Zhongliang Guo

Orcid: 0000-0002-6025-3021

Affiliations:
  • University of St Andrews, Scotland, UK


According to our database1, Zhongliang Guo authored at least 13 papers between 2023 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Online presence:

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Bibliography

2025
Perspective-assisted prototype-based learning for semi-supervised crowd counting.
Pattern Recognit., 2025

2024
Semi-Supervised Crowd Counting With Contextual Modeling: Facilitating Holistic Understanding of Crowd Scenes.
IEEE Trans. Circuits Syst. Video Technol., September, 2024

Correction: Guo et al. A Siamese Transformer Network for Zero-Shot Ancient Coin Classification. J. Imaging 2023, 9, 107.
J. Imaging, March, 2024

MMAD-Purify: A Precision-Optimized Framework for Efficient and Scalable Multi-Modal Attacks.
CoRR, 2024

Instant Adversarial Purification with Adversarial Consistency Distillation.
CoRR, 2024

A Grey-box Attack against Latent Diffusion Model-based Image Editing by Posterior Collapse.
CoRR, 2024

Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges.
CoRR, 2024

A Survey of Backdoor Attacks and Defenses on Large Language Models: Implications for Security Measures.
CoRR, 2024

Artwork Protection Against Neural Style Transfer Using Locally Adaptive Adversarial Color Attack.
CoRR, 2024

Artwork Protection Against Neural Style Transfer Using Locally Adaptive Adversarial Color Attack.
Proceedings of the ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain, 2024

A White-Box False Positive Adversarial Attack Method on Contrastive Loss Based Offline Handwritten Signature Verification Models.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
A Siamese Transformer Network for Zero-Shot Ancient Coin Classification.
J. Imaging, 2023

A White-Box False Positive Adversarial Attack Method on Contrastive Loss-Based Offline Handwritten Signature Verification Models.
CoRR, 2023


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