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Qiming Li

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4 papers

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AAAI Conference 2026 Conference Paper

Causal Tracing of Object Representations in Large Vision Language Models: Mechanistic Interpretability and Hallucination Mitigation

Qiming Li
Zekai Ye
Xiaocheng Feng
Weihong Zhong
Weitao Ma
Xiachong Feng

Despite the remarkable advancements of Large Vision-Language Models (LVLMs), the mechanistic interpretability remains underexplored. Existing analyses are insufficiently comprehensive and lack examination covering visual and textual tokens, model components, and the full range of layers. This limitation restricts actionable insights to improve the faithfulness of model output and the development of downstream tasks, such as hallucination mitigation. To address this limitation, we introduce Fine-grained Cross-modal Causal Tracing (FCCT) framework, which systematically quantifies the causal effects on visual object perception. FCCT conducts fine-grained analysis covering the full range of visual and textual tokens, three core model components including multi-head self-attention (MHSA), feed-forward networks (FFNs), and hidden states, across all decoder layers. Our analysis is the first to demonstrate that MHSAs of the last token in middle layers play a critical role in aggregating cross-modal information, while FFNs exhibit a three-stage hierarchical progression for the storage and transfer of visual object representations. Building on these insights, we propose Intermediate Representation Injection (IRI), a training-free inference-time technique that reinforces visual object information flow by precisely intervening on cross-modal representations at specific components and layers, thereby enhancing perception and mitigating hallucination. Consistent improvements across five widely used benchmarks and LVLMs demonstrate IRI achieves state-of-the-art performance, while preserving inference speed and other foundational performance.

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AAAI Conference 2026 Conference Paper

Gaussian Uncertainty-Driven Multi-Model Fitting with Graph Neural Network

Ligang Zhang
Jun Li
Qiming Li

Multi-model fitting is fundamental for robust geometric estimation in computer vision. However, recent deep learning methods enable parallel model detection but rely on simple architectures that inadequately model spatial relationships. Moreover, current methods typically generate hypotheses only through minimal solvers on randomly sampled points, thus failing to explore the full diversity of the solution space. To address these limitations, we propose a novel Jacobian-based Gaussian uncertainty modeling framework, which analytically propagates covariance through geometric transformations and enables efficient expansion of the hypothesis space with strong theoretical guarantees. We further introduce a Gaussian Hypothesis Generation Network (GHG-Net) to learn global parameter distributions, enabling the generation of diverse and geometrically valid hypotheses. Additionally, our network captures spatial relationships among observations by employing a dynamic graph neural network with a multi-head attention mechanism. This yields more accurate sample and inlier weights, significantly improving the quality of hypothesis generation. Extensive experiments on three representative geometric estimation tasks (i.e. vanishing point detection, fundamental matrix estimation, and homography estimation) demonstrate that our method achieves new state-of-the-art accuracy and stability, while maintaining high computational efficiency.

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EAAI Journal 2025 Journal Article

BIGC-Net: A Body Inter-intra-parts Graph Convolutional Network for repetitive action counting

Jun Li
Jinying Wu
Qiming Li
Bangshu Xiong

With the continuous development of human pose estimation techniques, researchers have gradually applied them to the field of repetitive action counting, resulting in pose-level methods. However, the current researches on the pose-level are still limited. Therefore, this paper proposes a simple but efficient Body Inter-intra-parts Graph Convolutional Network (BIGC-Net). Specifically, two core modules are developed in BIGC-Net: the Global Inter-Part Feature Learning Module (GIFL-Module) and the Salient Intra-Part Feature Learning Module (SIFL-Module). Unlike previous pose-level methods, which only model human joints globally and ignore local details. Instead, we innovatively introduce the concept of body parts with Graph Convolutional Networks (GCN) to the repetitive action counting task. Based on the natural topology of the human body, we divide the joints into multiple inter-intra-parts, each of which is regarded as a subgraph to form the overall graph structure. The complete action is then achieved by the collaborative operation between different subgraphs, thus modelling the action execution process more accurately. Therefore, the GIFL-Module is designed to capture the global collaborative relationships between the subgraphs. However, since the body joints are segmented into multiple parts, this segmentation may ignore the variation of local detail information within the subgraphs. To address this issue, the SIFL-Module aims to capture the local interdependencies between joints within the subgraphs, and the ability to focus on the most salient features of the subgraphs as it moves. The collaboration of these two modules further enhances the feature representation capability. Finally, extensive experimental results on the challenging benchmark datasets (RepCount-pose, UCFRep-pose, and Countix-Fitness-pose) show that the proposed BIGC-Net achieves excellent performance.

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JBHI Journal 2024 Journal Article

Predicting ICU Interventions: A Transparent Decision Support Model Based on Multivariate Time Series Graph Convolutional Neural Network

Zhen Xu
Jinjin Guo
Lang Qin
Yuntao Xie
Yao Xiao
Xinran Lin
Qiming Li
Xinyang Li

In this study, we present a novel approach for predicting interventions for patients in the intensive care unit using a multivariate time series graph convolutional neural network. Our method addresses two critical challenges: the need for timely and accurate decisions based on changing physiological signals, drug administration information, and static characteristics; and the need for interpretability in the decision-making process. Drawing on real-world ICU records from the MIMIC-III dataset, we demonstrate that our approach significantly improves upon existing machine learning and deep learning methods for predicting two targeted interventions, mechanical ventilation and vasopressors. Our model achieved an accuracy improvement from 81. 6% to 91. 9% and a F1 score improvement from 0. 524 to 0. 606 for predicting mechanical ventilation interventions. For predicting vasopressor interventions, our model achieved an accuracy improvement from 76. 3% to 82. 7% and a F1 score improvement from 0. 509 to 0. 619. We also assessed the interpretability by performing an adjacency matrix importance analysis, which revealed that our model uses clinically meaningful and appropriate features for prediction. This critical aspect can help clinicians gain insights into the underlying mechanisms of interventions, allowing them to make more informed and precise clinical decisions. Overall, our study represents a significant step forward in the development of decision support systems for ICU patient care, providing a powerful tool for improving clinical outcomes and enhancing patient safety.

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