Multiple-instance-learning
Pytorch implementation of three Multiple Instance Learning or Multi-classification papers, the performace of the visual_concept method is the best.
三种多示例学习方法实现，用于图像的多标签，其中 visual_concept效果最好
- data_process: vocabulary id dict construction file, used by the three methods.构造词汇数据词典，三个方法均通用
- CNN-RNN: A Unified Framework for Multi-label Image Classification https://arxiv.org/abs/1604.04573
- Visual_concept: From captions to visual concepts and back https://arxiv.org/abs/1411.4952?context=cs
- DeepMIML: https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/aaai17deepMIML.pdf
Data prepare
We will not provide the original dataset, but you can build it using your own dataset. Among them, resized2014 is image dataset, img_tag.txt is the mapping dict file of image to tags, having that, you can generate the zh_vocab.pkl vocabulary file using https://github.com/Epiphqny/Multiple-instance-learning/blob/master/data_process/build_vocab.py
Examples
img_tag.txt(with number id represent different image name):
1\tab girl,bottle,car
2\tab boy
3\tab child,bike
...
zh_vocab.pkl:
self.idx2word={1:girl,2:bottle,3:boy,4:car...}
self.word2idx={girl:1,bottle:2,boy:3,car:4...}
Just an example, the realization may have some variation, the lines in the text file are in json format.