.png){kind=icon}
.png){kind=icon}
返回
Few-Shot Object Detection via Sample Processing
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2021.3059446.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Few-shot object detection (FSOD) eliminates the dependence on tremendous instances with manual annotations in conventional object detection. We deem that the scarcity of positive samples is the main reason that restricts the performance of FSOD detectors. In this paper, a novel FSOD model via sample processing, namely, FSSP, is proposed to detect objects accurately with only a few annotated samples, which is based on the structural design of the Siamese network and uses YOLOv3-SPP as the baseline. Central to FSSP are our designed self-attention (SAM) and positive-sample augmentation (PSA) modules. The former attempts to better extract the representative features of hard samples, and the latter expands the number and enriches the scale distribution of positive samples, inhibiting the growth of negative samples. For the fine-tuning phase, we modify the classification loss function to increase the punishment for hard samples. Experiments conducted on the PASCAL VOC and MS COCO datasets confirm that the proposed FSSP achieves competitive detection performance compared with state-of-the-art detectors.
Keyword:
Object detection
Detectors
Task analysis
Annotations
Training
Feature extraction
Shape
Few-shot learning
image processing
machine learning
object detection
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。