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Hui Gao

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10 papers

2 author rows

AAAI Conference 2026 Conference Paper

Fine-Grained DINO Tuning with Dual Supervision for Face Forgery Detection

Tianxiang Zhang
Peipeng Yu
Zhihua Xia
Longchen Dai
Xiaoyu Zhou
Hui Gao

The proliferation of sophisticated deepfakes poses significant threats to information integrity. While DINOv2 shows promise for detection, existing fine-tuning approaches treat it as generic binary classification, overlooking distinct artifacts inherent to different deepfake methods. To address this, we propose a DeepFake Fine-Grained Adapter (DFF-Adapter) for DINOv2. Our method incorporates lightweight multi-head LoRA modules into every transformer block, enabling efficient backbone adaptation. DFF-Adapter simultaneously addresses authenticity detection and fine-grained manipulation type classification, where classifying forgery methods enhances artifact sensitivity. We introduce a shared branch propagating fine-grained manipulation cues to the authenticity head. This enables multi-task cooperative optimization, explicitly enhancing authenticity discrimination with manipulation-specific knowledge. Utilizing only 3.5M trainable parameters, our parameter-efficient approach achieves detection accuracy comparable to or even surpassing that of current complex state-of-the-art methods.

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TCS Journal 2025 Journal Article

Algorithms for Shortest Path Tour Problem

Yucen Gao
Zhuoran Li
Jingyu He
Jun Fang
Hui Gao
Xiaofeng Gao
Guihai Chen

Carpooling route planning becomes an important problem with the growth of low-carbon traffic systems. When each passenger has multiple potential pick-up/drop-off locations, the problem will be more challenging. In the paper, we discussed a simplified carpooling route planning problem, namely the Shortest Path Tour Problem (SPTP), whose aim is to find a single-origin single-destination shortest path through an ordered sequence of disjoint node subsets. We propose Stage Dijkstra and Global Dijkstra algorithms to find the optimal shortest path, with the time complexity of O ( l ( n + m ) log ⁡ n ) and O ( l ( n + m ) log ⁡ ( l n ) ) respectively, where l represents the number of node subsets. To the best of our knowledge, O ( l ( n + m ) log ⁡ n ) is the best time complexity of the exact algorithms for SPTP. Besides, the Stage Dijkstra and Global Dijkstra algorithms both have the linear space complexity, which is highly suitable for resource-constrained environments. Experiments conducted on large-scale road networks and synthetic datasets demonstrate the effectiveness and efficiency of our proposed algorithms in terms of running time and memory consumption.

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EAAI Journal 2025 Journal Article

An integrated feature extraction framework of linear multi-layer perceptron to reduce computation complexity for remaining useful life prediction

Hui Gao
Qingwen Guo
Zhizheng Zhang
Yibin Li

Recently, there has been a growth in deep learning-based solutions for RUL prediction, although these increasingly complex models have significantly improved prediction performance, these studies typically overlook the computational and storage resources required for model deployment. Thus, we attempt to construct a lightweight model based on a simple linear multi-layer perceptron (MLP) that achieves prediction performance comparable to complex models, while ensuring easier deployment on resource-constrained edge devices. Firstly, a feature reconstruction method based on unsupervised clustering is proposed, which uses the K-means algorithm to perform unsupervised clustering on the variable operating condition data, and then standardization is conducted according to the mean and variance of each class, so as to separate the degradation features from the operating condition. Then, we propose a time-series linear extractor (TiLE) architecture for extracting degradation features from multi-sensor data. This lightweight framework achieves the advantage of linear computational scalability, which improves the inference efficiency of the model. The feature recalibration mechanism of TiLE is designed to reduce the interference of random factors, which is conducive to improving the prediction accuracy. Experimental results on the NASA turbine engine dataset show that the TiLE-based model outperforms state-of-the-art methods while achieving superior computational complexity and inference efficiency.

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ICML Conference 2025 Conference Paper

Unlocking the Capabilities of Large Vision-Language Models for Generalizable and Explainable Deepfake Detection

Peipeng Yu
Jianwei Fei
Hui Gao
Xuan Feng 0002
Zhihua Xia
Chip-Hong Chang

Current Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in understanding multimodal data, but their potential remains underexplored for deepfake detection due to the misalignment of their knowledge and forensics patterns. To this end, we present a novel framework that unlocks LVLMs’ potential capabilities for deepfake detection. Our framework includes a Knowledge-guided Forgery Detector (KFD), a Forgery Prompt Learner (FPL), and a Large Language Model (LLM). The KFD is used to calculate correlations between image features and pristine/deepfake image description embeddings, enabling forgery classification and localization. The outputs of the KFD are subsequently processed by the Forgery Prompt Learner to construct fine-grained forgery prompt embeddings. These embeddings, along with visual and question prompt embeddings, are fed into the LLM to generate textual detection responses. Extensive experiments on multiple benchmarks, including FF++, CDF2, DFD, DFDCP, DFDC, and DF40, demonstrate that our scheme surpasses state-of-the-art methods in generalization performance, while also supporting multi-turn dialogue capabilities.

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ICML Conference 2024 Conference Paper

Quantum Implicit Neural Representations

Jiaming Zhao
Wenbo Qiao
Peng Zhang
Hui Gao

Implicit neural representations have emerged as a powerful paradigm to represent signals such as images and sounds. This approach aims to utilize neural networks to parameterize the implicit function of the signal. However, when representing implicit functions, traditional neural networks such as ReLU-based multilayer perceptrons face challenges in accurately modeling high-frequency components of signals. Recent research has begun to explore the use of Fourier Neural Networks (FNNs) to overcome this limitation. In this paper, we propose Quantum Implicit Representation Network (QIREN), a novel quantum generalization of FNNs. Furthermore, through theoretical analysis, we demonstrate that QIREN possesses a quantum advantage over classical FNNs. Lastly, we conducted experiments in signal representation, image superresolution, and image generation tasks to show the superior performance of QIREN compared to state-of-the-art (SOTA) models. Our work not only incorporates quantum advantages into implicit neural representations but also uncovers a promising application direction for Quantum Neural Networks.

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AAAI Conference 2024 Conference Paper

Quantum-Inspired Neural Network with Runge-Kutta Method

Zipeng Fan
Jing Zhang
Peng Zhang
Qianxi Lin
Hui Gao

In recent years, researchers have developed novel Quantum-Inspired Neural Network (QINN) frameworks for the Natural Language Processing (NLP) tasks, inspired by the theoretical investigations of quantum cognition. However, we have found that the training efficiency of QINNs is significantly lower than that of classical networks. We analyze the unitary transformation modules of existing QINNs based on the time displacement symmetry of quantum mechanics and discover that they are resembling a mathematical form similar to the first-order Euler method. The high truncation error associated with Euler method affects the training efficiency of QINNs. In order to enhance the training efficiency of QINNs, we generalize QINNs' unitary transformation modules to the Quantum-like high-order Runge-Kutta methods (QRKs). Moreover, we present the results of experiments on conversation emotion recognition and text classification tasks to validate the effectiveness of the proposed approach.

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EAAI Journal 2024 Journal Article

Synthetic data augmentation for high-resolution X-ray welding defect detection and classification based on a small number of real samples

Liangliang Li
Peng Wang
Jia Ren
Zhigang Lü
Xiaoyan Li
Hui Gao
RuoHai Di

Deep learning has become the dominant technology in most computer vision tasks. These methods often rely on a large number of labeled sample datasets for training, and in the field of non-destructive testing of welds in industrial manufacturing, weld images with defects are very scarce, and it is still a challenging challenge to construct high-resolution weld defect datasets that meet the requirements. To overcome this limitation, a new data augmentation method for high-resolution X-ray welding defect classification and synthesis based on a small number of real samples is proposed to realize the data augmentation of industrial nondestructive inspection X-ray film defect images. Firstly, to overcome the scarcity of the weld X-ray defect classification dataset, the weld X-ray defect classification dataset (Weld Defect Classification, WDC) is constructed. Secondly, the performance of 16 common deep classification models on WDC datasets is explored. Then, the images of the real local welding defects and the non-defective weld area are fused at random locations, and two data augmentation modes, (Single Image Single Defect, SISD) and (Single Image Multi Defects, SIMD), can generate defect files and annotation files (Visual Object Classes, VOC) at the same time, which can save a lot of time for manual marking. Finally, compared with the traditional data augmentation method, the proposed method can effectively improve the accuracy of defect detection and generalization, the mAP (Mean Average Precision, mAP) @0. 5 of YOLOV8X (You Only Look Once, YOLO) and YOLOV5. 6. 1X is 66. 6% and 72. 8%, which provides an effective solution for data sample generation in the industrial field.

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EAAI Journal 2023 Journal Article

Thermal failure of diamond tools indicated by diamond degradation: Damage evaluation and property prediction on small image datasets

Wucheng Sun
Hui Gao
Yuxiang Chen
Zhiming Wang
Longchen Duan
Songcheng Tan
Xiaohong Fang

High temperature induced diamond degradation often leads to the failure of diamond tools. In this work, diamond samples holding different degrees of thermal damage were prepared by heating and sintering. The influence of diamond particle size and processing temperature was investigated through mechanical testing and micromorphology observation, meanwhile, a dataset containing 2870 SEM images showing diamonds with different degrees of degradation was constructed. By modification of VGG16 network, classification models and regression models were developed for thermal damage evaluation and sample property prediction. Training strategies including transfer learning and data augmentation were implemented and verified essential on the small dataset, where drop-out showed no positive effects. Two classification models (3-class and 65-class) were constructed and trained for damage evaluation. Visualized damage feature maps exported from Grad-CAM revealed the influential mechanism of thermal damage on diamonds, which proved the effectiveness of the classification models as well. Under the optimized training strategies, regression models were built for sample property prediction. The models towards toughness index, bending strength loss, relative density and Rockwell hardness were examined. Comparing the output results with real property values in test sets, the first two models matched well, and the latter two showed the opposite. It verified the validity of the regression models for property prediction as they were all established based on diamond damage image datasets. The loss in bending strength loss prediction model was smaller than that of toughness index, indicating bending strength easier to be shorten than impact toughness for diamond/metal composites suffering thermal impacts.

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AIJ Journal 2022 Journal Article

Diffusion auction design

Bin Li
Dong Hao
Hui Gao
Dengji Zhao

This paper studies an auction design problem for a seller to sell a single commodity in a social network, where each individual (the seller or a buyer) can only communicate with her neighbors. The challenge is to design a mechanism to incentivize the buyers, who are aware of the auction, to further propagate the information to their neighbors, so that more buyers can participate in the auction and hence, the seller will be able to make a higher revenue and a higher welfare. We build a general framework for this new scenario and propose several novel diffusion auctions, which not only incentivize the buyers to report their valuations on the commodity truthfully, but also to propagate the auction information to all their neighbors. Particularly, the direct extension of the well-known Vickrey-Clarke-Groves (VCG) mechanism on social networks can have the incentives, but it will decrease the seller's revenue or even lead to a deficit. We also show that in the social network setting all efficient mechanisms that are individually rational and incentive compatible can lead to a deficit. The goal in this article is to increase the seller's revenue by attracting more buyers, so we give up welfare maximization and propose a class of mechanisms called critical diffusion mechanisms. It is proved that both the seller's revenue and the social welfare achieved in critical diffusion mechanisms are not less than that given in the VCG mechanism before attracting new buyers. The intuition behind the proposed mechanisms is that buyers who join the mechanism earlier have higher priorities to buy the commodity. If a buyer does not win the commodity because of her propagation, then she will be compensated. The formalization of the problem has not been well-studied in the literature of mechanism design, and there are many open problems worth further investigation. The study of this problem will provide insights for the emerging market based on the participants' recommendations via their social networks.

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TCS Journal 2013 Journal Article

Hamiltonian connectivity of restricted hypercube-like networks under the conditional fault model

Qiang Dong
Junlin Zhou
Yan Fu
Hui Gao

Restricted hypercube-like networks (RHLNs) are an important class of interconnection networks for parallel computing systems, which include most popular variants of the hypercubes, such as crossed cubes, Möbius cubes, twisted cubes and locally twisted cubes. This paper deals with the fault-tolerant hamiltonian connectivity of RHLNs under the conditional fault model. Let G be an n -dimensional RHLN and F ⊆ V ( G ) ⋃ E ( G ), where n ≥ 7 and ∣ F ∣ ≤ 2 n − 10. We prove that for any two nodes u, v ∈ V ( G − F ) satisfying a simple necessary condition on neighbors of u and v, there exists a hamiltonian or near-hamiltonian path between u and v in G − F. The result extends further the fault-tolerant graph embedding capability of RHLNs.

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