Where Is The Emotion? Dissecting A Multi-Gap Network For Image Emotion Classification

Citation

Lim, Lucinda and Khor, Huai Qian and Chaemchoy, Phatcharawat and See, John Su Yang and Wong, Lai Kuan (2020) Where Is The Emotion? Dissecting A Multi-Gap Network For Image Emotion Classification. In: 2020 IEEE International Conference on Image Processing (ICIP), 25-28 Oct. 2020, Virtual, Abu Dhabi, UAE.

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Abstract

Image emotion recognition has become an increasingly popular research domain in the area of image processing and affective computing. Despite fast-improving classification performance in this task, the understanding and interpretability of its performance are still lacking as there are limited studies on which part of an image would invoke a particular emotion. In this work, we propose a Multi-GAP deep neural network for image emotion classification, which is extensible to accommodate multiple streams of information. We also incorporate feature dependency into our network blocks by adding a bidirectional GRU network to learn transitional features. We report extensive results on the variants of our proposed network and provide valuable perspectives into the class-activated regions via Grad-CAM, and network depth contributions by truncation strategy

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Neural networks (Computer science), Image emotion classification, multi-GAP, CNN, visualizations, class activation maps
Subjects: Q Science > Q Science (General)
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 10 Sep 2021 15:16
Last Modified: 10 Sep 2021 15:16
URII: http://shdl.mmu.edu.my/id/eprint/8519

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