Occlusion Robust Part-aware Object Classification through Part Attention and Redundant Features Suppression

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*All presentations are available in the virtual platform on-demand. The posters will also be exhibited onsite in Hall E, Tokyo International Forum from 15 – 17 December 2021.

Abstract: We propose a part-aware deep learning approach for partial occlusion robust object classification. We train a network without occluded objects in training time and test the network under partial occlusions.

Author(s)/Presenter(s):
Sohee Kim, Kyung Hee University, South Korea
Seungkyu Lee, Kyung Hee University, South Korea

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