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Hanzhang Wang

4 papers hereh-index 472 citations10 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV3
  • cs.LG1
same name
  • Hanzhang Wang — 3 papers, h 14
  • Hanzhang Wang — 2 papers, h 1
  • Hanzhang Wang — 1 paper, h 3
  • Hanzhang Wang — 1 paper, h 0
  • Hanzhang Wang — 1 paper, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedHiCAST: Highly Customized Arbitrary Style Transfer with Adapter Enhanced Diffusion Models

2 citations · 3 across the 3 of their papers we have counts for

collaborators

4 papers

cs.CV2026

Safety-Potential Pruning for Enhancing Safety Prompts Against VLM Jailbreaking Without Retraining

Chongxin Li, Hanzhang Wang, Lian Duan

Safety prompts constitute an interpretable layer of defense against jailbreak attacks in vision-language models (VLMs); however, their efficacy is constrained by the models' latent…

cs.CV2024★ 1 cited

SGD: Street View Synthesis with Gaussian Splatting and Diffusion Prior

Zhongrui Yu, Haoran Wang, Jinze Yang +6

Novel View Synthesis (NVS) for street scenes play a critical role in the autonomous driving simulation. The current mainstream technique to achieve it is neural rendering, such as…

cs.CV2024★ 2 cited

HiCAST: Highly Customized Arbitrary Style Transfer with Adapter Enhanced Diffusion Models

Hanzhang Wang, Haoran Wang, Jinze Yang +7

The goal of Arbitrary Style Transfer (AST) is injecting the artistic features of a style reference into a given image/video. Existing methods usually focus on pursuing the balance…

cs.LG2023

On the Dynamics Under the Unhinged Loss and Beyond

Xiong Zhou, Xianming Liu, Hanzhang Wang +3

Recent works have studied implicit biases in deep learning, especially the behavior of last-layer features and classifier weights. However, they usually need to simplify the interm…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.