network discovered by NAS that is searched from the DeepLabv3 system.
• Score: 5.6590540986523035
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scale-permuted networks have shown promising results on object bounding box detection and instance segmentation. Scale permutation and cross-scale fusion of features enable the network to capture multi-scale semantics while preserving spatial resolution. In this work, we evaluate this meta-architecture design on semantic segmentation - another vision task that benefits from high spatial resolution and multi-scale feature fusion at different network stages. By further leveraging dilated convolution operations, we propose SpineNet-Seg, a network discovered by NAS that is searched from the DeepLabv3 system. SpineNet-Seg is designed with a better scale-permuted network topology with customized dilation ratios per block on a semantic segmentation task. SpineNet-Seg models outperform the DeepLabv3/v3+ baselines at all model scales on multiple popular benchmarks in speed and accuracy. In particular, our SpineNet-S143+ model achieves the new state-of-the-art on the popular Cityscapes benchmark at 83.04% mIoU and attained strong performance on the PASCAL VOC2012 benchmark at 85.56% mIoU. SpineNet-Seg models also show promising results on a challenging Street View segmentation dataset. Code and checkpoints will be open-sourced.

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