4 citations · 7 across the 2 of their papers we have counts for
4 papers
Large-Scale Object Detection in the Wild from Imbalanced Multi-Labels
Junran Peng, Xingyuan Bu, Ming Sun +3
Training with more data has always been the most stable and effective way of improving performance in deep learning era. As the largest object detection dataset so far, Open Images…
Learning an Efficient Network for Large-Scale Hierarchical Object Detection with Data Imbalance: 3rd Place Solution to Open Images Challenge 2019
Xingyuan Bu, Junran Peng, Changbao Wang +2
This report details our solution to the Google AI Open Images Challenge 2019 Object Detection Track. Based on our detailed analysis on the Open Images dataset, it is found that the…
Efficient Neural Architecture Transformation Searchin Channel-Level for Object Detection
Junran Peng, Ming Sun, Zhaoxiang Zhang +2
Recently, Neural Architecture Search has achieved great success in large-scale image classification. In contrast, there have been limited works focusing on architecture search for…
POD: Practical Object Detection with Scale-Sensitive Network
Junran Peng, Ming Sun, Zhaoxiang Zhang +2
Scale-sensitive object detection remains a challenging task, where most of the existing methods could not learn it explicitly and are not robust to scale variance. In addition, the…