A Recurrent and Meta-learned Model of Weakly Supervised Object Localization

Date

2022-01-01

Author

Sariyildiz, Mert Bulent
Sumbul, Gencer
Cinbiş, Ramazan Gökberk

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The object localization and detection has improved greatly over the past decade, thanks to developments in deep learning based representations and localization models. However, a major bottleneck remains at the reliance on fully-supervised datasets, which can be difficult to gather in many real-world scenarios. In this work, we focus on the problem of weakly-supervised localization, where the goal is to localize instances of objects based on simple image-level class annotations. In particular, instead of engineering a specific weakly-supervised localization model, we aim to meta-learn a recurrent neural network based model that aims to take series of training images of a novel class, and progressively discover the foreground pattern over them. We experimentally explore the model over scenes composed of MNIST digits and noisy patches as distractors.

Subject Keywords

meta learning, recurrent neural networks, Weakly supervised object localization

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85142794359&origin=inward
https://hdl.handle.net/11511/101546

DOI

https://doi.org/10.1109/ismsit56059.2022.9932735

Conference Name

6th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

M. B. Sariyildiz, G. Sumbul, and R. G. Cinbiş, “A Recurrent and Meta-learned Model of Weakly Supervised Object Localization,” presented at the 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022, Ankara, Türkiye, 2022, Accessed: 00, 2023. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85142794359&origin=inward.