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https://github.com/kahsolt/ew-mtcnn-unofficial

Unofficial implementation of EW-MTCNN for image emotion classification
https://github.com/kahsolt/ew-mtcnn-unofficial

emotion-analysis image-emotion-classification mikels-s-emotion-wheel wheel-of-emotion

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Unofficial implementation of EW-MTCNN for image emotion classification

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# EW-MTCNN-unofficial

Unofficial implementation of EW-MTCNN for image emotion classification

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复现论文: [基于情感轮和多任务卷积神经网络的图像情感分布学习 (2022) 赖金水,万中英,曾雪强](https://lkxb.jxnu.edu.cn/oa/DArticle.aspx?type=view&id=202204006)
这个论文甚至还有后续破事水 [融合情感轮注意力的情感分布学习 (2023) 陈启凡,曾雪强,左家莉,万中英,王明文](http://cea.ceaj.org/CN/10.3778/j.issn.1002-8331.2108-0356);或者看 [CSDN博客解读](https://blog.csdn.net/PLANTTHESON/article/details/132912287)

**结论:能复现出论文数据,但情感轮先验层基本上卵用没用,严重拉低精度还不如没有 🤬**

⚪ 模型结构

![EM-MTCNN](img/EW-MTCNN.png)

⚪ 情感轮先验层权重

![EW_layer](img/EW_layer.png)

⚪ **训练集**精度 (我懒得做五折交叉了)

⚠ inplace eval on trainset :(

| `-I` | `-M` | cls_acc ↑ | sdl_kl ↓ | comment |
| :-: | :-: | :-: | :-: |
| none | - | **74.747%** | **0.0456907** | 不使用情感轮先验层 |
| original | freeze | 50.051% | 0.0912462 | 冻结情感轮先验层权重 |
| original | unfreeze | 48.939% | 0.0741477 | 允许情感轮先验层参与微调 |
| eye | unfreeze | 48.485% | 0.0774974 | 对角阵初始化 |
| rand | unfreeze | 47.778% | 0.0732538 | 随机初始化 |
| corr | freeze | 19.141% | 0.1249517 | |
| corr | unfreeze | 27.677% | 0.1199178 | |

### references

- 论文
- 摘要: [https://lkxb.jxnu.edu.cn/oa/DArticle.aspx?type=view&id=202204006](https://lkxb.jxnu.edu.cn/oa/DArticle.aspx?type=view&id=202204006)
- 下载: [https://lkxb.jxnu.edu.cn/oa/pdfdow.aspx?Sid=202204006](https://lkxb.jxnu.edu.cn/oa/pdfdow.aspx?Sid=202204006)
- Emotion6 数据集
- 主页: [http://chenlab.ece.cornell.edu/downloads.html](http://chenlab.ece.cornell.edu/downloads.html)
- 下载: [http://chenlab.ece.cornell.edu/people/kuanchuan/publications/Emotion6.zip](http://chenlab.ece.cornell.edu/people/kuanchuan/publications/Emotion6.zip)

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by Armit
2023/12/11