30 citations · 50 across the 4 of their papers we have counts for
6 papers
Powering One-shot Topological NAS with Stabilized Share-parameter Proxy
Ronghao Guo, Chen Lin, Chuming Li +4
One-shot NAS method has attracted much interest from the research community due to its remarkable training efficiency and capacity to discover high performance models. However, the…
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…
Computation Reallocation for Object Detection
Feng Liang, Chen Lin, Ronghao Guo +4
The allocation of computation resources in the backbone is a crucial issue in object detection. However, classification allocation pattern is usually adopted directly to object det…
Improving One-shot NAS by Suppressing the Posterior Fading
Xiang Li, Chen Lin, Chuming Li +4
There is a growing interest in automated neural architecture search (NAS). To improve the efficiency of NAS, previous approaches adopt weight sharing method to force all models sha…
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…