1 code implementation • 15 Nov 2021 • Yaoming Cai, Zijia Zhang, Zhihua Cai, Xiaobo Liu, Yao Ding, Pedram Ghamisi
This paper presents FLGC, a simple yet effective fully linear graph convolutional network for semi-supervised and unsupervised learning.
1 code implementation • CVPR 2021 • Binghao Liu, Yao Ding, Jianbin Jiao, Xiangyang Ji, Qixiang Ye
Encouraging progress in few-shot semantic segmentation has been made by leveraging features learned upon base classes with sufficient training data to represent novel classes with few-shot examples.
Ranked #68 on Few-Shot Semantic Segmentation on COCO-20i (1-shot)
2 code implementations • ICCV 2021 • Hongliang He, Zhongyi Huang, Yao Ding, Guoli Song, Lin Wang, Qian Ren, Pengxu Wei, Zhiqiang Gao, Jie Chen
Specifically, we define the centripetal direction feature as a class of adjacent directions pointing to the nuclear center to represent the spatial relationship between pixels within the nucleus.
no code implementations • 4 Nov 2020 • Yuqi Gong, Xuehui Yu, Yao Ding, Xiaoke Peng, Jian Zhao, Zhenjun Han
We propose a novel concept, fusion factor, to control information that deep layers deliver to shallow layers, for adapting FPN to tiny object detection.
1 code implementation • ICCV 2019 • Yao Ding, Yanzhao Zhou, Yi Zhu, Qixiang Ye, Jianbin Jiao
Fine-grained recognition poses the unique challenge of capturing subtle inter-class differences under considerable intra-class variances (e. g., beaks for bird species).
Ranked #30 on Fine-Grained Image Classification on Stanford Cars
Fine-Grained Image Classification Fine-Grained Image Recognition