4.7 Article

Human-centered deep compositional model for handling occlusions

Journal

PATTERN RECOGNITION
Volume 138, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2023.109397

Keywords

Convolutional neural networks; Hierarchical compositonal model; Instance segmentation; Occlusion handling; Discriminability; Generalizability; Interpretability; Domain knowledge

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This study proposes a Human-Centered Deep Compositional model (HCDC) that combines the visual discrimination of a CNN and the reasoning of a Hierarchical Compositional model (HCM). The experimental results show that the HCDC model outperforms the current state-of-the-art Mask-RCNN in instance segmentation tasks, with higher discriminative and generative power, as well as better occlusion handling.
Despite their powerful discriminative abilities, Convolutional Neural Networks (CNNs) lack the properties of generative models. This leads to a decreased performance in environments where objects are poorly visible. Solving such a problem by adding more training samples can quickly lead to a combinatorial ex-plosion, therefore the underlying architecture has to be changed instead. This work proposes a Human -Centered Deep Compositional model (HCDC) that combines low-level visual discrimination of a CNN and the high-level reasoning of a Hierarchical Compositional model (HCM). Defined as a transparent model, it can be optimized to real-world environments by adding compactly encoded domain knowledge from hu-man studies and physical laws. The new FridgeNetv2 dataset and a mixture of publicly available datasets are used as a benchmark. The experimental results show the proposed model is explainable, has higher discriminative and generative power, and better handles the occlusion than the current state-of-the-art Mask-RCNN in instance segmentation tasks. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

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