适配方法

HTC

介绍

HTC是基于 Mask R-CNN和 Casecade R-CNN、针对目标检测和实例分割任务而设计的一种多任务多阶段混合级联模型。它结合了目标检测和实例分割的特点，通过多个阶段的级联和优化，能够同时准确地检测目标物体并对其进行实例分割。这种方法在处理复杂场景下的实例分割任务时具有较好的性能。

论文引用

@inproceedings{chen2019hybrid,
  title={Hybrid task cascade for instance segmentation},
  author={Chen, Kai and Pang, Jiangmiao and Wang, Jiaqi and Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and Liu, Ziwei and Shi, Jianping and Ouyang, Wanli and others},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={4974--4983},
  year={2019}
}

Mask2former

介绍

Mask2Former是一种基于Transformer的通用分割框架，通过简洁的架构设计和自注意力机制来实现高性能的实例分割目标。

论文引用

@inproceedings{cheng2022masked,
  title={Masked-attention mask transformer for universal image segmentation},
  author={Cheng, Bowen and Misra, Ishan and Schwing, Alexander G and Kirillov, Alexander and Girdhar, Rohit},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={1290--1299},
  year={2022}
}