Video Synopsis Based on Attention Mechanism and Local Transparent Processing

Chen, Shengbo; Liu, Xianrui; Huang, Yiyong; Zhou, Congcong; Miao, Huaikou

doi:10.1109/access.2020.2994613

Cited by 3 publications

(1 citation statement)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CBAM achieves better results on the ImageNet-1k dataset. In [ 35 ], Chen et al presented a method that integrates RetinaNet and attention mechanism to generate the video synopsis for long captured videos. In their experiments, the combined attention method improved the Average Precision (AP) of detection by about 3%.…”

Section: Related Workmentioning

confidence: 99%

Supernovae Detection with Fully Convolutional One-Stage Framework

Yin

Jia

Gao

et al. 2021

Sensors

View full text Add to dashboard Cite

A series of sky surveys were launched in search of supernovae and generated a tremendous amount of data, which pushed astronomy into a new era of big data. However, it can be a disastrous burden to manually identify and report supernovae, because such data have huge quantity and sparse positives. While the traditional machine learning methods can be used to deal with such data, deep learning methods such as Convolutional Neural Networks demonstrate more powerful adaptability in this area. However, most data in the existing works are either simulated or without generality. How do the state-of-the-art object detection algorithms work on real supernova data is largely unknown, which greatly hinders the development of this field. Furthermore, the existing works of supernovae classification usually assume the input images are properly cropped with a single candidate located in the center, which is not true for our dataset. Besides, the performance of existing detection algorithms can still be improved for the supernovae detection task. To address these problems, we collected and organized all the known objectives of the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) and the Popular Supernova Project (PSP), resulting in two datasets, and then compared several detection algorithms on them. After that, the selected Fully Convolutional One-Stage (FCOS) method is used as the baseline and further improved with data augmentation, attention mechanism, and small object detection technique. Extensive experiments demonstrate the great performance enhancement of our detection algorithm with the new datasets.

show abstract

Section: Related Workmentioning

confidence: 99%