| Paper ID | IVMSP-29.4 |
| Paper Title |
AGGREGATION ARCHITECTURE AND ALL-TO-ONE NETWORK FOR REAL-TIME SEMANTIC SEGMENTATION |
| Authors |
Kuntao Cao, Xi Huang, Jie Shao, University of Electronic Science and Technology of China, China |
| Session | IVMSP-29: Semantic Segmentation |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 13:00 - 13:45 |
| Presentation Time: | Friday, 11 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Image, Video, and Multidimensional Signal Processing: [IVARS] Image & Video Analysis, Synthesis, and Retrieval |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Deep convolutional neural network has demonstrated its outstanding performance in the field of image semantic segmentation. However, the enormous computational complexity of existing high-precision networks limits the application of the model in real-time segmentation tasks. How to achieve a good trade-off between accuracy and speed becomes a challenge. Existing solutions can be roughly divided into three categories according to the network architecture: dilation, encoder-decoder, and multi-pathway, each of which has its advantages. In this paper, we make the following contributions: (i) First, unlike the previous three architectures, we propose a new aggregation architecture as the network backbone. (ii) Second, a multi-level auxiliary loss design model is used for the training phase, which can improve the model segmentation effect. (iii) According to this aggregation structure, an all-to-one network (ATONet) for real-time semantic segmentation is proposed, which achieves a good trade-off between speed and accuracy by assembling the features of all blocks. (iv) Finally, the proposed network achieves the accuracy of 74.4% and 70.1% mIoU with the inference speed of 42.7 FPS and 93.5 FPS on the Cityscapes and CamVid datasets. |
