论文阅读
1.Visual-Semantic Graph Attention Network for Human-Object Interaction Detection
用于人机交互检测的视觉语义图注意网络
作者
Zhijun Liang, Yisheng Guan, and Juan Rojas
单位
Guangdong University of Technonlogy
数据集
HICO-DET，COCO
摘要：
In scene understanding, machines benefit from not only detecting individual scene instances but also from learning their possible interactions. Human-Object Interaction (HOI) Detection tries to infer the predicate on a <subject,predicate,object> triplet. Contextual information has been found critical in inferring interactions. However, most works use features from single object instances that have a direct relation with the subject. Few works have studied the disambiguating contribution of subsidiary relations in addition to how attention might leverage them for inference. We contribute a dual-graph attention network that aggregates contextual visual, spatial, and semantic information dynamically for primary subject-object relations as well as subsidiary relations. Graph attention networks dynamically leverage node neighborhood information. Our network uses attention to first leverage visual-spatial and semantic cues from primary and subsidiary relations independently and then combines them before a final readout step. Our network learns to use primary and subsidiary relations to improve inference: encouraging the right interpretations and discouraging incorrect ones. We call our model: Visual-Semantic Graph Attention Networks (VSGATs). We surpass state-of-the-art HOI detection mAPs in the challenging HICO-DET dataset, including in long-tail
cases that are harder to interpret. Code, video, and supplementary information will be made available.
贡献
1. 提出了双注意力图网络
2. 在HICO-det数据集上已经达到了最好的性能
FUll(600) 19.66 (第一)
Rare(138) 15.79 (第一)
Non-Rare(462) 20.81 (第二)
来自
https://arxiv.org/pdf/2001.02302.pdf
网友评论