TY  - JOUR
T1  - Attentive Sequential Auto-Encoding Towards Unsupervised Object-centric Scene Modeling
AU  - Cinbiş, Ramazan Gökberk
AU  - Çetin, Yarkın Deniz
PY  - 2022
DA  - December
DO  - 10.29109/gujsc.1139701
JF  - Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji
JO  - GUJS Part C
PB  - Gazi Üniversitesi
WT  - DergiPark
SN  - 2147-9526
SP  - 1127
EP  - 1142
VL  - 10
IS  - 4
LA  - en
AB  - 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.
KW  - Unsupervised learning
KW  - complex scene modeling
KW  - object discovery
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UR  - https://doi.org/10.29109/gujsc.1139701
L1  - https://dergipark.org.tr/tr/download/article-file/2522001
ER  -