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Wenbin Guo

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3 papers

2 author rows

AAAI Conference 2026 Conference Paper

rMMEA: Robust Multi-Modal Entity Alignment with Missing and Noise Visual Modality

Lingbing Guo
Zhuo Chen
Yichi Zhang
Wenbin Guo
Haonan Yang
Zhao Li
Zirui Chen
Xin Wang

Recently, multi-modal embedding methods have flourished in entity alignment. As state-of-the-art approaches evolve rapidly, visual modality (i.e., images) missing emerges as a critical challenge. While visual modality typically offers the most informative signals in multi-modal entity alignment (MMEA), it is frequently unavailable for many entities. The existing methods commonly use dummy vectors to represent visual-missing embeddings, which negatively impacts both model training and inference. In this paper, we propose robust multi-modal entity alignment (rMMEA), which leverages ranking-based knowledge distillation and mutual information (MI) estimation to address missing modalities while enhancing noise robustness. Unlike conventional teacher-student distillation that requires the student to replicate teacher outputs, our rMMEA learns soft rankings from pure and complete modality sides while capturing implicit key semantics of teacher embeddings through mutual information maximization, allowing rMMEA to avoid strict point-to-point alignment. The experimental results across multiple benchmarks and settings demonstrate that rMMEA significantly outperforms the state-of-the-art anti-modality-missing methods in terms of effectiveness and efficiency.

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ECAI Conference 2025 Conference Paper

GCQ-ViT: Group-Aware Collaborative Post-Training Quantization for Vision Transformers

Pan Peng 0006
Wenbin Guo
Ping Wei
Wei Zhou 0011

Post-training quantization (PTQ) is widely utilized in Vision Transformers (ViTs) for its computational efficiency and retraining elimination. However, the unique architecture of ViTs introduces significant quantization challenges. Dynamic fluctuations in channel activations, particularly post-LayerNorm, result in distributional mismatches. Additionally, the heavy-tailed nature of post-Softmax activations compromises the accurate representation of critical attention regions, vital for ViT performance. Moreover, weight quantization at low bit-widths leads to a loss of structural information, degrading global feature representation. To address these challenges, we introduce the Group-aware Collaborative Quantization framework (GCQ-ViT), which significantly improves both the accuracy and efficiency of ViT quantization. The GCQ-ViT framework integrates a novel dynamic perception grouping quantization mechanism to ensure distributional consistency within groups, thus reducing hardware expense. It also utilizes a self-adaptive displaced uniform log2 quantizer, optimizing shift factors and nonlinear intervals to enhance representation in high-density regions of post-Softmax activations. Additionally, we propose a dynamic dimension-aware error compensation method to correct quantization errors across channel dimensions using a residual mean compensation skill, ensuring robust feature preservation. Extensive experiments on image classification, object detection, and instance segmentation tasks demonstrate that GCQ-ViT outperforms the current leading PTQ methods, setting a new benchmark for ViT quantization.

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YNICL Journal 2020 Journal Article

Altered resting-state dynamic functional brain networks in major depressive disorder: Findings from the REST-meta-MDD consortium

Yicheng Long
Hengyi Cao
Chaogan Yan
Xiao Chen
Le Li
Francisco Xavier Castellanos
Tongjian Bai
Qijing Bo

BACKGROUND: Major depressive disorder (MDD) is known to be characterized by altered brain functional connectivity (FC) patterns. However, whether and how the features of dynamic FC would change in patients with MDD are unclear. In this study, we aimed to characterize dynamic FC in MDD using a large multi-site sample and a novel dynamic network-based approach. METHODS: Resting-state functional magnetic resonance imaging (fMRI) data were acquired from a total of 460 MDD patients and 473 healthy controls, as a part of the REST-meta-MDD consortium. Resting-state dynamic functional brain networks were constructed for each subject by a sliding-window approach. Multiple spatio-temporal features of dynamic brain networks, including temporal variability, temporal clustering and temporal efficiency, were then compared between patients and healthy subjects at both global and local levels. RESULTS: ). Corresponding local changes in MDD were mainly found in the default-mode, sensorimotor and subcortical areas. Measures of temporal variability and characteristic temporal path length were significantly correlated with depression severity in patients (corrected p < 0.05). Moreover, the observed between-group differences were robustly present in both first-episode, drug-naïve (FEDN) and non-FEDN patients. CONCLUSIONS: Our findings suggest that excessive temporal variations of brain FC, reflecting abnormal communications between large-scale bran networks over time, may underlie the neuropathology of MDD.

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