Search Results for author: Shuhao Cui

Found 7 papers, 6 papers with code

Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations

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.

Domain Adaptation

Gradually Vanishing Bridge for Adversarial Domain Adaptation

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.

Unsupervised Domain Adaptation

Fast Batch Nuclear-norm Maximization and Minimization for Robust Domain Adaptation

1 code implementation13 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.

Domain Adaptation

Heuristic Domain Adaptation

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.

Domain Adaptation

Unsupervised Open Domain Recognition by Semantic Discrepancy Minimization

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.

Classification General Classification

Learning Invariant Representation with Consistency and Diversity for Semi-supervised Source Hypothesis Transfer

1 code implementation7 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.

Domain Adaptation Semi-supervised Domain Adaptation

Tuning-Free Inversion-Enhanced Control for Consistent Image Editing

no code implementations22 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.

Denoising

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