Debiased Scene Graph Generation for Dual Imbalance Learning - Zhou, Hao (IEEE transactions on pattern analysis and machine intelligence, 2023)
Scene graph generation (SGG) is one of the hottest topics in computer vision and has attracted many interests since it provides rich semantic information between objects. In practice, the SGG datasets are often dual imbalanced, presented as a large number of backgrounds and rarely few foregrounds, and highly skewed foreground relationships categories (i.e., the long-tailed distribution). How to tackle this dual imbalanced problem is crucial but rarely studied in literature. Existing methods only consider the long-tailed distribution of foregrounds classes and ignore the background-foreground imbalance in SGG, which results in a biased model and prevents it from being applied in the downstream tasks widely. To reduce its side effect and make the contributions of different categories equally, we propose a novel debiased SGG method (named DSDI) by incorporating biased resistance loss and causal intervention tree. We first deeply analyze the potential causes of dual imbalanced problem in SGG. Then, to learn more discriminate representation of the foreground by expanding the foreground features space, the biased resistance loss decouples the background classification from foreground relationship recognition. Meanwhile, a causal graph of content and context is designed to remove the context bias and learn unbiased relationship features via casual intervention tree. Extensive experimental results on two extremely imbalanced datasets: VG150 and VrR-VG, demonstrate our DSDI outperforms other state-of-the-art methods. All our models will be available in https://github.com/zhouhao0515/unbiasedSGG-DSDI