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[bibtex]@InProceedings{Zhang_2022_ACCV, author = {Zhang, Ruonan and An, Gaoyun}, title = {Causal Property based Anti-Conflict Modeling with Hybrid Data Augmentation for Unbiased Scene Graph Generation}, booktitle = {Proceedings of the Asian Conference on Computer Vision (ACCV)}, month = {December}, year = {2022}, pages = {1556-1572} }
Causal Property based Anti-Conflict Modeling with Hybrid Data Augmentation for Unbiased Scene Graph Generation
Abstract
Scene Graph Generation(SGG) aims to detect visual triplets
of pairwise objects based on object detection. There are three key factors
being explored to determine a scene graph: visual information, local
and global context, and prior knowledge. However, conventional methods
balancing losses among these factors lead to conflict, causing ambiguity,
inaccuracy, and inconsistency. In this work, to apply evidence theory to
scene graph generation, a novel plug-and-play Causal Property based
Anti-conflict Modeling (CPAM) module is proposed, which models key
factors by Dempster-Shafer evidence theory, and integrates quantitative
information effectively. Compared with the existing methods, the proposed
CPAM makes the training process interpretable, and also manages
to cover more fine-grained relationships after inconsistencies reduction.
Furthermore, we propose a Hybrid Data Augmentation (HDA) method,
which facilitates data transfer as well as conventional debiasing methods
to enhance the dataset. By combining CPAM with HDA, significant improvement
has been achieved over the previous state-of-the-art methods.
And extensive ablation studies have also been conducted to demonstrate
the effectiveness of our method.
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