zhangbq-research/Prototype_Completion_for_FSL

Prototype Completion with Primitive Knowledge for Few-Shot Learning

This repository contains the code for the paper:
Prototype Completion with Primitive Knowledge for Few-Shot Learning
Baoquan Zhang, Xutao Li, Yunming Ye, Zhichao Huang, Lisai Zhang
CVPR 2021

Abstract

Few-shot learning is a challenging task, which aims to learn a classifier for novel classes with few examples. Pre-training based meta-learning methods effectively tackle the problem by pre-training a feature extractor and then fine-tuning it through the nearest centroid based meta-learning. However, results show that the fine-tuning step makes very marginal improvements. In this paper, 1) we figure out the key reason, i.e., in the pre-trained feature space, the base classes already form compact clusters while novel classes spread as groups with large variances, which implies that fine-tuning the feature extractor is less meaningful; 2) instead of fine-tuning the feature extractor, we focus on estimating more representative prototypes during meta-learning. Consequently, we propose a novel prototype completion based meta-learning framework. This framework first introduces primitive knowledge (i.e., class-level part or attribute annotations) and extracts representative attribute features as priors. Then, we design a prototype completion network to learn to complete prototypes with these priors. To avoid the prototype completion error caused by primitive knowledge noises or class differences, we further develop a Gaussian based prototype fusion strategy that combines the mean-based and completed prototypes by exploiting the unlabeled samples. Extensive experiments demonstrate that our method: (i) obtain more accurate prototypes; (ii) outperforms state-of-the-art techniques by $2% \sim 9%$ in terms of classification accuracy.

Citation

If you use this code for your research, please cite our paper:

@inproceedings{zhang2021prototype,
	author    = {Zhang, Baoquan and Li, Xutao and Ye, Yunming and Huang, Zhichao and Zhang, Lisai},
	title     = {Prototype Completion With Primitive Knowledge for Few-Shot Learning},
	booktitle = {CVPR},
	year      = {2021},
	pages     = {3754-3762}
}

Dependencies

• Python 3.6
• PyTorch 1.1.0

Usage

Installation

1. Clone this repository:

   git clone https://github.com/zhangbq-research/Prototype_Completion_for_FSL.git
   cd Prototype_Completion_for_FSL

2. Download and decompress dataset files: miniImageNet (courtesy of Spyros Gidaris)

3. For the dataset loader, specify the path to the directory. For example, in Prototype_Completion_for_FSL/data/mini_imagenet.py line 30:

   _MINI_IMAGENET_DATASET_DIR = 'path/to/miniImageNet'

Pre-training

1. To pre-train a feature extractor on miniImageNet and obtain a good representation for each image:

   python main.py --phase pretrain --gpu 0,1,2,3 --save-path "./experiments/meta_part_resnet12_mini" \
   --head CosineNet --network ResNet --pre_head LinearNet --dataset miniImageNet

2. You can experiment with varying classification head by changing '--pre_head' argument to LinearRotateNet.

Construct primitive knowledge for all classes

Download the file of glove_840b_300d and then perform

    python ./prior/make_miniimagenet_primitive_knowledge.py

Extract prior information from primitive knowledge

    python main.py --phase savepart --gpu 0,1,2,3 --save-path "./experiments/meta_part_resnet12_mini" \
    --network ResNet --dataset miniImageNet

Learn to complete prototype

1. To train ProtoComNet on 5-way 1-shot miniImageNet benchmark:

    python main.py --phase metainfer --gpu 0,1,2,3 --save-path "./experiments/meta_part_resnet12_mini" \
    --train-shot 1 --val-shot 1 --train-query 15 --val-query 15 --head FuseCosNet --network ResNet --dataset miniImageNet

2. To train ProtoComNet on 5-way 5-shot miniImageNet benchmark:

    python main.py --phase metainfer --gpu 0,1,2,3 --save-path "./experiments/meta_part_resnet12_mini" \
    --train-shot 5 --val-shot 5 --train-query 15 --val-query 15 --head FuseCosNet --network ResNet --dataset miniImageNet

Meta-training

1. To jointly fine-tune feature extractor and ProtoComNet on 5-way 1-shot miniImageNet benchmark:

   python main.py --phase metatrain --gpu 0,1,2,3 --save-path "./experiments/meta_part_resnet12_mini" \
   --train-shot 1 --val-shot 1 --train-query 15 --val-query 15 --head FuseCosNet --network ResNet --dataset miniImageNet

2. To jointly fine-tune feature extractor and ProtoComNet on 5-way 5-shot miniImageNet benchmark:

   python main.py --phase metatrain --gpu 0,1,2,3 --save-path "./experiments/meta_part_resnet12_mini" \
   --train-shot 5 --val-shot 5 --train-query 15 --val-query 15 --head FuseCosNet --network ResNet --dataset miniImageNet

Meta-testing

1. To evaluate performance on 5-way 1-shot miniImageNet benchmark:

   python main.py --phase metatest --gpu 0,1,2,3 --save-path "./experiments/meta_part_resnet12_mini" \
   --train-shot 1 --val-shot 1 --train-query 15 --val-query 15 --head FuseCosNet --network ResNet --dataset miniImageNet

2. To evaluate performance on 5-way 5-shot miniImageNet benchmark:

   python main.py --phase metatest --gpu 0,1,2,3 --save-path "./experiments/meta_part_resnet12_mini" \
   --train-shot 5 --val-shot 5 --train-query 15 --val-query 15 --head FuseCosNet --network ResNet --dataset miniImageNet

Acknowledgments

This code is based on the implementations of MetaOptNet