
In recent years, convolutional neural networks have achieved rapid development in the field of object detection. However, due to the imbalance of data, high costs in labor and uneven level of data labeling, the overall performance of the previous detection network has dropped sharply when dataset extended to the large-scale with hundreds and thousands of categories. The embedding of external semantic knowledge can bring new additional information to reduce the cost of annotation and improve the performance of large-scale detection. In this paper, the Adaptive Word Embedding Module is proposed to extract the adaptive semantic knowledge graph under the constraint of the semantic consistency. Our method endows the ability to infer global semantic of detection networks without other attribute or relationship annotations. Compared with Faster RCNN, the algorithm on the MSCOCO dataset was significantly improved by 4.1%, and the mAP value has reached 32.8%. On the VG1000 dataset, it increased by 0.9% to 6.7% compared with Faster RCNN. Adaptive Word Embedding Module is lightweight, general-purpose and can be plugged into diverse detection networks.

Adaptive Word Embedding Module for Semantic Reasoning in Large-scale Detection

**Session Chair**: prof. Silvia Cascianelli

PS T3.8: Computer Vision and Intelligent Sensing

poster

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
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Adaptive Word Embedding Module for Semantic Reasoning in Large-scale Detection

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