81 citations · 156 across the 7 of their papers we have counts for
18 papers · 1 filter
NAS-FCOS: Efficient Search for Object Detection Architectures
Ning Wang, Yang Gao, Hao Chen +4
Neural Architecture Search (NAS) has shown great potential in effectively reducing manual effort in network design by automatically discovering optimal architectures. What is notew…
ABCNet v2: Adaptive Bezier-Curve Network for Real-time End-to-end Text Spotting
Yuliang Liu, Chunhua Shen, Lianwen Jin +4
End-to-end text-spotting, which aims to integrate detection and recognition in a unified framework, has attracted increasing attention due to its simplicity of the two complimentar…
Generic Perceptual Loss for Modeling Structured Output Dependencies
Yifan Liu, Hao Chen, Yu Chen +2
The perceptual loss has been widely used as an effective loss term in image synthesis tasks including image super-resolution, and style transfer. It was believed that the success l…
BoxInst: High-Performance Instance Segmentation with Box Annotations
Zhi Tian, Chunhua Shen, Xinlong Wang +1
We present a high-performance method that can achieve mask-level instance segmentation with only bounding-box annotations for training. While this setting has been studied in the l…
Memory-Efficient Hierarchical Neural Architecture Search for Image Restoration
Haokui Zhang, Ying Li, Hao Chen +3
Recently, much attention has been spent on neural architecture search (NAS), aiming to outperform those manually-designed neural architectures on high-level vision recognition task…
Unifying Instance and Panoptic Segmentation with Dynamic Rank-1 Convolutions
Hao Chen, Chunhua Shen, Zhi Tian
Recently, fully-convolutional one-stage networks have shown superior performance comparing to two-stage frameworks for instance segmentation as typically they can generate higher-q…