[Faster RCNN](./fluid/PaddleCV/rcnn/README_cn.md)|典型的两阶段目标检测器|创造性地采用卷积网络自行产生建议框,并且和目标检测网络共享卷积网络,建议框数目减少,质量提高|[Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://arxiv.org/abs/1506.01497)
[ICNet](./fluid/PaddleCV/icnet)|图像实时语义分割模型|即考虑了速度,也考虑了准确性,在高分辨率图像的准确性和低复杂度网络的效率之间获得平衡|[ICNet for Real-Time Semantic Segmentation on High-Resolution Images](https://arxiv.org/abs/1704.08545)
[DeepLabv3+](./fluid/PaddleCV/deeplabv3+)|基于编码器解码器结构的语意分割模型|通过编码器解码器进行多尺度信息的融合,同时保留了原来的空洞卷积和空洞空间金字塔池化(ASSP), 其骨干网络使用了Xception模型,降深度可分离的卷积用于ASSP和解码器模块,提高了语义分割的健壮性和运行速率|[Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1802.02611)
[DCGAN](./fluid/PaddleCV/gan/c_gan)|图像生成模型|深度卷积生成对抗网络,将GAN和卷积网络结合起来,以解决GAN训练不稳定的问题|[Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/pdf/1511.06434.pdf)