173 citations · 275 across the 8 of their papers we have counts for
9 papers
When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search
Guocheng Qian, Xuanyang Zhang, Guohao Li +5
The key challenge in neural architecture search (NAS) is designing how to explore wisely in the huge search space. We propose a new NAS method called TNAS (NAS with trees), which i…
Joint COCO and Mapillary Workshop at ICCV 2019: COCO Instance Segmentation Challenge Track
Zeming Li, Yuchen Ma, Yukang Chen +2
In this report, we present our object detection/instance segmentation system, MegDetV2, which works in a two-pass fashion, first to detect instances then to obtain segmentation. Ou…
Dynamic Scale Training for Object Detection
Yukang Chen, Peizhen Zhang, Zeming Li +5
We propose a Dynamic Scale Training paradigm (abbreviated as DST) to mitigate scale variation challenge in object detection. Previous strategies like image pyramid, multi-scale tra…
Learning Dynamic Routing for Semantic Segmentation
Yanwei Li, Lin Song, Yukang Chen +4
Recently, numerous handcrafted and searched networks have been applied for semantic segmentation. However, previous works intend to handle inputs with various scales in pre-defined…
DetNAS: Backbone Search for Object Detection
Yukang Chen, Tong Yang, Xiangyu Zhang +3
Object detectors are usually equipped with backbone networks designed for image classification. It might be sub-optimal because of the gap between the tasks of image classification…
EAT-NAS: Elastic Architecture Transfer for Accelerating Large-scale Neural Architecture Search
Jiemin Fang, Yukang Chen, Xinbang Zhang +5
Neural architecture search (NAS) methods have been proposed to release human experts from tedious architecture engineering. However, most current methods are constrained in small-s…