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Anno-incomplete Multi-dataset Detection by Xu, Yiran; Zhong, Haoxiang; Wu, Kai; Li, Jialin; Liu, Yong; Wang, Chengjie; Xia, Shu-Tao; Liao, Hongen is a scholarly article available to read on EtoBox.

What is Anno-incomplete Multi-dataset Detection about?

Object detectors have shown outstanding performance on various public datasets. However, annotating a new dataset for a new task is usually unavoidable in real, since 1) a single existing dataset usually does not contain all object categories needed; 2) using multiple datasets usually suffers from annotation incompletion and heterogeneous features. We propose a novel problem as "Annotation-incomplete Multi-dataset Detection", and develop an end-to-end multi-task learning architecture which can accurately detect all the object categories with multiple partially annotated datasets. Specifically, we propose an attention feature extractor which helps to mine the relations among different datasets. Besides, a knowledge amalgamation training strategy is incorporated to accommodate heterogeneous features from different sources. Extensive experiments on different object detection datasets demonstrate the effectiveness of our methods and an improvement of 2.17%, 2.10% in mAP can be achieved on COCO and VOC respectively.

Author

Xu, Yiran; Zhong, Haoxiang; Wu, Kai; Li, Jialin; Liu, Yong; Wang, Chengjie; Xia, Shu-Tao; Liao, Hongen

Published

2024

Language

EN