2 code implementations • 11 Sep 2023 • Chengkun Wang, Wenzhao Zheng, Zheng Zhu, Jie zhou, Jiwen Lu
This paper proposes an introspective deep metric learning (IDML) framework for uncertainty-aware comparisons of images.
1 code implementation • CVPR 2023 • Chengkun Wang, Wenzhao Zheng, Junlong Li, Jie zhou, Jiwen Lu
Learning a generalizable and comprehensive similarity metric to depict the semantic discrepancies between images is the foundation of many computer vision tasks.
1 code implementation • 15 Nov 2022 • Chengkun Wang, Wenzhao Zheng, Xian Sun, Jiwen Lu, Jie zhou
We propose to learn a global probabilistic distribution for each pixel in the patch and a probabilistic metric to model the distance between distributions.
1 code implementation • ICCV 2023 • Chengkun Wang, Wenzhao Zheng, Zheng Zhu, Jie zhou, Jiwen Lu
The pretrain-finetune paradigm in modern computer vision facilitates the success of self-supervised learning, which tends to achieve better transferability than supervised learning.
2 code implementations • 9 May 2022 • Wenzhao Zheng, Chengkun Wang, Jie zhou, Jiwen Lu
This paper proposes an introspective deep metric learning (IDML) framework for uncertainty-aware comparisons of images.
1 code implementation • CVPR 2021 • Wenzhao Zheng, Chengkun Wang, Jiwen Lu, Jie zhou
In this paper, we propose a deep compositional metric learning (DCML) framework for effective and generalizable similarity measurement between images.