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Domain Knowledge Graph Embedding for Zero-Shot Human-Object Interaction Detection
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AI
DOI：10.1587/transinf.2025EDL8052.png)
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Abstract
En 中文
As a downstream task of visual entity and relationship extraction, human-object interaction detection focuses on complex relationships centered around humans as the primary subject. This has significant potential for application in some labour-intensive industries such as construction engineering. However, the data in these contexts often display a long-tailed distribution, featuring numerous unknown entities and relationships that are not present in standard datasets. This phenomenon places considerable demands on the model's zero-shot learning capabilities. To tackle this challenge, this letter proposed an end-to-end human-object interaction detection method that utilized domain knowledge graph embeddings as part of prior queries for the decoders. In the case study, this method achieved a mean Average Precision (mAP) of 48.57% for the Full types across various scenarios. Specifically, the Rare types achieved a mAP of 52.45%, while the Non-Rare types achieved a mAP of 41.67%.
Keywords:
human-object interaction detection
domain knowledge graph
graph embedding
zero-shot learning
Journal
I
IF:
0.8
Papers:
171
Citations:
2.3K