no code implementations • 22 Dec 2023 • Xiaoyue Duan, Shuhao Cui, Guoliang Kang, Baochang Zhang, Zhengcong Fei, Mingyuan Fan, Junshi Huang
Consistent editing of real images is a challenging task, as it requires performing non-rigid edits (e. g., changing postures) to the main objects in the input image without changing their identity or attributes.
1 code implementation • 13 Jul 2021 • Shuhao Cui, Shuhui Wang, Junbao Zhuo, Liang Li, Qingming Huang, Qi Tian
Due to the domain discrepancy in visual domain adaptation, the performance of source model degrades when bumping into the high data density near decision boundary in target domain.
1 code implementation • 7 Jul 2021 • Xiaodong Wang, Junbao Zhuo, Shuhao Cui, Shuhui Wang
Semi-supervised domain adaptation (SSDA) aims to solve tasks in target domain by utilizing transferable information learned from the available source domain and a few labeled target data.
1 code implementation • NeurIPS 2020 • Shuhao Cui, Xuan Jin, Shuhui Wang, Yuan He, Qingming Huang
In visual domain adaptation (DA), separating the domain-specific characteristics from the domain-invariant representations is an ill-posed problem.
2 code implementations • CVPR 2020 • Shuhao Cui, Shuhui Wang, Junbao Zhuo, Chi Su, Qingming Huang, Qi Tian
On the discriminator, GVB contributes to enhance the discriminating ability, and balance the adversarial training process.
2 code implementations • CVPR 2020 • Shuhao Cui, Shuhui Wang, Junbao Zhuo, Liang Li, Qingming Huang, Qi Tian
We find by theoretical analysis that the prediction discriminability and diversity could be separately measured by the Frobenius-norm and rank of the batch output matrix.
1 code implementation • CVPR 2019 • Junbao Zhuo, Shuhui Wang, Shuhao Cui, Qingming Huang
We address the unsupervised open domain recognition (UODR) problem, where categories in labeled source domain S is only a subset of those in unlabeled target domain T. The task is to correctly classify all samples in T including known and unknown categories.