/dcfs

Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation (NeurIPS 2022)

Primary LanguagePythonMIT LicenseMIT

Introduction

This repository is a official PyTorch implementation of Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation (NeurIPS 2022). This repo is created by Bin-Bin Gao.

PWC PWC PWC

Training and Evaluation

  • To reproduce the FSOD/gFSOD results on COCO 
    bash run_coco_fsod.sh r101 dcfs 8
  • To reproduce the FSIS/gFSIS results on COCO 
    bash run_coco_fsis.sh r101 dcfs 8

Results on COCO Benchmark

  • Few-shot Object Detection

    Method mAPnovel
    Shot 1 2 3 5 10 30
    DeFRCN* 7.7 11.4 13.3 15.5 18.5 22.5
    DCFS 8.1 12.1 14.4 16.6 19.5 22.7

  • Generalized Few-shot Object Detection

    Method mAPnovel
    Shot 1 2 3 5 10 30
    DeFRCN 4.8 8.5 10.7 13.6 16.8 21.2
    DCFS 6.2 10.4 12.9 15.7 18.3 21.9
    Method mAPBase
    Shot 1 2 3 5 10 30
    DeFRCN 30.4 31.4 32.1 32.6 34.0 34.8
    DCFS 34.4 34.7 34.9 35.0 35.7 35.8

  • Few-shot Instance Segmentation

    Method mAPnovel
    Shot 1 2 3 5 10 30
    Mask-DeFRCN 6.7 9.5 11.0 12.7 15.4 18.3
    DCFS 7.2 10.3 13.5 15.7 15.9 18.3

  • Generalized Few-shot Instance Segmentation

    Method mAPnovel
    Shot 1 2 3 5 10 30
    Mask-DeFRCN* 4.5 7.5 9.5 11.6 14.3 17.6
    DCFS 5.7 9.4 11.5 13.5 15.7 18.3
    Method mAPbase
    Shot 1 2 3 5 10 30
    Mask-DeFRCN* 24.6 25.6 26.2 26.8 28.2 29.1
    DCFS 28.0 28.5 28.9 29.1 29.9 30.3
  • Please refer to DeFRCN for data peparation and installation details.
  • * indicates that the results are reproduced by us with the DeFRCN source code.
  • The results of mAPbase and mAPall for gFSOD and gFSIS can be seen at the Supplementary Material and ProjectPage.

Visualization Results

The baseline DeFRCN may tend to incorrectly recognize positive object as background (middle two rows) due to the biased classification. This problem is greatly alleviated using our proposed method (DCFS).

Citing

If you find this code useful in your research, please consider citing us:

@inproceedings{gao2022dcfs,
	title={Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation},
	author={Gao, Bin-Bin and Chen, Xiaochen and Huang, Zhongyi and Nie, Congchong and Liu, Jun and Lai, Jinxiang and Jiang, Guannan and Wang, Xi and Wang, Chengjie},
	booktitle={Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022)},
	pages={--},
	year={2022}
	}

Acknowledgement

This repo is developed based on DeFRCN and Detectron2.