.png){kind=icon}
.png){kind=icon}
Return
Unbiased scene graph generation using the self-distillation method
.png){kind=icon}
PRE
AI
DOI：10.1007/s00371-023-02924-9.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Scene graph generation (SGG) aims to build a structural representation for the image with the object instance and the relations between object pairs. Due to the long-tail distribution of the dataset labeling, scene graph generation models must adopt the debiasing method during the learning process. In this paper, we propose to integrating a novel self-distillation method into the existing SGG models and the experimental results have shown competitive debiasing performance. Further analysis of its effectiveness with causal inference theory has indicated that our method can be considered as a new intervention method.
Keywords:
Scene graph generation
Long-tail
Self-distillation
Causal inference