Improved Region Proposal Network for Enhanced Few-Shot Object Detection
- URL: http://arxiv.org/abs/2308.07535v1
- Date: Tue, 15 Aug 2023 02:35:59 GMT
- Title: Improved Region Proposal Network for Enhanced Few-Shot Object Detection
- Authors: Zeyu Shangguan and Mohammad Rostami
- Abstract summary: Few-shot object detection (FSOD) methods have emerged as a solution to the limitations of classic object detection approaches.
We develop a semi-supervised algorithm to detect and then utilize unlabeled novel objects as positive samples during the FSOD training stage.
Our improved hierarchical sampling strategy for the region proposal network (RPN) also boosts the perception of the object detection model for large objects.
- Score: 23.871860648919593
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Despite significant success of deep learning in object detection tasks, the
standard training of deep neural networks requires access to a substantial
quantity of annotated images across all classes. Data annotation is an arduous
and time-consuming endeavor, particularly when dealing with infrequent objects.
Few-shot object detection (FSOD) methods have emerged as a solution to the
limitations of classic object detection approaches based on deep learning. FSOD
methods demonstrate remarkable performance by achieving robust object detection
using a significantly smaller amount of training data. A challenge for FSOD is
that instances from novel classes that do not belong to the fixed set of
training classes appear in the background and the base model may pick them up
as potential objects. These objects behave similarly to label noise because
they are classified as one of the training dataset classes, leading to FSOD
performance degradation. We develop a semi-supervised algorithm to detect and
then utilize these unlabeled novel objects as positive samples during the FSOD
training stage to improve FSOD performance. Specifically, we develop a
hierarchical ternary classification region proposal network (HTRPN) to localize
the potential unlabeled novel objects and assign them new objectness labels to
distinguish these objects from the base training dataset classes. Our improved
hierarchical sampling strategy for the region proposal network (RPN) also
boosts the perception ability of the object detection model for large objects.
We test our approach and COCO and PASCAL VOC baselines that are commonly used
in FSOD literature. Our experimental results indicate that our method is
effective and outperforms the existing state-of-the-art (SOTA) FSOD methods.
Our implementation is provided as a supplement to support reproducibility of
the results.
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