Abstract
Cascade is a classic yet powerful architecture that has boosted performance on various tasks. However, how to introduce cascade to instance segmentation remains an open question. A simple combination of Cascade R-CNN and Mask R-CNN only brings limited gain. In exploring a more effective approach, we find that the key to a successful instance segmentation cascade is to fully leverage the reciprocal relationship between detection and segmentation. In this work, we propose a new framework, Hybrid Task Cascade (HTC), which differs in two important aspects: (1) instead of performing cascaded refinement on these two tasks separately, it interweaves them for a joint multi-stage processing; (2) it adopts a fully convolutional branch to provide spatial context, which can help distinguishing hard foreground from cluttered background. Overall, this framework can learn more discriminative features progressively while integrating complementary features together in each stage. Without bells and whistles, a single HTC obtains 38.4 and 1.5 improvement over a strong Cascade Mask R-CNN baseline on MSCOCO dataset. Moreover, our overall system achieves 48.6 mask AP on the test-challenge split, ranking 1st in the COCO 2018 Challenge Object Detection Task. Code is available at: https://github.com/open-mmlab/mmdetection.Results
We won the COCO Challenge 2018 (team: MMDet)
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Application
The winning entry of iMaterialist (Fashion)
2019 at FGVC6 (CVPR 2019 Workshop) is based on
HTC. Here is the post of
the winner.
Citation
@inproceedings{chen2019hybrid,
author = {Kai Chen and Jiangmiao Pang and Jiaqi Wang and Yu Xiong and Xiaoxiao Li
and Shuyang Sun and Wansen Feng and Ziwei Liu and Jianping Shi and
Wanli Ouyang and Chen Change Loy and Dahua Lin},
title = {Hybrid Task Cascade for Instance Segmentation},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
year = {2019}
}