Skip to content

Latest commit

 

History

History
135 lines (101 loc) · 17 KB

README.md

File metadata and controls

135 lines (101 loc) · 17 KB
Raw

Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild (ECCV'2020)

Abstract

Detecting objects and estimating their viewpoint in images are key tasks of 3D scene understanding. Recent approaches have achieved excellent results on very large benchmarks for object detection and view-point estimation. However, performances are still lagging behind for novel object categories with few samples. In this paper, we tackle the problems of few-shot object detection and few-shot viewpoint estimation. We propose a meta-learning framework that can be applied to both tasks, possibly including 3D data. Our models improve the results on objects of novel classes by leveraging on rich feature information originating from base classes with many samples. A simple joint feature embedding module is proposed to make the most of this feature sharing. Despite its simplicity, our method outperforms state-of-the-art methods by a large margin on a range of datasets, including PASCAL VOC and MS COCO for few-shot object detection, and Pascal3D+ and ObjectNet3D for few-shot viewpoint estimation. And for the first time, we tackle the combination of both few-shot tasks, on ObjectNet3D, showing promising results. Our code and data are available at http://imagine.enpc.fr/~xiaoy/FSDetView/.

Citation

@inproceedings{xiao2020fsdetview,
    title={Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild},
    author={Yang Xiao and Renaud Marlet},
    booktitle={European Conference on Computer Vision (ECCV)},
    year={2020}
}

Note: ALL the reported results use the data split released from TFA official repo. Currently, each setting is only evaluated with one fixed few shot dataset. Please refer to DATA Preparation to get more details about the dataset and data preparation.

How to reproduce FSDetView

Following the original implementation, it consists of 2 steps:

  • Step1: Base training

    • use all the images and annotations of base classes to train a base model.

  • Step2: Few shot fine-tuning:

    • use the base model from step1 as model initialization and further fine tune the model with few shot datasets.

An example of VOC split1 1 shot setting with 8 gpus

# step1: base training for voc split1
bash ./tools/detection/dist_train.sh \
    configs/detection/fsdetview/voc/split1/fsdetview_r101_c4_8xb4_voc-split1_base-training.py 8

# step2: few shot fine-tuning
bash ./tools/detection/dist_train.sh \
    configs/detection/fsdetview/voc/split1/fsdetview_r101_c4_8xb4_voc-split1_1shot-fine-tuning.py 8

Note:

  • The default output path of the reshaped base model in step2 is set to work_dirs/{BASE TRAINING CONFIG}/base_model_random_init_bbox_head.pth. When the model is saved to different path, please update the argument load_from in step3 few shot fine-tune configs instead of using resume_from.
  • To use pre-trained checkpoint, please set the load_from to the downloaded checkpoint path.

Results on VOC dataset

Note:

  • The official implementation use batch size 1x4 for training, while we use batch size 8x4.
  • For few shot fine-tuning we only fine tune the bbox head and the iterations or training strategy may not be the optimal in 8gpu setting.
  • Base training use 200 support base instances each class for testing.
  • The performance of the base training and few shot setting can be unstable, even using the same random seed. To reproduce the reported few shot results, it is highly recommended using the released model for few shot fine-tuning.
  • The difficult samples will be used in base training query set, but not be used in support set and few shot setting.

Base Training

Arch Split Base AP50 ckpt log
r101 c4 1 73.7 ckpt log
r101 c4 2 74.6 ckpt log
r101 c4 3 73.2 ckpt log

Few Shot Fine-tuning

Arch Split Shot Base AP50 Novel AP50 ckpt log
r101 c4 1 1 61.1 35.5 ckpt log
r101 c4 1 2 67.9 49.9 ckpt log
r101 c4 1 3 68.1 54.6 ckpt log
r101 c4 1 5 69.9 60.5 ckpt log
r101 c4 1 10 71.7 61.0 ckpt log
r101 c4 2 1 65.0 27.9 ckpt log
r101 c4 2 2 69.6 36.6 ckpt log
r101 c4 2 3 70.9 41.4 ckpt log
r101 c4 2 5 71.3 43.2 ckpt log
r101 c4 2 10 72.4 47.8 ckpt log
r101 c4 3 1 61.4 37.3 ckpt log
r101 c4 3 2 69.3 44.0 ckpt log
r101 c4 3 3 70.5 47.5 ckpt log
r101 c4 3 5 72.2 52.9 ckpt log
r101 c4 3 10 73.1 52.9 ckpt log

Results on COCO dataset

Note:

  • The official implementation use batch size 1x4 for training, while we use batch size 8x4.
  • For few shot fine-tuning we only fine tune the bbox head and the iterations or training strategy may not be the optimal in 8gpu setting.
  • Base training use 200 support base instances each class for testing.
  • The performance of the base training and few shot setting can be unstable, even using the same random seed. To reproduce the reported few shot results, it is highly recommended using the released model for few shot fine-tuning.

Base Training

Arch Base mAP ckpt log
r50 c4 21.3 ckpt log

Few Shot Fine-tuning

Arch Shot Base mAP Novel mAP ckpt log
r50 c4 10 21.1 9.1 ckpt log
r50 c4 30 23.5 12.4 ckpt log