CamouFinder: Finding Camouflaged Instances in Images

Authors

  • Trung-Nghia Le National Institute of Informatics
  • Vuong Nguyen University of Science VNU-HCM
  • Cong Le University of Science VNU-HCM
  • Tan-Cong Nguyen University of Social Sciences and Humanities VNU-HCM
  • Minh-Triet Tran University of Science VNU-HCM
  • Tam V. Nguyen University of Dayton

Keywords:

Camouflaged Instance Segmentation, Object Segmentation, Instance Segmentation

Abstract

In this paper, we investigate the interesting yet challenging problem of camouflaged instance segmentation. To this end, we first annotate the available CAMO dataset at the instance level. We also embed the data augmentation in order to increase the number of training samples. Then, we train different state-of-the-art instance segmentation on the CAMO-instance data. Last but not least, we develop an interactive user interface which demonstrates the performance of different state-of-the-art instance segmentation methods on the task of camouflaged instance segmentation. The users are able to compare the results of different methods on the given input images. Our work is expected to push the envelope of the camouflage analysis problem.

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Published

2021-05-18

How to Cite

Le, T.-N., Nguyen, V., Le, C., Nguyen, T.-C., Tran, M.-T., & Nguyen, T. V. (2021). CamouFinder: Finding Camouflaged Instances in Images. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 16071-16074. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/18015