The Art of Camouflage: Few-shot Learning for Animal Detection and
Segmentation
- URL: http://arxiv.org/abs/2304.07444v3
- Date: Mon, 22 Jan 2024 02:56:05 GMT
- Title: The Art of Camouflage: Few-shot Learning for Animal Detection and
Segmentation
- Authors: Thanh-Danh Nguyen, Anh-Khoa Nguyen Vu, Nhat-Duy Nguyen, Vinh-Tiep
Nguyen, Thanh Duc Ngo, Thanh-Toan Do, Minh-Triet Tran, and Tam V. Nguyen
- Abstract summary: We propose a novel method to efficiently detect and segment the camouflaged objects in the images.
In particular, we introduce the instance triplet loss and the instance memory storage.
Our proposed method achieves state-of-the-art performance on the newly collected dataset.
- Score: 21.047026366450197
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Camouflaged object detection and segmentation is a new and challenging
research topic in computer vision. There is a serious issue of lacking data of
camouflaged objects such as camouflaged animals in natural scenes. In this
paper, we address the problem of few-shot learning for camouflaged object
detection and segmentation. To this end, we first collect a new dataset,
CAMO-FS, for the benchmark. We then propose a novel method to efficiently
detect and segment the camouflaged objects in the images. In particular, we
introduce the instance triplet loss and the instance memory storage. The
extensive experiments demonstrated that our proposed method achieves
state-of-the-art performance on the newly collected dataset.
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