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Ping Liu

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13 papers

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TMLR Journal 2026 Journal Article

ABCDE: Agentic-Based Controlled Dynamic Erasure for Intent-Aware Safety Reasoning

Ping Liu
Chi Zhang

Concept erasure has emerged as a central mechanism for safety alignment in text-conditioned generative models, yet most existing approaches implicitly adopt an unconditional suppression paradigm in which target concepts are removed whenever they appear, regardless of contextual intent. This formulation conflates benign and harmful concept usage, leading to systematic over-suppression that unnecessarily censors policy-compliant content and degrades model utility. We argue that safety intervention should instead be framed as a decision problem grounded in contextual language understanding, rather than as a purely mechanistic removal operation. Based on this perspective, we introduce Intent-Aware Concept Erasure (ICE), a decision-centric formulation that explicitly separates the question of whether a concept should be suppressed from how suppression is realized, enabling context-sensitive intervention policies that preserve benign usage while maintaining safety guarantees. To operationalize this formulation, we present Agentic-Based Controlled Dynamic Erasure (ABCDE), an agentic framework that infers a stable intervention decision from semantic context and realizes it through minimal prompt-level intervention with closed-loop multimodal output feedback, without modifying model parameters. To enable principled evaluation of intent-aware intervention, we further construct the Context-Aware Erasure Benchmark (CAEB), a paired benchmark comprising 500 prompts over 10 object concepts and 100 prompts over 5 artist styles, in which the same concept appears in both removal-required and preservation-required contexts. Experiments on CAEB show that ABCDE achieves substantially higher precision than unconditional baselines while maintaining strong recall, demonstrating effective avoidance of unnecessary suppression in benign contexts.

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EAAI Journal 2025 Journal Article

Ceramic tableware surface defect detection based on deep learning

Pu Sun
Changchun Hua
Weili Ding
Changsheng Hua
Ping Liu
Ziqi Lei

Detecting defects in ceramic tableware is a critical step in ensuring product quality, food safety, durability, and aesthetics, and it is important to both manufacturers and consumers. However, manual labour is still the main way to detect defects in ceramic tableware. This is because there are many difficulties in detecting defects in ceramic tableware: multi-scale, small-sized defects, and high demands on the accuracy and inference speed of the detection system. To address the challenges, we developed a detector based on You Only Look Once version 8 (YOLOv8) to achieve accurate and efficient detection of surface defects on ceramic tableware. First, a dataset containing five types of defects was produced, named the Ceramic Tableware Defect Dataset (CE5-DET). This was achieved by the image acquisition system we built. Second, a new efficient and lightweight convolution using a non-strided convolution and space-to-depth layer has been developed to solve the problems of insufficient feature extraction and loss of fine-grained information in traditional convolution. Finally, experiments were conducted on our CE5-DET and a public dataset (NEU-DET). Our model achieved a mean average precision (mAP) of 68. 3% on CE5-DET and 78. 7% on NEU-DET, which are 4. 7% and 2. 2% higher than that of baseline and significantly higher than the state-of-the-art (SOTA) detection methods. The experimental results demonstrated that the model exhibited a balance between detection accuracy and inference speed, and is expected to achieve automated detection of surface defects on ceramic tableware.

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JBHI Journal 2025 Journal Article

Graph Attention Fusion With Kolmogorov-Arnold Network for Drug-Gene Interaction Prediction

Xinguo Lu
Zihao Li
Ping Liu
Anqi Tang
Xing Liu
Hongrui Liu

Deep learning-based computational methods have emerged as powerful tools for predicting novel drug-gene interactions. It is essential to parse the joint influence of diverse attention focuses in large complex datasets in the model's decision-making process. Here, we propose graph attention fusion with Kolmogorov-Arnold network (KAN) for drug-gene interaction prediction (dgKAN). This approach parses the mutual influence of heterogeneous attention in drug-gene relationships by constructing an interpretable KAN network. Specifically, we use dynamic neighbor selection module by dynamic attention sampling to construct subgraphs and generate embedding representations for drugs and genes within these subgraphs. Then, we utilize a module consisted of Transformer and GNN architectures (TransGNN) to fuse the mechanism of global attention and local attention. Finally, we develop an interpretable KAN network with spline functions to model and analyze the cross-domain information flow between drugs and genes, enabling the prediction of drug-gene interactions. We conducted comprehensive experiments on various datasets, and the results demonstrate that dgKAN outperforms other baseline methods. Meanwhile, results illustrate that dgKAN captures the implicit characteristics by parsing heterogeneous attention in drug-gene relationships. The predicted drug-gene interactions have the potential to significantly aid in drug development for disease treatment.

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IJCAI Conference 2025 Conference Paper

Grounding Creativity in Physics: A Brief Survey of Physical Priors in AIGC

Siwei Meng
Yawei Luo
Ping Liu

Recent advancements in AI-generated content have significantly improved the realism of 3D and 4D generation. However, most existing methods prioritize appearance consistency while neglecting underlying physical principles, leading to artifacts such as unrealistic deformations, unstable dynamics, and implausible objects interactions. Incorporating physics priors into generative models has become a crucial research direction to enhance structural integrity and motion realism. This survey provides a review of physics-aware generative methods, systematically analyzing how physical constraints are integrated into 3D and 4D generation. First, we examine recent works in incorporating physical priors into static and dynamic 3D generation, categorizing methods based on representation types, including vision-based, NeRF-based, and Gaussian Splatting-based approaches. Second, we explore emerging techniques in 4D generation, focusing on methods that model temporal dynamics with physical simulations. Finally, we conduct a comparative analysis of major methods, highlighting their strengths, limitations, and suitability for different materials and motion dynamics. By presenting an in-depth analysis of physics-grounded AIGC, this survey aims to bridge the gap between generative models and physical realism, providing insights that inspire future research in physically consistent content generation.

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EAAI Journal 2025 Journal Article

Picking point localization method of table grape picking robot based on you only look once version 8 nano

Yanjun Zhu
Shunshun Sui
Wensheng Du
Xiang Li
Ping Liu

Automatic localization of the picking point for table grape is the key to achieving intelligent harvesting. Aiming at the problem, the You Only Look Once version 8 nano-Deformable Convolutional Networks-Wise Intersection over Union-Fourth Detection Layer (YOLO v8n-DWF) network was designed for the table grape picking robot to realize the detection of table grapes and the localization of picking points. The Deformable Convolutional Networks (DCN) model was used to enhance the robustness of table grape detection and improve the detection precision of grape stems. To improve the detection precision of table grape and stem and reduce the impact of low-quality data on model generalization ability, Wise-Intersection over Union version 3 (WIou v3) was applied. In addition, aiming at the problem of difficultly identifying due to small target and low pixel of table grape stem, a fourth detection layer for small target detection was added to improve the recognition and detection ability of the network model for table grape stem. Further, a more accurate geometric localization method of picking points was proposed to achieve fast picking of table grapes. Finally, the results showed that the detection precision, recall, mean Average Precision50 (mAP 50 ) and mean Average Precision50-95 (mAP 50-95 ) of the YOLO v8n-DWF model was 97. 9%, 95. 3%, 97. 6% and 85. 4%, respectively. In addition, the success rate of the geometric method based on the results of identification for YOLO v8n-DWF was 88. 24%, and the average picking success rate of table grapes in field experiments was 87. 40%. It can fully meet the requirements of intelligent picking of table grapes.

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AAAI Conference 2024 Conference Paper

Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Domain Learning

Chuangchuang Tan
Yao Zhao
Shikui Wei
Guanghua Gu
Ping Liu
Yunchao Wei

This research addresses the challenge of developing a universal deepfake detector that can effectively identify unseen deepfake images despite limited training data. Existing frequency-based paradigms have relied on frequency-level artifacts introduced during the up-sampling in GAN pipelines to detect forgeries. However, the rapid advancements in synthesis technology have led to specific artifacts for each generation model. Consequently, these detectors have exhibited a lack of proficiency in learning the frequency domain and tend to overfit to the artifacts present in the training data, leading to suboptimal performance on unseen sources. To address this issue, we introduce a novel frequency-aware approach called FreqNet, centered around frequency domain learning, specifically designed to enhance the generalizability of deepfake detectors. Our method forces the detector to continuously focus on high-frequency information, exploiting high-frequency representation of features across spatial and channel dimensions. Additionally, we incorporate a straightforward frequency domain learning module to learn source-agnostic features. It involves convolutional layers applied to both the phase spectrum and amplitude spectrum between the Fast Fourier Transform (FFT) and Inverse Fast Fourier Transform (iFFT). Extensive experimentation involving 17 GANs demonstrates the effectiveness of our proposed method, showcasing state-of-the-art performance (+9.8\%) while requiring fewer parameters. The code is available at https://github.com/chuangchuangtan/FreqNet-DeepfakeDetection.

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JBHI Journal 2024 Journal Article

Survival Analysis for Multimode Ablation Using Self-Adapted Deep Learning Network Based on Multisource Features

Ziqi Zhao
Wentao Li
Ping Liu
Aili Zhang
Jianqi Sun
Lisa X. Xu

Novel multimode thermal therapy by freezing before radio-frequency heating has achieved a desirable therapeutic effect in liver cancer. Compared with surgical resection, ablation treatment has a relatively high risk of tumor recurrence. To monitor tumor progression after ablation, we developed a novel survival analysis framework for survival prediction and efficacy assessment. We extracted preoperative and postoperative MRI radiomics features and vision transformer-based deep learning features. We also combined the immune features extracted from peripheral blood immune responses using flow cytometry and routine blood tests before and after treatment. We selected features using random survival forest and improved the deep Cox mixture (DCM) for survival analysis. To properly accommodate multitype input features, we proposed a self-adapted fully connected layer for locally and globally representing features. We evaluated the method using our clinical dataset. Of note, the immune features rank the highest feature importance and contribute significantly to the prediction accuracy. The results showed a promising C $^{\mathit{td}}$ -index of 0. 885 $\pm$ 0. 040 and an integrated Brier score of 0. 041 $\pm$ 0. 014, which outperformed state-of-the-art method combinations of survival prediction. For each patient, individual survival probability was accurately predicted over time, which provided clinicians with trustable prognosis suggestions.

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TMLR Journal 2023 Journal Article

The Analysis of the Expected Change in the Classification Probability of the Predicted Label

Ruo Yang
Ping Liu
Mustafa Bilgic

We present a formalism for estimating the expected change in the probability distribution of the predicted label of an object, with respect to all small perturbations to the object. We first derive analytically an estimate of the expected probability change as a function of the input noise. We then conduct three empirical studies: in the first study, experimental results on image classification show that the proposed measure can be used to distinguish the not-robust label predictions from those that are robust, even when they are all predicted with high confidence. The second study shows that the proposed robustness measure is almost always higher for the predictions on the corrupted images, compared to the predictions on the original versions of them. The final study shows that the proposed measure is lower for models when they are trained using adversarial training approaches.

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AAAI Conference 2021 Conference Paper

Relational Classification of Biological Cells in Microscopy Images

Ping Liu
Mustafa Bilgic

We investigate the relational classification of biological cells in 2D microscopy images. Rather than treating each cell image independently, we investigate whether and how the neighborhood information of a cell can be informative for its prediction. We propose a Relational Long Short-Term Memory (R-LSTM) algorithm, coupled with auto-encoders and convolutional neural networks, that can learn from both annotated and unlabeled microscopy images and that can utilize both the local and neighborhood information to perform an improved classification of biological cells. Experimental results on both synthetic and real datasets show that R-LSTM performs comparable to or better than six baselines.

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AAAI Conference 2020 Conference Paper

Adversarial Localized Energy Network for Structured Prediction

Pingbo Pan
Ping Liu
Yan Yan
Tianbao Yang
Yi Yang

This paper focuses on energy model based structured output prediction. Though inheriting the beneﬁts from energybased models to handle the sophisticated cases, previous deep energy-based methods suffered from the substantial computation cost introduced by the enormous amounts of gradient steps in the inference process. To boost the efﬁciency and accuracy of the energy-based models on structured output prediction, we propose a novel method analogous to the adversarial learning framework. Speciﬁcally, in our proposed framework, the generator consists of an inference network while the discriminator is comprised of an energy network. The two sub-modules, i. e. , the inference network and the energy network, can beneﬁt each other mutually during the whole computation process. On the one hand, our modiﬁed inference network can boost the efﬁciency by predicting good initializations and reducing the searching space for the inference process; On the other hand, inheriting the beneﬁts of the energy network, the energy module in our network can evaluate the quality of the generated output from the inference network and correspondingly provides a resourceful guide to the training of the inference network. In the ideal case, the adversarial learning strategy makes sure the two sub-modules can achieve an equilibrium state after steps. We conduct extensive experiments to verify the effectiveness and efﬁciency of our proposed method.

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NeurIPS Conference 2020 Conference Paper

Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation

Yawei Luo
Ping Liu
Tao Guan
Junqing Yu
Yi Yang

We aim at the problem named One-Shot Unsupervised Domain Adaptation. Unlike traditional Unsupervised Domain Adaptation, it assumes that only one unlabeled target sample can be available when learning to adapt. This setting is realistic but more challenging, in which conventional adaptation approaches are prone to failure due to the scarce of unlabeled target data. To this end, we propose a novel Adversarial Style Mining approach, which combines the style transfer module and task-specific module into an adversarial manner. Specifically, the style transfer module iteratively searches for harder stylized images around the one-shot target sample according to the current learning state, leading the task model to explore the potential styles that are difficult to solve in the almost unseen target domain, thus boosting the adaptation performance in a data-scarce scenario. The adversarial learning framework makes the style transfer module and task-specific module benefit each other during the competition. Extensive experiments on both cross-domain classification and segmentation benchmarks verify that ASM achieves state-of-the-art adaptation performance under the challenging one-shot setting.

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TIST Journal 2020 Journal Article

FROST

Meng Wang
Hui Li
Jiangtao Cui
Sourav S. Bhowmick
Ping Liu

The facility relocation (FR) problem, which aims to optimize the placement of facilities to accommodate the changes of users’ locations, has a broad spectrum of applications. Despite the significant progress made by existing solutions to the FR problem, they all assume each user is stationary and represented as a single point. Unfortunately, in reality, objects (e.g., people, animals) are mobile. For example, a car-sharing user picks up a vehicle from a station close to where he or she is currently located. Consequently, these efforts may fail to identify a superior solution to the FR problem. In this article, for the first time, we take into account the movement history of users and introduce a novel FR problem, called motion-fr, to address the preceding limitation. Specifically, we present a framework called frost to address it. frost comprises two exact algorithms: index based and index free. The former is designed to address the scenario when facilities and objects are known a priori, whereas the latter solves the motion-fr problem by jettisoning this assumption. Further, we extend the index-based algorithm to solve the general k - motion-fr problem, which aims to relocate k inferior facilities. We devise an approximate solution due to NP-hardness of the problem. Experimental study over both real-world and synthetic datasets demonstrates the superiority of our framework in comparison to state-of-the-art FR techniques in efficiency and effectiveness.

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EAAI Journal 2020 Journal Article

Unsupervised feature selection via graph matrix learning and the low-dimensional space learning for classification

XiaoHong Han
Ping Liu
Li Wang
Dengao Li

Unsupervised feature selection is a powerful tool to select a subset of features for effective representation of high-dimensional data. In this paper, we proposes a novel unsupervised feature selection method via the graph matrix learning and the low-dimensional space learning to obtain their individually optimized result. Furthermore, the global and local correlation of features have been taken into consideration through the low-rank constraint and the feature-level representation property on the graph matrix. Experimental analysis on 15 benchmark datasets verified that our proposed method outperformed the state-of-the-art feature selection methods in terms of classification performance.

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