no code implementations • 30 Nov 2023 • Zongqian Wu, Yujing Liu, Mengmeng Zhan, Jialie Shen, Ping Hu, Xiaofeng Zhu
Although current prompt learning methods have successfully been designed to effectively reuse the large pre-trained models without fine-tuning their large number of parameters, they still have limitations to be addressed, i. e., without considering the adverse impact of meaningless patches in every image and without simultaneously considering in-sample generalization and out-of-sample generalization.
no code implementations • 11 Aug 2023 • Rui Xu, Yong Luo, Han Hu, Bo Du, Jialie Shen, Yonggang Wen
Weakly supervised object localization (WSOL) is one of the most popular and challenging tasks in computer vision.
1 code implementation • 1 Aug 2023 • Guanyu Xu, Jiawei Hao, Li Shen, Han Hu, Yong Luo, Hui Lin, Jialie Shen
Recently, the efficient deployment and acceleration of powerful vision transformers (ViTs) on resource-limited edge devices for providing multimedia services have become attractive tasks.
no code implementations • 9 Jul 2022 • Lin Wu, Deyin Liu, Wenying Zhang, Dapeng Chen, ZongYuan Ge, Farid Boussaid, Mohammed Bennamoun, Jialie Shen
In this paper, we present a pseudo-pair based self-similarity learning approach for unsupervised person re-ID without human annotations.
no code implementations • 14 Apr 2019 • Bo Du, Zengmao Wang, Lefei Zhang, Liangpei Zhang, Wei Liu, Jialie Shen, DaCheng Tao
Then can we find a way to fuse the two active sampling criteria without any assumption on data?
no code implementations • 20 Feb 2019 • Shuai Yu, Yongbo Wang, Min Yang, Baocheng Li, Qiang Qu, Jialie Shen
In this paper, we develop a neural attentive interpretable recommendation system, named NAIRS.