One-Shot Object Detection without Fine-Tuning
- URL: http://arxiv.org/abs/2005.03819v1
- Date: Fri, 8 May 2020 01:59:23 GMT
- Title: One-Shot Object Detection without Fine-Tuning
- Authors: Xiang Li, Lin Zhang, Yau Pun Chen, Yu-Wing Tai, Chi-Keung Tang
- Abstract summary: We introduce a two-stage model consisting of a first stage Matching-FCOS network and a second stage Structure-Aware Relation Module.
We also propose novel training strategies that effectively improve detection performance.
Our method exceeds the state-of-the-art one-shot performance consistently on multiple datasets.
- Score: 62.39210447209698
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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