6 citations · 18 across the 5 of their papers we have counts for
5 papers
MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation
Yong Yang, Qiong Chen, Yuan Feng +1
Existing few-shot segmentation methods are based on the meta-learning strategy and extract instance knowledge from a support set and then apply the knowledge to segment target obje…
Probabilistic Ranking-Aware Ensembles for Enhanced Object Detections
Mingyuan Mao, Baochang Zhang, David Doermann +5
Model ensembles are becoming one of the most effective approaches for improving object detection performance already optimized for a single detector. Conventional methods directly…
PAFNet: An Efficient Anchor-Free Object Detector Guidance
Ying Xin, Guanzhong Wang, Mingyuan Mao +5
Object detection is a basic but challenging task in computer vision, which plays a key role in a variety of industrial applications. However, object detectors based on deep learnin…
The 1st Tiny Object Detection Challenge:Methods and Results
Xuehui Yu, Zhenjun Han, Yuqi Gong +22
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…
2nd Place Solution in Google AI Open Images Object Detection Track 2019
Ruoyu Guo, Cheng Cui, Yuning Du +6
We present an object detection framework based on PaddlePaddle. We put all the strategies together (multi-scale training, FPN, Cascade, Dcnv2, Non-local, libra loss) based on ResNe…