| # CondInst | |
| > [CondInst: Conditional Convolutions for Instance | |
| > Segmentation](https://arxiv.org/pdf/2003.05664.pdf) | |
| <!-- [ALGORITHM] --> | |
| ## Abstract | |
| 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. | |
| <div align=center> | |
| <img src="https://user-images.githubusercontent.com/57584090/203303488-3dbc36da-09a6-4dc8-be9d-d9af27bd1234.png"/> | |
| </div> | |
| ## Results and Models | |
| | Backbone | Style | MS train | Lr schd | bbox AP | mask AP | Config | Download | | |
| | :------: | :-----: | :------: | :-----: | :-----: | :-----: | :-------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | |
| | R-50 | pytorch | Y | 1x | 39.8 | 36.0 | [config](./condinst_r50_fpn_ms-poly-90k_coco_instance.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/condinst/condinst_r50_fpn_ms-poly-90k_coco_instance/condinst_r50_fpn_ms-poly-90k_coco_instance_20221129_125223-4c186406.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/condinst/condinst_r50_fpn_ms-poly-90k_coco_instance/condinst_r50_fpn_ms-poly-90k_coco_instance_20221129_125223.json) | | |
| ## Citation | |
| ```latex | |
| @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} | |
| } | |
| ``` | |