1 code implementation • 20 Sep 2023 • Yazhou Zhu, Shidong Wang, Tong Xin, Zheng Zhang, Haofeng Zhang
In this work, we present an approach to extract multiple representative sub-regions from a given support medical image, enabling fine-grained selection over the generated image regions.
1 code implementation • 9 Sep 2023 • Yazhou Zhu, Shidong Wang, Tong Xin, Haofeng Zhang
First, a subdivision strategy is introduced to produce a collection of regional prototypes from the foreground of the support prototype.
no code implementations • 19 Nov 2022 • Chenyi Jiang, Dubing Chen, Shidong Wang, Yuming Shen, Haofeng Zhang, Ling Shao
Compositional Zero-Shot Learning (CZSL) aims to recognize unseen compositions from seen states and objects.
no code implementations • 19 Aug 2022 • Tailin Chen, Desen Zhou, Jian Wang, Shidong Wang, Qian He, Chuanyang Hu, Errui Ding, Yu Guan, Xuming He
In this paper, we study the problem of one-shot skeleton-based action recognition, which poses unique challenges in learning transferable representation from base classes to novel classes, particularly for fine-grained actions.
1 code implementation • 23 Dec 2021 • Xiaojie Zhao, Yuming Shen, Shidong Wang, Haofeng Zhang
Most generative ZSL methods use category semantic attributes plus a Gaussian noise to generate visual features.
no code implementations • 1 Nov 2021 • Tailin Chen, Shidong Wang, Desen Zhou, Yu Guan
We devise our model into a pure factorised architecture which can alternately perform spatial feature aggregation and temporal feature aggregation.
1 code implementation • 10 Aug 2021 • Tailin Chen, Desen Zhou, Jian Wang, Shidong Wang, Yu Guan, Xuming He, Errui Ding
The task of skeleton-based action recognition remains a core challenge in human-centred scene understanding due to the multiple granularities and large variation in human motion.
no code implementations • 11 Nov 2020 • Shidong Wang, Yi Ren, Gerard Parr, Yu Guan, Ling Shao
In this article, we propose a novel invariant deep compressible covariance pooling (IDCCP) to solve nuisance variations in aerial scene categorization.
no code implementations • 9 Aug 2020 • Haoran Duan, Shidong Wang, Yu Guan
To obtain the appropriate crowd representation, in this work we proposed SOFA-Net(Second-Order and First-order Attention Network): second-order statistics were extracted to retain selectivity of the channel-wise spatial information for dense heads while first-order statistics, which can enhance the feature discrimination for the heads' areas, were used as complementary information.