A Survey of Deep Learning for Low-Shot Object Detection

TL;DR

This survey reviews recent advances in Low-Shot Object Detection, highlighting methods for detecting objects with limited or no annotated data, and discusses challenges and future research directions.

Contribution

It provides a comprehensive taxonomy and systematic analysis of LSOD methods, including extensions like semi-supervised and incremental approaches.

Findings

Comparison of LSOD methods' performance

Analysis of pros and cons of current techniques

Discussion of challenges and future directions

Abstract

Object detection has achieved a huge breakthrough with deep neural networks and massive annotated data. However, current detection methods cannot be directly transferred to the scenario where the annotated data is scarce due to the severe overfitting problem. Although few-shot learning and zero-shot learning have been extensively explored in the field of image classification, it is indispensable to design new methods for object detection in the data-scarce scenario since object detection has an additional challenging localization task. Low-Shot Object Detection (LSOD) is an emerging research topic of detecting objects from a few or even no annotated samples, consisting of One-Shot Object Detection (OSOD), Few-Shot Object Detection (FSOD) and Zero-Shot Object Detection (ZSD). This survey provides a comprehensive review of LSOD methods. First, we propose a thorough taxonomy of LSOD methods and analyze them systematically, comprising some extensional topics of LSOD (semi-supervised LSOD, weakly-supervised LSOD, and incremental LSOD). Then, we indicate the pros and cons of current LSOD methods with a comparison of their performance. Finally, we discuss the challenges and promising directions of LSOD to provide guidance for future works.