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- W4313483447 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." @default.
- W4313483447 created "2023-01-06" @default.
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- W4313483447 date "2022-12-31" @default.
- W4313483447 modified "2023-09-25" @default.
- W4313483447 title "Peer Learning for Unbiased Scene Graph Generation" @default.
- W4313483447 doi "https://doi.org/10.48550/arxiv.2301.00146" @default.
- W4313483447 hasPublicationYear "2022" @default.
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