Channel spatial attention based single-shot object detector for autonomous vehicles
Sign Up to like & getrecommendations! Published in 2021 at "Multimedia Tools and Applications"
DOI: 10.1007/s11042-021-11267-3
Abstract: Real-time object detection with high accuracy is the measure concern for the autonomous vehicle to provide safety. Recently many state-of-the-art methods used Convolutional Neural Network (CNN) for object detection. Although these methods provide better results… read more here.
Keywords: detection; spatial attention; shot object; single shot ... See more keywords
Few-Shot Object Detection With Self-Adaptive Attention Network for Remote Sensing Images
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2021.3078177
Abstract: In remote sensing field, there are many applications of object detection in recent years, which demands a great number of labeled data. However, we may be faced with some cases where only limited data are… read more here.
Keywords: self adaptive; remote sensing; attention; object detection ... See more keywords
Few-Shot Object Detection via Context-Aware Aggregation for Remote Sensing Images
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2022.3171257
Abstract: Few-shot object detection methods have made prodigious progress in recent years. However, these methods are designed for optical images at a single scale, which leads to significantly degraded detection performance due to object scale variation… read more here.
Keywords: shot object; remote sensing; detection; object detection ... See more keywords
Solo-to-Collaborative Dual-Attention Network for One-Shot Object Detection in Remote Sensing Images
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2021.3091003
Abstract: In this article, we attempt to achieve one-shot object detection by mimicking the human ability to learn new concepts under limited reference, which aims at detecting all object instances of an unseen class in a… read more here.
Keywords: object detection; image; one shot; shot ... See more keywords
FSODS: A Lightweight Metalearning Method for Few-Shot Object Detection on SAR Images
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3192996
Abstract: At present, few-shot object detection research in the field of optical remote sensing images has been conducted, but few-shot object detection in the field of synthetic aperture radar (SAR) images has rarely been explored. To… read more here.
Keywords: shot object; detection; object detection; method ... See more keywords
Category Knowledge-Guided Parameter Calibration for Few-Shot Object Detection
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2023.3239197
Abstract: Few-shot object detection (FSOD) aims to adapt generic detectors to the novel categories with only a few annotations, which is an important and realistic task. Although the generic object detection has been widely studied over… read more here.
Keywords: shot object; category knowledge; detection; object detection ... See more keywords
Few-Shot Object Detection With Self-Supervising and Cooperative Classifier.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3204597
Abstract: Few-shot object detection (FSOD), which detects novel objects with only a few training instances, has recently attracted more attention. Previous works focus on making the most use of label information of objects. Still, they fail… read more here.
Keywords: shot object; novel classes; object detection; self ... See more keywords