Author name cluster

Lili Wang

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9 papers

1 author row

EAAI Journal 2025 Journal Article

Adaptive detection method for driver fatigue using facial multisource dynamic behavior fusion

Guoxin Zhang
Fei Yang
Xin Fang
Lili Wang
Lei Zhao
Chaoning Yu

Driving while fatigued is a leading cause of traffic accidents. This study proposed an adaptive detection model to recognize driver fatigue based on the dynamic facial behavior information of drivers. First, drivers’ facial fatigue features were extracted to establish a general feature space, including pupil movement, eye state, and fatigue expression parameters. A differentiated feature space was then built based on individual drivers, taking into account the homogeneity, regularity, and individual variances in drivers' facial behavior at various states. A complete adaptive fatigue feature space was built by integrating the general feature space and differentiated feature space. Finally, a driver adaptive fatigue discrimination model was constructed to classify the general and adaptive fatigue feature space to detect driver fatigue states adaptively. A driver fatigue detection dataset from real scenarios had been established to validate the performance of the proposed model. Experimental results demonstrated that the proposed method significantly improved the detection accuracy of driver fatigue. In terms of artificial intelligence, this study contributes a novel adaptive feature space construction method based on multimodal dynamic feature fusion for facial fatigue recognition; in engineering application, it develops an adaptive driver fatigue detection system grounded in multimodal dynamic behaviors, which provides real-time alerts upon detecting driver fatigue and ensures driving safety.

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EAAI Journal 2025 Journal Article

Enhanced forest fire detection via dynamic multiscale fusion and contextual partial cross features

Lili Wang
Lei Guo
Haiyan Li
Bingbing He
Jundong Yang
Yaqun Huang

Timely and accurate detection of forest fires, particularly in the early stages when smoke and small flames are present, is crucial for minimizing ecological damage and improving the effectiveness of emergency response. However, existing methods face challenges such as missing edge information, ineffective multi-scale feature fusion, and low accuracy when identifying small or distant targets in complex forest conditions. To address these issues, a novel detection framework is proposed, called the Dynamic Contextual Shallow Network (DCSNet). This framework enhances detection performance and real-time efficiency. The proposed method incorporates three key components: (1) the Contextual Partial Cross Feature Network (CPCFNet), which employs a reparameterized non-local attention mechanism and partial channel separation to strengthen contextual representation; (2) the Dynamic Multiscale Fusion Pyramid Network (DMFPN), which uses dynamic sampling and deformable convolution to fuse multi-scale features adaptively; and (3) the Shallow Feature Detection Layer (SFDL), which refines shallow features to improve the detection of small smoke and flame targets. Experimental evaluations on visible-light and infrared remote sensing datasets collected by unmanned aerial vehicles (UAVs) demonstrate the effectiveness of DCSNet. Specifically, DCSNet achieves an average mean average precision (mAP) at 50 % intersection over union (IoU) of 76. 0 %, a frame rate of 283 frames per second (FPS), and an F1-score of 71. 9 % on the visible-light dataset. On the infrared dataset, the framework achieves an mAP@50 of 77. 5 %, an mAP@50: 95 of 45. 5 %, and an F1 score of 72. 9 %. These results suggest that DCSNet provides high accuracy and robustness in the real-time detection of forest fires under diverse environmental conditions. The code and datasets are available at https: //github. com/Lili-wang-del/DCSNet.

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AAAI Conference 2025 Conference Paper

GoHD: Gaze-oriented and Highly Disentangled Portrait Animation with Rhythmic Poses and Realistic Expressions

Ziqi Zhou
Weize Quan
Hailin Shi
Wei Li
Lili Wang
Dong-Ming Yan

Audio-driven talking head generation necessitates seamless integration of audio and visual data amidst the challenges posed by diverse input portraits and intricate correlations between audio and facial motions. In response, we propose a robust framework GoHD designed to produce highly realistic, expressive, and controllable portrait videos from any reference identity with any motion. GoHD innovates with three key modules: Firstly, an animation module utilizing latent navigation is introduced to improve the generalization ability across unseen input styles. This module achieves high disentanglement of motion and identity, and it also incorporates gaze orientation to rectify unnatural eye movements that were previously overlooked. Secondly, a conformer-structured conditional diffusion model is designed to guarantee head poses that are aware of prosody. Thirdly, to estimate lip-synchronized and realistic expressions from the input audio within limited training data, a two-stage training strategy is devised to decouple frequent and frame-wise lip motion distillation from the generation of other more temporally dependent but less audio-related motions, e.g., blinks and frowns. Extensive experiments validate GoHD's advanced generalization capabilities, demonstrating its effectiveness in generating realistic talking face results on arbitrary subjects.

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JBHI Journal 2025 Journal Article

Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma From Multi-Sequence Magnetic Resonance Imaging Based on Deep Fusion Representation Learning

Haishu Ma
Lili Wang
Lingzhi Sun
Shinan Wang
Lulu Lu
Chaoyang Zhang
Yong He
Yuan Zhu

Recent studies have identified microvascular invasion (MVI) as the most vital independent biomarker associated with early tumor recurrence. With advancements in medical technology, several computational methods have been developed to predict preoperative MVI using diverse medical images. These existing methods rely on human experience, attribute selection or clinical trial testing, which is often time-consuming and labor-intensive. Leveraging the advantages of deep learning, this study presents a novel end-to-end algorithm for predicting MVI prior to surgery. We devised a series of data preprocessing strategies to fully extract multi-view features from the data while preserving peritumoral information. Notably, a new multi-branch deep fused feature algorithm based on ResNet (DFFResNet) is introduced, which combines Magnetic Resonance Images (MRI) from different sequences to enhance information complementarity and integration. We conducted prediction experiments on a dataset from the Radiology Department of the First Hospital of Lanzhou University, comprising 117 individuals and seven MRI sequences. The model was trained on 80% of the data using 10-fold cross-validation, and the remaining 20% were used for testing. This evaluation was processed in two cases: CROI, containing samples with a complete region of interest (ROI), and PROI, containing samples with a partial ROI region. The robustness results from repeated experiments at both image and patient levels demonstrate the superior performance and improved generalization of the proposed method compared to alternative models. Our approach yields highly competitive prediction results even when the ROI region outline is incomplete, offering a novel and effective multi-sequence fused strategy for predicting preoperative MVI.

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AAAI Conference 2025 Conference Paper

QCS:Feature Refining from Quadruplet Cross Similarity for Facial Expression Recognition

Chengpeng Wang
Li Chen
Lili Wang
Zhaofan Li
Xuebin Lv

Facial expression recognition faces challenges where labeled significant features in datasets are mixed with unlabeled redundant ones. In this paper, we introduce Cross Similarity Attention (CSA) to mine richer intrinsic information from image pairs, overcoming a limitation when the Scaled Dot-Product Attention of ViT is directly applied to calculate the similarity between two different images. Based on CSA, we simultaneously minimize intra-class differences and maximize inter-class differences at the fine-grained feature level through interactions among multiple branches. Contrastive residual distillation is utilized to transfer the information learned in the cross module back to the base network. We ingeniously design a four-branch centrally symmetric network, named Quadruplet Cross Similarity (QCS), which alleviates gradient conflicts arising from the cross module and achieves balanced and stable training. It can adaptively extract discriminative features while isolating redundant ones. The cross-attention modules exist during training, and only one base branch is retained during inference, resulting in no increase in inference time. Extensive experiments show that our proposed method achieves state-of-the-art performance on several FER datasets.

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JBHI Journal 2024 Journal Article

Three-Direction Fusion for Accurate Volumetric Liver and Tumor Segmentation

Feng Zhan
Wenwu Wang
Qian Chen
Yina Guo
Lidan He
Lili Wang

Biomedical image segmentation of organs, tissues and lesions has gained increasing attention in clinical treatment planning and navigation, which involves the exploration of two-dimensional (2D) and three-dimensional (3D) contexts in the biomedical image. Compared to 2D methods, 3D methods pay more attention to inter-slice correlations, which offer additional spatial information for image segmentation. An organ or tumor has a 3D structure that can be observed from three directions. Previous studies focus only on the vertical axis, limiting the understanding of the relationship between a tumor and its surrounding tissues. Important information can also be obtained from sagittal and coronal axes. Therefore, spatial information of organs and tumors can be obtained from three directions, i. e. the sagittal, coronal and vertical axes, to understand better the invasion depth of tumor and its relationship with the surrounding tissues. Moreover, the edges of organs and tumors in biomedical image may be blurred. To address these problems, we propose a three-direction fusion volumetric segmentation (TFVS) model for segmenting 3D biomedical images from three perspectives in sagittal, coronal and transverse planes, respectively. We use the dataset of the liver task provided by the Medical Segmentation Decathlon challenge to train our model. The TFVS method demonstrates a competitive performance on the 3D-IRCADB dataset. In addition, the t-test and Wilcoxon signed-rank test are also performed to show the statistical significance of the improvement by the proposed method as compared with the baseline methods. The proposed method is expected to be beneficial in guiding and facilitating clinical diagnosis and treatment.

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AAAI Conference 2021 Conference Paper

Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path

Lili Wang
Chongyang Gao
Chenghan Huang
Ruibo Liu
Weicheng Ma
Soroush Vosoughi

Networks found in the real-world are numerous and varied. A common type of network is the heterogeneous network, where the nodes (and edges) can be of different types. Accordingly, there have been efforts at learning representations of these heterogeneous networks in low-dimensional space. However, most of the existing heterogeneous network embedding methods suffer from the following two drawbacks: (1) The target space is usually Euclidean. Conversely, many recent works have shown that complex networks may have hyperbolic latent anatomy, which is non-Euclidean. (2) These methods usually rely on meta-paths, which require domainspecific prior knowledge for meta-path selection. Additionally, different down-streaming tasks on the same network might require different meta-paths in order to generate taskspecific embeddings. In this paper, we propose a novel selfguided random walk method that does not require metapath for embedding heterogeneous networks into hyperbolic space. We conduct thorough experiments for the tasks of network reconstruction and link prediction on two public datasets, showing that our model outperforms a variety of well-known baselines across all tasks.

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AAAI Conference 2021 Conference Paper

Mitigating Political Bias in Language Models through Reinforced Calibration

Ruibo Liu
Chenyan Jia
Jason Wei
Guangxuan Xu
Lili Wang
Soroush Vosoughi

Current large-scale language models can be politically biased as a result of the data they are trained on, potentially causing serious problems when they are deployed in realworld settings. In this paper, we describe metrics for measuring political bias in GPT-2 generation and propose a reinforcement learning (RL) framework for mitigating political biases in generated text. By using rewards from word embeddings or a classifier, our RL framework guides debiased generation without having access to the training data or requiring the model to be retrained. In empirical experiments on three attributes sensitive to political bias (gender, location, and topic), our methods reduced bias according to both our metrics and human evaluation, while maintaining readability and semantic coherence.

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AAAI Conference 2021 Conference Paper

Reinforcement Learning Based Multi-Agent Resilient Control: From Deep Neural Networks to an Adaptive Law

Jian Hou
Fangyuan Wang
Lili Wang
Zhiyong Chen

Recent advances in Multi-agent Reinforcement Learning (MARL) have made it possible to implement various tasks in cooperative as well as competitive scenarios through trial and error, and deep neural networks. These successes motivate us to bring the mechanism of MARL into the Multi-agent Resilient Consensus (MARC) problem that studies the consensus problem in a network of agents with faulty ones. Relying on the natural characteristics of the system goal, the key component in MARL, reward function, can thus be directly constructed via the relative distance among agents. Firstly, we apply Deep Deterministic Policy Gradient (DDPG) on each single agent to train and learn adjacent weights of neighboring agents in a distributed manner, that we call Distributed- DDPG (D-DDPG), so as to minimize the weights from suspicious agents and eliminate the corresponding influences. Secondly, to get rid of neural networks and their time-consuming training process, a Q-learning based algorithm, called Qconsensus, is further presented by building a proper reward function and a credibility function for each pair of neighboring agents so that the adjacent weights can update in an adaptive way. The experimental results indicate that both algorithms perform well with appearance of constant and/or random faulty agents, yet the Q-consensus algorithm outperforms the faulty ones running D-DDPG. Compared to the traditional resilient consensus strategies, e. g. , Weighted-Mean- Subsequence-Reduced (W-MSR) or trustworthiness analysis, the proposed Q-consensus algorithm has greatly relaxed the topology requirements, as well as reduced the storage and computation loads. Finally, a smart-car hardware platform consisting of six vehicles is used to verify the effectiveness of the Q-consensus algorithm by achieving resilient velocity synchronization.

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