RcSGG samples relationship features based on training losses and object-pair diversity to reduce head-tail and foreground-background biases in scene graph generation, achieving state-of-the-art mean recall on four benchmarks.
Peer Learning for Unbiased Scene Graph Generation
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abstract
Unbiased scene graph generation (USGG) is a challenging task that requires predicting diverse and heavily imbalanced predicates between objects in an image. To address this, we propose a novel framework peer learning that uses predicate sampling and consensus voting (PSCV) to encourage multiple peers to learn from each other. Predicate sampling divides the predicate classes into sub-distributions based on frequency, and assigns different peers to handle each sub-distribution or combinations of them. Consensus voting ensembles the peers' complementary predicate knowledge by emphasizing majority opinion and diminishing minority opinion. Experiments on Visual Genome show that PSCV outperforms previous methods and achieves a new state-of-the-art on SGCls task with 31.6 mean.
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A Reverse Causal Framework to Mitigate Spurious Correlations for Debiasing Scene Graph Generation
RcSGG samples relationship features based on training losses and object-pair diversity to reduce head-tail and foreground-background biases in scene graph generation, achieving state-of-the-art mean recall on four benchmarks.