167 citations · 774 across the 23 of their papers we have counts for
6 papers · 1 filter
Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation
Bowen Cheng, Maxwell D. Collins, Yukun Zhu +4
In this work, we introduce Panoptic-DeepLab, a simple, strong, and fast system for panoptic segmentation, aiming to establish a solid baseline for bottom-up methods that can achiev…
Panoptic-DeepLab
Bowen Cheng, Maxwell D. Collins, Yukun Zhu +4
We present Panoptic-DeepLab, a bottom-up and single-shot approach for panoptic segmentation. Our Panoptic-DeepLab is conceptually simple and delivers state-of-the-art results. In p…
SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems
Xiaofan Zhang, Haoming Lu, Cong Hao +9
Object detection and tracking are challenging tasks for resource-constrained embedded systems. While these tasks are among the most compute-intensive tasks from the artificial inte…
SPGNet: Semantic Prediction Guidance for Scene Parsing
Bowen Cheng, Liang-Chieh Chen, Yunchao Wei +6
Multi-scale context module and single-stage encoder-decoder structure are commonly employed for semantic segmentation. The multi-scale context module refers to the operations to ag…
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
Bowen Cheng, Bin Xiao, Jingdong Wang +3
Bottom-up human pose estimation methods have difficulties in predicting the correct pose for small persons due to challenges in scale variation. In this paper, we present HigherHRN…
High Frequency Residual Learning for Multi-Scale Image Classification
Bowen Cheng, Rong Xiao, Jianfeng Wang +2
We present a novel high frequency residual learning framework, which leads to a highly efficient multi-scale network (MSNet) architecture for mobile and embedded vision problems. T…