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- W4387323818 abstract "Scene graph generation (SGG) aims to automatically map an image into a semantic structural graph for better scene understanding. It has attracted significant attention for its ability to provide object and relation information, enabling graph reasoning for downstream tasks. However, it faces severe limitations in practice due to the biased data and training method. In this paper, we present a more rational and effective strategy based on causal inference for object relation prediction. To further evaluate the superiority of our strategy, we propose an object enhancement module to conduct ablation studies. Experimental results on the Visual Gnome 150 (VG-150) dataset demonstrate the effectiveness of our proposed method. These contributions can provide great potential for foundation models for decision-making." @default.
- W4387323818 created "2023-10-04" @default.
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- W4387323818 date "2023-10-01" @default.
- W4387323818 modified "2023-10-14" @default.
- W4387323818 title "Logical Bias Learning for Object Relation Prediction" @default.
- W4387323818 doi "https://doi.org/10.48550/arxiv.2310.00712" @default.
- W4387323818 hasPublicationYear "2023" @default.
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