Name
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 LaynaTransformer. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CVPR46437.2021.00373 | 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021 |
DocType | ISSN | Citations |
Conference | 1063-6919 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cheng-Fu Yang | 1 | 0 | 1.01 |
| Wan-Cyuan Fan | 2 | 0 | 1.01 |
| Fu-En Yang | 3 | 12 | 2.60 |
| Yu-Chiang Frank Wang | 4 | 914 | 61.63 |