- Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji
- Vol: 10 Issue: 4
- Attentive Sequential Auto-Encoding Towards Unsupervised Object-centric Scene Modeling
Attentive Sequential Auto-Encoding Towards Unsupervised Object-centric Scene Modeling
Authors : Yarkın Deniz Çetin, Ramazan Gökberk Cinbiş
Pages : 1127-1142
Doi:10.29109/gujsc.1139701
View : 14 | Download : 6
Publication Date : 2022-12-30
Article Type : Research
Abstract :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.Keywords : Unsupervised learning, complex scene modeling, object discovery