SeLiNet: Sentiment enriched Lightweight Network for Emotion Recognition
  in Images
Tuneer Khargonkar Shwetank Choudhary Sumit Kumar Barath Raj KR
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.