2 code implementations • 3 Dec 2022 • Wentong Li, Wenyu Liu, Jianke Zhu, Miaomiao Cui, Risheng Yu, Xiansheng Hua, Lei Zhang
In contrast to fully supervised methods using pixel-wise mask labels, box-supervised instance segmentation takes advantage of simple box annotations, which has recently attracted increasing research attention.
1 code implementation • 19 Jul 2022 • Wentong Li, Wenyu Liu, Jianke Zhu, Miaomiao Cui, Xiansheng Hua, Lei Zhang
A simple mask supervised SOLOv2 model is adapted to predict the instance-aware mask map as the level set for each instance.
1 code implementation • 15 Jun 2022 • Yuxuan Zhou, Wangmeng Xiang, Chao Li, Biao Wang, Xihan Wei, Lei Zhang, Margret Keuper, Xiansheng Hua
Unlike convolutional inductive biases, which are forced to focus exclusively on hard-coded local regions, our proposed SPs are learned by the model itself and take a variety of spatial relations into account.
Ranked #153 on Image Classification on ImageNet
1 code implementation • CVPR 2022 • Mu Hu, Junyi Feng, Jiashen Hua, Baisheng Lai, Jianqiang Huang, Xiaojin Gong, Xiansheng Hua
Structural re-parameterization has drawn increasing attention in various computer vision tasks.
1 code implementation • 18 Mar 2022 • Tao Yang, Peiran Ren, Xuansong Xie, Xiansheng Hua, Lei Zhang
Most of the existing deep learning based VFI methods adopt off-the-shelf optical flow algorithms to estimate the bidirectional flows and interpolate the missing frames accordingly.
no code implementations • 31 Oct 2021 • Xiaoshuang Chen, Yiru Zhao, Yu Qin, Fei Jiang, Mingyuan Tao, Xiansheng Hua, Hongtao Lu
Crowd counting aims to learn the crowd density distributions and estimate the number of objects (e. g. persons) in images.
no code implementations • 29 Apr 2021 • Xin Guo, Zhongming Jin, Chong Chen, Helei Nie, Jianqiang Huang, Deng Cai, Xiaofei He, Xiansheng Hua
In this paper, we propose a DiscRiminative-gEnerative duAl Memory (DREAM) anomaly detection model to take advantage of a few anomalies and solve data imbalance.
1 code implementation • 4 Feb 2021 • Chi Wang, Yunke Zhang, Miaomiao Cui, Peiran Ren, Yin Yang, Xuansong Xie, Xiansheng Hua, Hujun Bao, Weiwei Xu
This paper proposes a novel active boundary loss for semantic segmentation.
1 code implementation • CVPR 2021 • Xing Shen, Jirui Yang, Chunbo Wei, Bing Deng, Jianqiang Huang, Xiansheng Hua, Xiaoliang Cheng, Kewei Liang
Generally, a low-resolution grid is not sufficient to capture the details, while a high-resolution grid dramatically increases the training complexity.
1 code implementation • ECCV 2020 • Dong Zhang, Hanwang Zhang, Jinhui Tang, Meng Wang, Xiansheng Hua, Qianru Sun
Yet, the non-local spatial interactions are not across scales, and thus they fail to capture the non-local contexts of objects (or parts) residing in different scales.