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- W3178679506 abstract "When translating text inputs into layouts or images, existing works typically require explicit descriptions of each object in a scene, including their spatial information or the associated relationships. To better exploit the text input, so that implicit objects or relationships can be properly inferred during layout generation, we propose a LayoutTransformer Network (LT-Net) in this paper. Given a scene-graph input, our LT-Net uniquely encodes the semantic features for exploiting their co-occurrences and implicit relationships. This allows one to manipulate conceptually diverse yet plausible layout outputs. Moreover, the decoder of our LT-Net translates the encoded contextual features into bounding boxes with self-supervised relation consistency preserved. By fitting their distributions to Gaussian mixture models, spatially-diverse layouts can be additionally produced by LT-Net. We conduct extensive experiments on the datasets of MS-COCO and Visual Genome, and confirm the effectiveness and plausibility of our LT-Net over recent layout generation models. Codes will be released at LayoutTransformer." @default.
- W3178679506 created "2021-07-19" @default.
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- W3178679506 date "2021-06-01" @default.
- W3178679506 modified "2023-10-01" @default.
- W3178679506 title "LayoutTransformer: Scene Layout Generation with Conceptual and Spatial Diversity" @default.
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- W3178679506 doi "https://doi.org/10.1109/cvpr46437.2021.00373" @default.
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