One-Shot Object Detection without Fine-Tuning

Abstract

Deep learning has revolutionized object detection thanks to large-scale datasets, but their object categories are still arguably very limited. In this paper, we attempt to enrich such categories by addressing the one-shot object detection problem, where the number of annotated training examples for learning an unseen class is limited to one. We introduce a two-stage model consisting of a first stage Matching-FCOS network and a second stage Structure-Aware Relation Module, the combination of which integrates metric learning with an anchor-free Faster R-CNN-style detection pipeline, eventually eliminating the need to fine-tune on the support images. We also propose novel training strategies that effectively improve detection performance. Extensive quantitative and qualitative evaluations were performed and our method exceeds the state-of-the-art one-shot performance consistently on multiple datasets.

References in corpus (6)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
• Prototypical Networks for Few-shot Learning
• FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation
• LSTD: A Low-Shot Transfer Detector for Object Detection
• Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector

Cited by in corpus (2)

• UP-DETR: Unsupervised Pre-training for Object Detection with Transformers
• A Comparative Review of Recent Few-Shot Object Detection Algorithms