Multi-grained interpretable network for image recognition

Peiyu Yang, Zeyi Wen, Ajmal Mian

Research output: Chapter in Book/Conference paperConference paperpeer-review


Given a classification problem with a large number of classes, humans often compare features at different granularities from coarse to fine to gradually recognize an object. However, current deep models are generally trained to directly make the final prediction, focusing on improving the ability of the network to extract features without considering the interpretability of the model. In this paper, we propose a multi-grained interpretable network to imitate the reasoning process of humans. The proposed network is equipped with techniques to assign images with multi-grained labels, so as to train a tree-structured classifier that learns features at different levels of granularity. The proposed method can hierarchically classify objects in images at different granularities, while providing a decision pathway with multi-grained explanations for practitioners. Experimental results demonstrate that our method achieves competitive prediction accuracy on CUB-200-2011 and Stanford Cars datasets, and simultaneously produces high-quality explanations of its decisions. Moreover, our method shows higher robustness of the learned features to adversarial examples generated by the FGSM and PGD attacks.
Original languageEnglish
Title of host publication2022 26th International Conference on Pattern Recognition, ICPR 2022
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages7
ISBN (Electronic)9781665490627
Publication statusPublished - 2022
Event26th International Conference on Pattern Recognition, ICPR 2022 - Montreal, Canada
Duration: 21 Aug 202225 Aug 2022

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Conference26th International Conference on Pattern Recognition, ICPR 2022


Dive into the research topics of 'Multi-grained interpretable network for image recognition'. Together they form a unique fingerprint.

Cite this