"In July 2022, PaddleSeg upgraded PP-HumanSeg to PP-HumanSegV2, providing new portrait segmentation solution which refreshed the SOTA indicator of the open-source portrait segmentation solutions with 96.63% mIoU accuracy and 63FPS mobile inference speed. Compared with the V1 solution, the inference speed is increased by 87.15%, the segmentation accuracy is increased by 3.03%, and the visualization effect is better. The PP-HumanSegV2 is comparable to the commercial solutions!\n",
"\n",
"PP-HumanSeg is officially produced by PaddlePaddle and proposed by PaddleSeg team. More information about PaddleSeg can be found here https://github.com/PaddlePaddle/PaddleSeg."
"PP-HumanSeg is officially produced by PaddlePaddle and proposed by PaddleSeg team. More information about PaddleSeg can be found here [https://github.com/PaddlePaddle/PaddleSeg](https://github.com/PaddlePaddle/PaddleSeg)."
"On the Cityscapes test set, PP-LiteSeg achieves 72.0% mIoU/273.6 FPS and 77.5% mIoU/102.6 FPS on NVIDIA GTX 1080Ti. PP-LiteSeg achieves a superior tradeoff between accuracy and speed compared to other methods.\n",
"\n",
"PP-LiteSeg model is officially produced by PaddlePaddle and is a SOTA model proposed by PaddleSeg. More information about PaddleSeg can be found here https://github.com/PaddlePaddle/PaddleSeg."
"PP-LiteSeg model is officially produced by PaddlePaddle and is a SOTA model proposed by PaddleSeg. More information about PaddleSeg can be found here [https://github.com/PaddlePaddle/PaddleSeg](https://github.com/PaddlePaddle/PaddleSeg)."
" author={Chen, Guowei and Liu, Yi and Wang, Jian and Peng, Juncai and Hao, Yuying and Chu, Lutao and Tang, Shiyu and Wu, Zewu and Chen, Zeyu and Yu, Zhiliang and others},\n",
"In many image matting algorithms, in order to pursue precision, trimap is often provided as auxiliary information, but this greatly limits the application of the algorithm. PP-Matting, as a trimap-free image matting method, overcomes the disadvantages of auxiliary information and achieves SOTA performance in Composition-1k and Distinctions-646 datasets. PP-Matting uses Semantic Context Branch (SCB) to extract high-level semantic information of images and gradually guides high-resolution detail branch (HRDB) to extract details in transition area through Guidance Flow. Finally, alpha matte is obtained by fusing semantic map and detail map with fusion module.\n",
"\n",
"More details can be found in the paper: https://arxiv.org/abs/2204.09433.\n",
"More details can be found in the paper: [https://arxiv.org/abs/2204.09433](https://arxiv.org/abs/2204.09433).\n",
"\n",
"More about PaddleMatting，you can click https://github.com/PaddlePaddle/PaddleSeg/tree/develop/Matting to learn.\n",
"More about PaddleMatting，you can click [https://github.com/PaddlePaddle/PaddleSeg/tree/develop/Matting](https://github.com/PaddlePaddle/PaddleSeg/tree/develop/Matting) to learn.\n",
" author={Chen, Guowei and Liu, Yi and Wang, Jian and Peng, Juncai and Hao, Yuying and Chu, Lutao and Tang, Shiyu and Wu, Zewu and Chen, Zeyu and Yu, Zhiliang and others},\n",
