Recovering the Unbiased Scene Graphs from the Biased Ones

Abstract

Given input images, scene graph generation (SGG) aims to produce comprehensive, graphical representations describing visual relationships among salient objects. Recently, more efforts have been paid to the long tail problem in SGG; however, the imbalance in the fraction of missing labels of different classes, or reporting bias, exacerbating the long tail is rarely considered and cannot be solved by the existing debiasing methods. In this paper we show that, due to the missing labels, SGG can be viewed as a "Learning from Positive and Unlabeled data" (PU learning) problem, where the reporting bias can be removed by recovering the unbiased probabilities from the biased ones by utilizing label frequencies, i.e., the per-class fraction of labeled, positive examples in all the positive examples. To obtain accurate label frequency estimates, we propose Dynamic Label Frequency Estimation (DLFE) to take advantage of training-time data augmentation and average over multiple training iterations to introduce more valid examples. Extensive experiments show that DLFE is more effective in estimating label frequencies than a naive variant of the traditional estimate, and DLFE significantly alleviates the long tail and achieves state-of-the-art debiasing performance on the VG dataset. We also show qualitatively that SGG models with DLFE produce prominently more balanced and unbiased scene graphs.

Accepted by ACMMM 2021. Source code will be available at https://github.com/coldmanck/recovering-unbiased-scene-graphs

References in corpus (9)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Visual Relationship Detection with Language Priors
• Large-Scale Long-Tailed Recognition in an Open World
• Scene Graph Generation with External Knowledge and Image Reconstruction
• ST-HOI: A Spatial-Temporal Baseline for Human-Object Interaction Detection in Videos
• Tackling the Unannotated: Scene Graph Generation with Bias-Reduced Models
• Learning Visual Commonsense for Robust Scene Graph Generation
• Sketching Image Gist: Human-Mimetic Hierarchical Scene Graph Generation
• Learning from the Scene and Borrowing from the Rich: Tackling the Long Tail in Scene Graph Generation