SeLiNet: Sentiment enriched Lightweight Network for Emotion Recognition in Images
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Abstract (arXiv)
In this paper, we propose a sentiment-enriched lightweight network SeLiNet and an end-to-end on-device pipeline for contextual emotion recognition in images. SeLiNet model consists of body feature extractor, image aesthetics feature extractor, and learning-based fusion network which jointly estimates discrete emotion and human sentiments tasks. On the EMOTIC dataset, the proposed approach achieves an Average Precision (AP) score of 27.17 in comparison to the baseline AP score of 27.38 while reducing the model size by >85%. In addition, we report an on-device AP score of 26.42 with reduction in model size by >93% when compared to the baseline.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 40 references that are in the map. First posted on arXiv 6 Jul 2023.
Builds on
9 of 40 references.
References · 9
- 2014Microsoft COCO: Common Objects in Contextcited by 17,498
- 2016Semantic Understanding of Scenes through the ADE20K Datasetcited by 1,063
- 2017NIMA: Neural Image Assessmentcited by 387
- 2019Context-Aware Emotion Recognition Networkscited by 99
- 2020Context Based Emotion Recognition using EMOTIC Datasetcited by 73
- 2020EmotiCon: Context-Aware Multimodal Emotion Recognition using Frege's Principlecited by 50
- 2016Learning Multi-level Deep Representations for Image Emotion Classificationcited by 31
- 2020Adaptive Fractional Dilated Convolution Network for Image Aesthetics Assessmentcited by 28
- 2020Emotion Recognition From Gait Analyses: Current Research and Future Directionscited by 11
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2 papers.