Papers › Explicit Shape Encoding for Real-Time Instance Segmentation

Explicit Shape Encoding for Real-Time Instance Segmentation

12 Aug 2019ICCV 2019 10arXiv:1908.04067archive 2025-07-28

Wenqiang Xu, Haiyang Wang, Fubo Qi, Cewu Lu

In this paper, we propose a novel top-down instance segmentation framework based on explicit shape encoding, named \textbf{ESE-Seg}. It largely reduces the computational consumption of the instance segmentation by explicitly decoding the multiple object shapes with tensor operations, thus performs the instance segmentation at almost the same speed as the object detection. ESE-Seg is based on a novel shape signature Inner-center Radius (IR), Chebyshev polynomial fitting and the strong modern object detectors. ESE-Seg with YOLOv3 outperforms the Mask R-CNN on Pascal VOC 2012 at mAPʳ@0.5 while 7 times faster.

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Tasks

Instance Segmentation Object Object Detection Real-time Instance Segmentation Segmentation Semantic Segmentation object-detection

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1x1 Convolution Average Pooling Batch Normalization Convolution Global Average Pooling Logistic Regression Mask R-CNN RPN Residual Connection RoIAlign SPEED Softmax YOLOv3 k-Means Clustering

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