We present a novel masked image modeling (MIM) approach, context autoencoder (CAE), for self-supervised learning.
Object detection with Transformers (DETR) has achieved a competitive performance over traditional detectors, such as Faster R-CNN.
This leads to a new problem of confidence discrepancy for the detector ensembles.
1 code implementation • 16 Sep 2020 • Xuehui Yu, Zhenjun Han, Yuqi Gong, Nan Jiang, Jian Zhao, Qixiang Ye, Jie Chen, Yuan Feng, Bin Zhang, Xiaodi Wang, Ying Xin, Jingwei Liu, Mingyuan Mao, Sheng Xu, Baochang Zhang, Shumin Han, Cheng Gao, Wei Tang, Lizuo Jin, Mingbo Hong, Yuchao Yang, Shuiwang Li, Huan Luo, Qijun Zhao, Humphrey Shi
The 1st Tiny Object Detection (TOD) Challenge aims to encourage research in developing novel and accurate methods for tiny object detection in images which have wide views, with a current focus on tiny person detection.
We present an object detection framework based on PaddlePaddle.
Real-time object detection and tracking have shown to be the basis of intelligent production for industrial 4. 0 applications.