Few Shot Object Detection via Feature Reweighting

Bingyi Kang, Zhuang Liu, Xin Wang, Fisher Yu, Jiashi Feng, Trevor Darrell
ICCV 2019

Few Shot Object Detection via Feature Reweighting

Abstract

Conventional training of a deep CNN based object detector demands a large number of bounding box annotations, which may be unavailable for rare categories. In this work we develop a few-shot object detector that can learn to detect novel objects from only a few annotated examples. Our proposed model leverages fully labeled base classes and quickly adapts to novel classes, using a meta feature learner and a reweighting module within a one-stage detection architecture. The feature learner extracts meta features that are generalizable to detect novel object classes, using training data from base classes with sufficient samples. The reweighting module transforms a few support examples from the novel classes to a global vector that indicates the importance or relevance of meta features for detecting the corresponding objects. These two modules, together with a detection prediction module, are trained end-to-end based on an episodic few-shot learning scheme and a carefully designed loss function. Through extensive experiments we demonstrate that our model outperforms well-established baselines by a large margin for few-shot object detection, on multiple datasets and settings. We also present analysis on various aspects of our proposed model, aiming to provide some inspiration for future few-shot detection works.

Paper

Code

paper
github.com/bingykang/Fewshot_Detection

Citation

@inproceedings{kang2019few,
  title={Few-shot Object Detection via Feature Reweighting},
  author={Kang, Bingyi and Liu, Zhuang and Wang, Xin and Yu, Fisher and Feng, Jiashi and Darrell, Trevor},
  booktitle={ICCV},
  year={2019}
}

Related


Frustratingly Simple Few-Shot Object Detection

Frustratingly Simple Few-Shot Object Detection

ICML 2020 State-of-the-art few-shot detection method with backpropagation learning.


Robust Object Detection via Instance-Level Temporal Cycle Confusion

Robust Object Detection via Instance-Level Temporal Cycle Confusion

ICCV 2021 We study the effectiveness of auxiliary self-supervised tasks to improve the out-of-distribution generalization of object detectors.


TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning

TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning

CVPR 2019 We propose Task-Aware Feature Embedding Networks (TAFE-Nets) to learn how to adapt the image representation to a new task in a meta learning fashion.