This paper describes an unsupervised sequential auto-encoding model targeting multi-object scenes. The proposed model uses an attention-based formulation, with reconstruction-driven losses. The main model relies on iteratively writing regions onto a canvas, in a differentiable manner. To enforce attention to objects and/or parts, the model uses a convolutional localization network, a region level bottleneck auto-encoder and a loss term that encourages reconstruction within a limited number of iterations. An extended version of the model incorporates a background modeling component that aims at handling scenes with complex backgrounds. The model is evaluated on two separate datasets: a synthetic dataset that is constructed by composing MNIST digit instances together, and the MS-COCO dataset. The model achieves high reconstruction ability on MNIST based scenes. The extended model shows promising results on the complex and challenging MS-COCO scenes.
TUBITAK
116E445
116E445
Primary Language | English |
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Subjects | Engineering |
Journal Section | Tasarım ve Teknoloji |
Authors | |
Project Number | 116E445 |
Publication Date | December 30, 2022 |
Submission Date | July 2, 2022 |
Published in Issue | Year 2022 Volume: 10 Issue: 4 |