Real-Time Multi-class Instance Segmentation with One-Time Deep Embedding Clustering
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Journal Volume
12046 LNCS
Pages
223-235
Date Issued
2020
Author(s)
Abstract
In recent years, instance segmentation research has been considered as an extension of object detection and semantic segmentation, which can provide pixel-level annotations on detected objects. Several approaches for instance segmentation exploit object detection network to generate bounding box and segment each bounding box with segmentation network. However, these approaches need more time consumption due to two independent networks as their framework. On the other hand, some approaches based on clustering transform each pixel into unique representation and produce instance mask by postprocessing. Nevertheless, most clustering approaches have to cluster all instances of each class individually, which contribute to additional time consumption. In this research, we propose a fast clustering method called one-time clustering with single network aiming at reducing time consumption on multi-class instance segmentation. Moreover, we present a class-sensitive loss function that allows the network to generate unique embedding which contains class and instance information. With the informative embeddings, we can cluster them only once instead of clustering for each class in other clustering approaches. Our approach is up to 6x faster than the state-of-the-art UPSNet [1], which appeared in CVPR 2019, and get about 25% lower AP performance on Cityscape dataset. It achieves significantly faster speed and great segmentation quality while having an acceptable AP performance. ? Springer Nature Switzerland AG 2020.
Subjects
Deep learning; Embeddings; Image segmentation; Object recognition; Pixels; Semantics; Clustering approach; Clustering methods; Detection networks; One-time; Real time; Segmentation quality; Semantic segmentation; State of the art; Object detection
SDGs
Type
conference paper