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Anfeng Liu

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5 papers

1 author row

JBHI Journal 2025 Journal Article

DoctorPupil: A Virtual Reality System for Parkinson's Diagnosis Through Task-Evoked Pupil Response

Xucheng Zhang
Zhirong Wan
Jing Zhao
Xinjin Li
Anfeng Liu
Xiangmin Fan
Wei Sun
Feng Tian

Parkinson's Disease (PD) is one of the most critical neurodegenerative diseases, yet there is no cure for it, and the state-of-the-art treatment is to slow its progression. Thus, the earlier a patient with PD is recognized, the better he can be treated. Our project joins the research effort that aims to support early PD diagnosis by designing a Virtual Reality (VR)-based system to monitor pupil diameter patterns as new biomarkers (e. g. , Pupil Light Reflex and Task-evoked Pupil Response) and provide early warning of potential PD onset. A follow-up experiment with 55 participants shows that the accuracy of recognizing early PD from healthy controls could reach 0. 8942. Our study shows early results of a promising research direction that leverages VR-based technology to non-intrusively recognize patterns and provide alerts to early PD patients who would otherwise not know their symptoms until much later.

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AAAI Conference 2025 Conference Paper

MM-Mixing: Multi-Modal Mixing Alignment for 3D Understanding

Jiaze Wang
Yi Wang
Ziyu Guo
Renrui Zhang
Donghao Zhou
Guangyong Chen
Anfeng Liu
Pheng-Ann Heng

We introduce MM-Mixing, a multi-modal mixing alignment framework for 3D understanding. MM-Mixing applies mixing-based methods to multi-modal data, preserving and optimizing cross-modal connections while enhancing diversity and improving alignment across modalities. Our proposed two-stage training pipeline combines feature-level and input-level mixing to optimize the 3D encoder. The first stage employs feature-level mixing with contrastive learning to align 3D features with their corresponding modalities. The second stage incorporates both feature-level and input-level mixing, introducing mixed point cloud inputs to further refine 3D feature representations. MM-Mixing enhances intermodality relationships, promotes generalization, and ensures feature consistency while providing diverse and realistic training samples. We demonstrate that MM-Mixing significantly improves baseline performance across various learning scenarios, including zero-shot 3D classification, linear probing 3D classification, and cross-modal 3D shape retrieval. Notably, we improved the zero-shot classification accuracy on ScanObjectNN from 51.3% to 61.9%, and on Objaverse-LVIS from 46.8% to 51.4%. Our findings highlight the potential of multi-modal mixing-based alignment to significantly advance 3D object recognition and understanding while remaining straightforward to implement and integrate into existing frameworks.

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NeurIPS Conference 2025 Conference Paper

What We Miss Matters: Learning from the Overlooked in Point Cloud Transformers

Yi Wang
Jiaze Wang
Ziyu Guo
Renrui Zhang
Donghao Zhou
Guangyong Chen
Anfeng Liu
Pheng-Ann Heng

Point Cloud Transformers have become a cornerstone in 3D representation for their ability to model long-range dependencies via self-attention. However, these models tend to overemphasize salient regions while neglecting other informative regions, which limits feature diversity and compromises robustness. To address this challenge, we introduce BlindFormer, a novel contrastive attention learning framework that redefines saliency by explicitly incorporating features typically neglected by the model. The proposed Attentional Blindspot Mining (ABM) suppresses highly attended regions during training, thereby guiding the model to explore its own blind spots. This redirection of attention expands the model’s perceptual field and uncovers richer geometric cues. To consolidate these overlooked features, BlindFormer employs Blindspot-Aware Joint Optimization (BJO), a joint learning objective that integrates blindspot feature alignment with the original pretext task. BJO enhances feature discrimination while preserving performance on the primary task, leading to more robust and generalizable representations. We validate BlindFormer on several challenging benchmarks and demonstrate consistent performance gains across multiple Transformer backbones. Notably, it improves Point-MAE by +13. 4\% and PointGPT-S by +6. 3\% on OBJ-BG under Gaussian noise. These results highlight the importance of mitigating attentional biases in 3D representation learning, revealing BlindFormer’s superior ability to handle perturbations and improve feature discrimination.

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AAAI Conference 2024 Conference Paper

PointPatchMix: Point Cloud Mixing with Patch Scoring

Yi Wang
Jiaze Wang
Jinpeng Li
Zixu Zhao
Guangyong Chen
Anfeng Liu
Pheng Ann Heng

Data augmentation is an effective regularization strategy for mitigating overfitting in deep neural networks, and it plays a crucial role in 3D vision tasks, where the point cloud data is relatively limited. While mixing-based augmentation has shown promise for point clouds, previous methods mix point clouds either on block level or point level, which has constrained their ability to strike a balance between generating diverse training samples and preserving the local characteristics of point clouds. The significance of each part component of the point clouds has not been fully considered, as not all parts contribute equally to the classification task, and some parts may contain unimportant or redundant information. To overcome these challenges, we propose PointPatchMix, a novel approach that mixes point clouds at the patch level and integrates a patch scoring module to generate content-based targets for mixed point clouds. Our approach preserves local features at the patch level, while the patch scoring module assigns targets based on the content-based significance score from a pre-trained teacher model. We evaluate PointPatchMix on two benchmark datasets including ModelNet40 and ScanObjectNN, and demonstrate significant improvements over various baselines in both synthetic and real-world datasets, as well as few-shot settings. With Point-MAE as our baseline, our model surpasses previous methods by a significant margin. Furthermore, our approach shows strong generalization across various point cloud methods and enhances the robustness of the baseline model. Code is available at https://jiazewang.com/projects/pointpatchmix.html.

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TIST Journal 2019 Journal Article

A Trust Computing-based Security Routing Scheme for Cyber Physical Systems

Yuxin Liu
Xiao Liu
Anfeng Liu
Neal N. Xiong
Fang Liu

Security is a pivotal issue for the development of Cyber Physical Systems (CPS). The trusted computing of CPS includes the complete protection mechanisms, such as hardware, firmware, and software, the combination of which is responsible for enforcing a system security policy. A Trust Detection-based Secured Routing (TDSR) scheme is proposed to establish security routes from source nodes to the data center under malicious environment to ensure network security. In the TDSR scheme, sensor nodes in the routing path send detection routing to identify relay nodes’ trust. And then, data packets are routed through trustworthy nodes to sink securely. In the TDSR scheme, the detection routing is executed in those nodes that have abundant energy; thus, the network lifetime cannot be affected. Performance evaluation through simulation is carried out for success of routing ratio, compromised node detection ratio, and detection routing overhead. The experiment results show that the performance can be improved in the TDSR scheme compared to previous schemes.

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