Enhancing object recognition: The role of object knowledge decomposition and component-labeled datasets

Abstract

Deep learning models’ decision-making processes can be elusive, often raising concerns about their reliability. To address this, we have introduced the Object Knowledge Decomposition and Components Label Dataset (OKD-CL), designed to improve the interpretability and accuracy of object recognition models. This dataset includes 99 categories from PartImageNet, each detailed with clear physical structures that align with human visual concepts. In a hierarchical structure, every category is described by Abstract Component Knowledge (ACK) descriptions and each image instance comes with Explicit Visual Knowledge (EVK) masks, highlighting the visual components’ appearance. By evaluating multiple deep neural networks guided with ACK and EVK (dual-knowledge-guidance approach), we saw better accuracy and a higher Foreground Reasoning Ratio (FRR), confirming our knowledge-guided method's effectiveness. When used on the Hard-ImageNet dataset, this approach reduced the model's reliance on incorrect feature assumptions without sacrificing classification accuracy. This hierarchical comprehension encouraged by OKD-CL is crucial in minimizing incorrect feature associations and strengthening model robustness. The entire code and dataset are available on: https://github.com/XiGuaBo/OKD-CL.

RAS ID

76458

Document Type

Journal Article

Date of Publication

2-7-2025

Volume

617

Funding Information

Natural Science Foundation of Shaanxi Province, China (2024JC-JCQN-66) / National Natural Science Foundation of China (62072358, 62073252)

School

School of Science

Copyright

subscription content

Publisher

Elsevier

Identifier

Syed Afaq Ali Shah: https://orcid.org/0000-0003-2181-8445

Comments

Xiong, N., Wang, N., Li, H., Zhu, G., Zhang, L., Shah, S. A. A., & Bennamoun, M. (2025). Enhancing object recognition: The role of object knowledge decomposition and component-labeled datasets. Neurocomputing, 617. https://doi.org/10.1016/j.neucom.2024.128969

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Link to publisher version (DOI)

10.1016/j.neucom.2024.128969