CondInst: Conditional Convolutions for Instance Segmentation
We propose a simple yet effective instance segmentation framework, termed CondInst (conditional convolutions for instance segmentation). Top-performing instance segmentation methods such as Mask R-CNN rely on ROI operations (typically ROIPool or ROIAlign) to obtain the final instance masks. In contrast, we propose to solve instance segmentation from a new perspective. Instead of using instancewise ROIs as inputs to a network of fixed weights, we employ dynamic instance-aware networks, conditioned on instances. CondInst enjoys two advantages: 1) Instance segmentation is solved by a fully convolutional network, eliminating the need for ROI cropping and feature alignment. 2) Due to the much improved capacity of dynamically-generated conditional convolutions, the mask head can be very compact (e.g., 3 conv. layers, each having only 8 channels), leading to significantly faster inference. We demonstrate a simpler instance segmentation method that can achieve improved performance in both accuracy and inference speed. On the COCO dataset, we outperform a few recent methods including welltuned Mask R-CNN baselines, without longer training schedules needed.
| Backbone | Style | MS train | Lr schd | bbox AP | mask AP | Config | Download |
|---|---|---|---|---|---|---|---|
| R-50 | pytorch | Y | 1x | 39.8 | 36.0 | config | model | log |
@inproceedings{tian2020conditional,
title = {Conditional Convolutions for Instance Segmentation},
author = {Tian, Zhi and Shen, Chunhua and Chen, Hao},
booktitle = {Proc. Eur. Conf. Computer Vision (ECCV)},
year = {2020}
}