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Research
  1. Queen Mary University of London
  2. Research
  3. Publications

Publications: DR Ziquan Liu

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Cao Y, Liu Z, Zhang Z, Deng J, Gong S, Song J ( 2026 ) . LiteVSR: Enabling Cross-Domain Fine-Grained Detail Generation in Light-Weight Transformers for Video Super-Resolution . Conference: International Conference on Machine Learning
Cheng Z, Hu J, Liu Z, Si C, Li W, Gong S ( 2026 ) . V-star: Benchmarking video-llms on video spatio-temporal reasoning . Conference: IEEE Conference on Computer Vision and Pattern Recognition
Gaintseva T, Oncescu A-M, Ma C, Liu Z, Benning M, Deng J, Slabaugh G, Ismail E ( 2026 ) . CASteer: Cross-Attention Steering for Controllable Concept Erasure . Conference: International Conference on Learning Representations
Jia X, Shi Y, Liu Z, Xu Y, Yan Y . Cost-Sensitive Conformal Training with Provably Controllable Learning Bounds . Proceedings of the AAAI Conference on Artificial Intelligence . vol. 40 , 22274 - 22282 .
Wan J, Liu Z, Gao J, Wu X, Chan AB ( 2026 ) . Adaptive momentum weight averaging reduces initialization noise . Pattern Recognition vol. 171 ,
Chen D, Liu Z, Yang C, Wang D, Yan Y, Xu Y, Ji X ( 2025 ) . ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction . Conference: 2025 IEEE/CVF International Conference on Computer Vision (ICCV) vol. 00 , 24045 - 24055 .
Zuo J, Hu H, Zhou Z, Cui Y, Liu Z, Wang J, Guan N, Wang J et al. ( 2025 ) . RALAD: Bridging the Real-to-Sim Domain Gap in Autonomous Driving with Retrieval-Augmented Learning . Conference: 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) vol. 00 , 17001 - 17007 .
Wu Q, Yu Y, Kong C, Liu Z, Wan J, Li H, Kot A, Chan A ( 2025 ) . Temporal Unlearnable Examples: Preventing Personal Video Data from Unauthorized Exploitation by Object Tracking . Conference: International Conference on Computer Vision
Zhi Z, Liu Z, Elbadawi M, Daneshmend A, Orlu M, Basit A, Demosthenous A, Rodrigues M ( 2025 ) . Borrowing treasures from neighbors: In-context learning for multimodal learning with missing modalities and data scarcity . Neurocomputing vol. 647 ,
Zhao Z, Liu Z, Cao Y, Gong S, Patras I ( 2025 ) . AIM-Fair: Advancing Algorithmic Fairness via Selectively Fine-Tuning Biased Models with Contextual Synthetic Data . Conference: 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) vol. 00 , 28748 - 28758 .
Feng C, Liu Z, Zhi Z, Bogunovic I, Gerner-Beuerle C, Rodrigues M . PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks . Proceedings of the AAAI Conference on Artificial Intelligence . vol. 39 , 2933 - 2941 .
Zhao Z, Liu Z, Cao Y, Gong S, Patras I ( 2025 ) . AIM-Fair: Advancing Algorithmic Fairness via Selectively Fine-Tuning Biased Models with Contextual Synthetic Data . Conference: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ( Nashville, US ) from: 11/06/2025 to: 16/06/2025 ,
Zhi Z, Sun Y, Wu Q, Liu Z, Rodrigues M ( 2025 ) . Wasserstein Modality Alignment Makes Your Multimodal Transformer More Robust . Transactions on Machine Learning Research
Zhang Z, Liu Z, Patras I ( 2025 ) . Get Confused Cautiously: Textual Sequence Memorization Erasure with Selective Entropy Maximization . Proceedings International Conference on Computational Linguistics Coling . 10924 - 10939 .
Alballa N, Zhang W, Liu Z, Abdelmoniem AM, Elhoseiny M, Canini M ( 2025 ) . QUERY-BASED KNOWLEDGE TRANSFER FOR HETEROGENEOUS LEARNING ENVIRONMENTS . 13th International Conference on Learning Representations Iclr 2025 . 95820 - 95851 .
Zhi Z, Feng C, Daneshmend A, Orlu M, Demosthenous A, Yin L, Li D, Liu Z et al. ( 2025 ) . TFAR: A Training-Free Framework for Autonomous Reliable Reasoning in Visual Question Answering . Transactions on Machine Learning Research vol. 2025-August ,
Chen F, Lin W, Liu Z, Chan AB ( 2025 ) . A Secure Image Watermarking Framework with Statistical Guarantees via Adversarial Attacks on Secret Key Networks . Lecture Notes in Computer Science . vol. 15098 , 428 - 445 .
Zhang Z, Liu Z, Patras I ( 2024 ) . Get Confused Cautiously: Textual Sequence Memorization Erasure with Selective Entropy Maximization .
Cui Y, Mao Y, Liu Z, Li Q, Chan AB, Liu X, Kuo T-W, Xue CJ ( 2023 ) . Variational Nested Dropout . IEEE Transactions on Pattern Analysis and Machine Intelligence vol. 45 , ( 8 ) 10519 - 10534 .
Wu Q, Yang T, Liu Z, Wu B, Shan Y, Chan AB ( 2023 ) . DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking Tasks . Conference: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) vol. 00 , 14561 - 14571 .
Liu Z, Xu Y, Ji X, Chan AB ( 2023 ) . TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization . Conference: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) vol. 00 , 16436 - 16446 .
Lan H, Liu Z, Hsiao JH, Yu D, Chan AB ( 2023 ) . Clustering Hidden Markov Models With Variational Bayesian Hierarchical EM . IEEE Transactions on Neural Networks and Learning Systems vol. 34 , ( 3 ) 1537 - 1551 .
Cui Y, Liu Z, Liu X, Liu X, Wang C, Kuo TW, Xue CJ, Chan AB ( 2023 ) . BAYES-MIL: A NEW PROBABILISTIC PERSPECTIVE ON ATTENTION-BASED MULTIPLE INSTANCE LEARNING FOR WHOLE SLIDE IMAGES . 11th International Conference on Learning Representations Iclr 2023 .
Cui Y, Liu Z, Chen Y, Lu Y, Yu X, Liu X, Kuo TW, Rodrigues MRD et al. ( 2023 ) . Retrieval-Augmented Multiple Instance Learning . Advances in Neural Information Processing Systems . vol. 36 ,
Liu Z, Yu L, Hsiao JH, Chan AB ( 2022 ) . PRIMAL-GMM: PaRametrIc MAnifold Learning of Gaussian Mixture Models . IEEE Transactions on Pattern Analysis and Machine Intelligence vol. 44 , ( 6 ) 3197 - 3211 .
Liu Z, Chan AB ( 2022 ) . Boosting Adversarial Robustness From The Perspective of Effective Margin Regularization . Bmvc 2022 33rd British Machine Vision Conference Proceedings .
Liu Z, Xu Y, Xu Y, Qian Q, Li H, Ji X, Chan AB, Jin R ( 2022 ) . Improved Fine-Tuning by Better Leveraging Pre-Training Data . Advances in Neural Information Processing Systems . vol. 35 ,
Wan J, Liu Z, Chan AB ( 2021 ) . A Generalized Loss Function for Crowd Counting and Localization . Conference: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) vol. 00 , 1974 - 1983 .
Cui Y, Liu Z, Li Q, Chan AB, Xue CJ ( 2021 ) . Bayesian Nested Neural Networks for Uncertainty Calibration and Adaptive Compression . Conference: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) vol. 00 , 2392 - 2401 .
Cui Y, Liu Z, Yao W, Li Q, Chan AB, Kuo TW, Xue CJ ( 2020 ) . Fully nested neural network for adaptive compression and quantization . Ijcai International Joint Conference on Artificial Intelligence . vol. 2021-January , 2080 - 2087 .
Liu Z, Yu L, Hsiao JH, Chan AB ( 2019 ) . Parametric Manifold Learning of Gaussian Mixture Models . Conference: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence3073 - 3079 .
Zhang Z, Li C, Liu X, Shen C, Liu Z, Patras I . Confidence Should Be Calibrated More Than One Turn Deep . Conference: The 64th Annual Meeting of the Association for Computational Linguistics
Masoud R, Liu Z, Ferianc M, Treleaven P, Rodrigues M . Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensions . Conference: International Conference on Computational Linguistics
Zhang Z, Liu Z, Patras I . GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models . Conference: The 64th Annual Meeting of the Association for Computational Linguistics
Gaintseva T, Stepanov A, Liu Z, Benning M, Slabaugh G, Deng J, Elezi I . MidSteer: Optimal Affine Framework for Steering Generative Models . Conference: International Conference on Machine Learning from: 06/06/2026 to: 11/07/2026 ,
Kappiyath A, Chaudhuri A, Jaiswal AK, Liu Z, Li Y, Zhu X, Yin L . SEBRA: Debiasing through Self-Guided Bias Ranking . Conference: International Conference on Learning Representations
Liu Z, Cui Y, Yan Y, Xu Y, Ji X, Liu X, Chan A . The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks . Conference: International Conference on Machine Learning ( Vienna, Austria ) from: 21/07/2024 to: 27/07/2024 ,
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