The code repository for [Multi-Instance Attention Network for Few-shot Learning]
The attention mechanism is usually equipped with a few-shot learning framework, and plays a key role in extracting the semantic object(s). However, most attention networks in existing few-shot learning algorithms often work on the channel and/or pixel dimension, and require learning a large amount of parameters. Due to the limitation of training examples, these attention networks tend to be under-fitting, and may fail to find the semantic target(s). In this paper, we transform the few-shot learning problem into a multi-instance learning problem, and design a new attention network that works on image patches, which significantly decreases the number of parameters in the attention network meanwhile classifying semantic patches and irrelevant patches directly. In each episode, we first convert the original image into a multi-instance bag by splitting the whole image into several blocks. Afterward, an attention-based multi-instance model is proposed to learn the representative prototypes of each bag. Finally, we further introduce a metric-based meta-classifier for prediction. Extensive experiments on typical real-world data sets have demonstrated that our proposed algorithm achieves consistent improvement over many state-of-the-art models.
Please click the Google Drive link for downloading the
following datasets, or running the downloading bash scripts in folder datasets/ to download.
It contains 100 classes with 600 images in each class, which are built upon the ImageNet dataset. The 100 classes are divided into 64, 16, 20 for meta-training, meta-validation and meta-testing, respectively.
TieredImageNet is also a subset of ImageNet, which includes 608 classes from 34 super-classes. Compared with miniImageNet, the splits of meta-training(20), meta-validation(6) and meta-testing(8) are set according to the super-classes to enlarge the domain difference between training and testing phase. The dataset also include more images for training and evaluation (779,165 images in total).
CUB was originally proposed for fine-grained bird classification, which contains 11,788 images from 200 classes. We follow the splits in FEAT that 200 classes are divided into 100, 50 and 50 for meta-training, meta-validation and meta-testing, respectively.
FC100 is a few-shot classification dataset built on CIFAR100. We follow the split division proposed in TADAM, where 36 super-classes were divided into 12 (including 60 classes), 4 (including 20 classes), 4 (including 20 classes), for meta-training, meta-validation and meta-testing, respectively, and each class contains 100 images.
First run (pretrain.py) to get the pretrain model parameters. Then adjust the path of pretrain parameters in (train.py), run (train.py) to start training and testing processes.
Our project references the codes in the following repos.