GA-DAN models cross-domain shifts in geometry and appearance spaces with multi-modal spatial learning and disentangled cycle-consistency loss, yielding superior scene text detection and recognition performance on adapted images.
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2019 2verdicts
UNVERDICTED 2representative citing papers
An iterative multi-task GAN-based framework completes occluded vehicle segmentation masks and recovers invisible appearance using coupled discriminators, a 3D silhouette pool, and a shared two-path network, outperforming prior methods on a new synthetic-plus-real dataset.
citing papers explorer
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GA-DAN: Geometry-Aware Domain Adaptation Network for Scene Text Detection and Recognition
GA-DAN models cross-domain shifts in geometry and appearance spaces with multi-modal spatial learning and disentangled cycle-consistency loss, yielding superior scene text detection and recognition performance on adapted images.
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Visualizing the Invisible: Occluded Vehicle Segmentation and Recovery
An iterative multi-task GAN-based framework completes occluded vehicle segmentation masks and recovers invisible appearance using coupled discriminators, a 3D silhouette pool, and a shared two-path network, outperforming prior methods on a new synthetic-plus-real dataset.