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.

在本文中，我们提出了一个情感丰富的轻量级网络 SeLiNet 和一个端到端的设备上的上下文情感识别流程，SeLiNet 模型由主体特征提取器、图像美学特征提取器和学习融合网络组成，共同估计离散情绪和人类情感任务。在 EMOTIC 数据集上，所提出的方法在与基准 AP 得分 27.38 相比下取得了 27.17 的平均准确率（AP）得分，同时模型大小缩小了 85% 以上。此外，与基准相比，我们在设备上报告了 26.42 的 AP 得分，模型大小缩小了 93% 以上。

SeLiNet: 在图像情感识别中增强情感的轻量级网络

SeLiNet: Sentiment enriched Lightweight Network for Emotion Recognition  in Images

Emotion regulation is a crucial element in dealing with emotional events and
has positive effects on mental health. This paper aims to provide a more
comprehensive understanding of emotional events by introducing a new French
corpus of emotional narratives collected using a questionnaire for emotion
regulation. We follow the theoretical framework of the Component Process Model
which considers emotions as dynamic processes composed of four interrelated
components (behavior, feeling, thinking and territory). Each narrative is
related to a discrete emotion and is structured based on all emotion components
by the writers. We study the interaction of components and their impact on
emotion classification with machine learning methods and pre-trained language
models. Our results show that each component improves prediction performance,
and that the best results are achieved by jointly considering all components.
Our results also show the effectiveness of pre-trained language models in
predicting discrete emotion from certain components, which reveal differences
in how emotion components are expressed.

本研究使用新的情感叙事法和机器学习方法，基于 Component Process Model 的理论框架，研究了由行为、感觉、思考和场域组成的情感构成，证明了每个情感组成都对情感分类有积极影响，同时考虑所有情感组成的综合表现是最佳的，利用预训练语言模型来预测离散情感是有效的，揭示了情感组成表达方式的不同之处。