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*.pyc | ||
runs/ | ||
logs/ | ||
.vscode/ |
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## 运行 | ||
# StyleEDL: Style-Guided High-order Attention Network for Image | ||
## Abstract | ||
the tendency to express emotions through images. As for emotion | ||
ambiguity arising from humans’ subjectivity, substantial previous | ||
methods generally focused on learning appropriate representations | ||
from the holistic or significant part of images. However, they rarely | ||
consider establishing connections with the stylistic information although it can lead to a better understanding of images. In this paper, | ||
we propose a style-guided high-order attention network for image | ||
emotion distribution learning termed StyleEDL, which interactively | ||
learns stylistic-aware representations of images by exploring the | ||
hierarchical stylistic information of visual contents. Specifically, we | ||
consider exploring the intra- and inter-layer correlations among | ||
GRAM-based stylistic representations, and meanwhile exploit an | ||
adversary-constrained high-order attention mechanism to capture | ||
potential interactions between subtle visual parts. In addition, we | ||
introduce a stylistic graph convolutional network to dynamically | ||
generate the content-dependent emotion representations to benefit the final emotion distribution learning. Extensive experiments | ||
conducted on several benchmark datasets demonstrate the effectiveness of our proposed StyleEDL compared to state-of-the-art | ||
methods. | ||
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## Method | ||
 | ||
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## Results | ||
### Twitter-LDL ([log](logs/Twitter_LDL/train.log)) | ||
 | ||
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### Emotion6 ([log](logs/Emotion6/train.log)) | ||
 | ||
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### Flickr-LDL ([log](logs/Flickr_LDL/train.log)) | ||
 | ||
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## Installation | ||
```bash | ||
python main.py --tag | ||
conda env create -f env.yaml | ||
``` | ||
+ `--tag` 指定保存目录的前缀,默认为cache | ||
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## train | ||
```bash | ||
python main.py --tag xxx | ||
``` | ||
+ `--tag` the log will saved at `logs/$datetime.time$xxx` | ||
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## 添加新的模型 | ||
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1. 在`network`文件夹下新建新的文件 | ||
1. 在`network`下`__init__`中的`models`加入新的网络模型 | ||
1. 在`configs`文件夹中新建对应的配置文件 | ||
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## 添加新的trainer | ||
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1. 在`trainer`文件夹中新建文件,继承`basetrainer` | ||
1. 在`trainer`下`__init__`中的`trainers`加入新的trainer | ||
1. 运行时,由配置文件中的`trainer`指定 | ||
## Citation | ||
If you find this repository helpful, please consider citing: | ||
``` | ||
add latter. | ||
``` |
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