@inproceedings{a1e0061cba4c4ff7baa51681fca2e4cb,
title = "FASSST: Fast Attention Based Single-Stage Segmentation Net for Real-Time Instance Segmentation",
abstract = "Real-time instance segmentation is crucial in various AI applications. This work designs a network named Fast Attention based Single-Stage Segmentation NeT (FASSST) that performs instance segmentation with video-grade speed. Using an instance attention module (IAM), FASSST quickly locates target instances and segments with region of interest (ROI) feature fusion (RFF) aggregating ROI features from pyramid mask layers. The module employs an efficient single-stage feature regression, straight from features to instance coordinates and class probabilities. Experiments on COCO and CityScapes datasets show that FASSST achieves state-of-the-art performance under competitive accuracy: real-time inference of 47.5FPS on a GTX1080Ti GPU and 5.3FPS on a Jetson Xavier NX board with only 71.6 GFLOPs.",
keywords = "Grouping and Shape, Large-scale Vision Applications Segmentation",
author = "Yuan Cheng and Rui Lin and Peining Zhen and Tianshu Hou and Ng, \{Chiu Wa\} and Chen, \{Hai Bao\} and Hao Yu and Ngai Wong",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 22nd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022 ; Conference date: 04-01-2022 Through 08-01-2022",
year = "2022",
doi = "10.1109/WACV51458.2022.00277",
language = "English",
series = "Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022",
pages = "2714--2722",
booktitle = "Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022",
}