Identification of Novel Classes for Improving Few-Shot Object Detection
- URL: http://arxiv.org/abs/2303.10422v1
- Date: Sat, 18 Mar 2023 14:12:52 GMT
- Title: Identification of Novel Classes for Improving Few-Shot Object Detection
- Authors: Zeyu Shangguan, Mohammad Rostami
- Abstract summary: Few-shot object detection (FSOD) methods offer a remedy by realizing robust object detection using only a few training samples per class.
We develop a semi-supervised algorithm to detect and then utilize unlabeled novel objects as positive samples during training to improve FSOD performance.
Our experimental results indicate that our method is effective and outperforms the existing state-of-the-art (SOTA) FSOD methods.
- Score: 12.013345715187285
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Conventional training of deep neural networks requires a large number of the
annotated image which is a laborious and time-consuming task, particularly for
rare objects. Few-shot object detection (FSOD) methods offer a remedy by
realizing robust object detection using only a few training samples per class.
An unexplored challenge for FSOD is that instances from unlabeled novel classes
that do not belong to the fixed set of training classes appear in the
background. These objects behave similarly to label noise, leading to FSOD
performance degradation. We develop a semi-supervised algorithm to detect and
then utilize these unlabeled novel objects as positive samples during training
to improve FSOD performance. Specifically, we propose a hierarchical ternary
classification region proposal network (HTRPN) to localize the potential
unlabeled novel objects and assign them new objectness labels. Our improved
hierarchical sampling strategy for the region proposal network (RPN) also
boosts the perception ability of the object detection model for large objects.
Our experimental results indicate that our method is effective and outperforms
the existing state-of-the-art (SOTA) FSOD methods.
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