Scene graphs are semantic abstraction of images that encourage visual
understanding and reasoning. However, the performance of Scene Graph Generation
(SGG) is unsatisfactory when faced with biased data in real-world scenarios.
Conventional debiasing research mainly studies from the view of balancing data
distribution or learning unbiased models and representations, ignoring the
correlations among the biased classes. In this work, we analyze this problem
from a novel cognition perspective: automatically building a hierarchical
cognitive structure from the biased predictions and navigating that hierarchy
to locate the relationships, making the tail relationships receive more
attention in a coarse-to-fine mode. To this end, we propose a novel debiasing
Cognition Tree (CogTree) loss for unbiased SGG. We first build a cognitive
structure CogTree to organize the relationships based on the prediction of a
biased SGG model. The CogTree distinguishes remarkably different relationships
at first and then focuses on a small portion of easily confused ones. Then, we
propose a debiasing loss specially for this cognitive structure, which supports
coarse-to-fine distinction for the correct relationships. The loss is
model-agnostic and consistently boosting the performance of several
state-of-the-art models. The code is available at:
this https URL

本研究从认知角度研究解决 Scene Graph Generation 领域中偏差数据的问题，提出了一种新的解决方案 —— 建立一种基于偏差预测结果的层次认知结构，通过对结构进行模糊到精细的遍历，实现关注尾部关系的目的。该方案使用 CogTree loss 作为 Debiasing loss，可用于现有多种主流模型的性能优化。