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SGformer:Boosting transformers for indoor lighting estimation from a single image

Computational Visual Media 2024年第4期10卷 671-686页

作者： Junhong Zhao Bing Xue Mengjie Zhang Centre for Data Science and Artificial Intelligence&School of Engineering and Computer Science Victoria University of WellingtonWellington 6012New Zealand

Estimating lighting from standard images can effectively circumvent the need for resourceintensive high-dynamic-range(HDR)lighting ***,this task is often ill-posed and challenging,particularly for indoor scenes,due to the intricacy and ambiguity inherent in various indoor illumination *** propose an innovative transformer-based method called SGformer for lighting estimation through modeling spherical Gaussian(SG)distributions—a compact yet expressive lighting *** from previous approaches,we explore underlying local and global dependencies in lighting features,which are crucial for reliable lighting ***,we investigate the structural relationships spanning various resolutions of SG distributions,ranging from sparse to dense,aiming to enhance structural consistency and curtail potential stochastic noise stemming from independent SG component *** harnessing the synergy of local–global lighting representation learning and incorporating consistency constraints from various SG resolutions,the proposed method yields more accurate lighting estimates,allowing for more realistic lighting effects in object relighting and *** code and model implementing our work can be found at https://***/junhong-jennifer-zhao/SGformer.

关键词： lighting estimation transformer spherical Gaussian(SG) augmented reality

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Multiobjective Dynamic Flexible Job Shop Scheduling With Biased Objectives via Multitask Genetic Programming

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2025年第1期6卷 169-183页

作者： Zhang, Fangfang Shi, Gaofeng Mei, Yi Zhang, Mengjie Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Emerson New Plymouth4310 New Zealand

Dynamic flexible job shop scheduling is an important combinatorial optimization problem that has rich real-world applications such as product processing in manufacturing. Genetic programming has been successfully used to learn scheduling heuristics for dynamic flexible job shop scheduling. Intuitively, users prefer small and effective scheduling heuristics that can not only generate promising schedules but also are computationally efficient and easy to be understood. However, a scheduling heuristic with better effectiveness tends to have a larger size, and the effectiveness of rules and rule size are potentially conflicting objectives. With the traditional dominance relation-based multiobjective algorithms, there is a search bias toward rule size, since rule size is much easier to optimized than effectiveness, and larger rules are easily abandoned, resulting in the loss of effectiveness. To address this issue, this article develops a novel multiobjective genetic programming algorithm that takes size and effectiveness of scheduling heuristics for optimization via multitask learning mechanism. Specifically, we construct two tasks for the multiobjective optimization with biased objectives using different search mechanisms for each task. The focus of the proposed algorithm is to improve the effectiveness of learned small rules by knowledge sharing between constructed tasks which is implemented with the crossover operator. The results show that our proposed algorithm performs significantly better, i.e., with smaller and more effective scheduling heuristics, than the state-of-the-art algorithms in the examined scenarios. By analyzing the population diversity, we find that the proposed algorithm has a good balance between exploration and exploitation during the evolutionary process. © 2024 IEEE.

关键词： Genetic programming

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A survey on cross-user federated recommendation

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science China(Information sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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Importance-Based Pruning for Genetic Programming Based Symbolic Regression 37th

Importance-Based Pruning for Genetic Programming Based Symb...

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37th Australasian Joint Conference on artificial intelligence, AJCAI 2024

作者： Rimas, Mohamad Chen, Qi Zhang, Mengjie Centre for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

ISBN: (纸本)9789819603503

Pruning is a major research field in neural networks, enhancing their efficiency and generalization. The field of pruning approaches in genetic programming (GP) is continually evolving, with researchers actively exploring new techniques and approaches to optimise the performance of GP models. This research introduces a novel pruning algorithm for Genetic Programming-based Symbolic Regression (GPSR). The proposed method employs a weighting mechanism to identify and filter out unimportant subtrees in each generation. To achieve this, the method arranges all subtrees linearly and assigns weights to each subtree and terminal. It then uses Ordinary Least Squares (OLS) to optimize these weights, enabling the identification of unimportant subtrees and terminals for effective pruning. The algorithm’s effectiveness was evaluated using ten regression datasets, including high-dimensional and complex feature sets. Furthermore, comparisons were made with two other algorithms to evaluate its performance. The results indicate that the proposed approach not only achieves better learning and generalisation performance but also generates smaller trees compared to standard GP, thereby improving interpretability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic Programming Ordinary Least Squares Pruning Regression Subtrees

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Evolutionary Trainer-Based Deep Q-Network for Dynamic Flexible Job Shop Scheduling

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IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Liu, Yun Zhang, Fangfang Sun, Yanan Zhang, Mengjie College of Computer Science Sichuan University Chengdu PR China Center for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

Dynamic flexible job shop scheduling (DFJSS) aims to achieve the optimal efficiency for production planning in the face of dynamic events. In practice, deep Q-network (DQN) algorithms have been intensively studied for solving various DFJSS problems. However, these algorithms often cause moving targets for the given job-shop state. This will inevitably lead to unstable training and severe deterioration of the performance. In this paper, we propose a training algorithm based on genetic algorithm to efficiently and effectively address this critical issue. Specifically, a state feature extraction method is first developed, which can effectively represent different job shop scenarios. Furthermore, a genetic encoding strategy is designed, which can reduce the encoding length to enhance search ability. In addition, an evaluation strategy is proposed to calculate a fixed target for each job-shop state, which can avoid the parameter update of target networks. With the designs, the DQNs could be stably trained, thus their performance is greatly improved. Extensive experiments demonstrate that the proposed algorithm outperforms the state-of-the-art peer competitors in terms of both effectiveness and generalizability to multiple scheduling scenarios with different scales. In addition, the ablation study also reveals that the proposed algorithm can outperform the DQN algorithms with different updating frequencies of target networks. IEEE

关键词： Job shop scheduling

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Constrained Multiobjective Optimization via Relaxations on Both Constraints and Objectives

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2024年第12期5卷 6709-6722页

作者： Ming, Fei Xue, Bing Zhang, Mengjie Gong, Wenyin Zhen, Huixiang China University of Geosciences School of Computer Science Wuhan430074 China Victoria University of Wellington Center of Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand

Since most multiobjective optimization problems in real-world applications contain constraints, constraint-handling techniques (CHTs) are necessary for a multiobjective optimizer. However, existing CHTs give no relaxation to objectives, resulting in the elimination of infeasible dominated solutions that are promising (potentially useful but inferior) for detecting feasible regions and the constrained Pareto front (CPF). To overcome this drawback, in this work, we propose an objective relaxation technique that can preserve promising by relaxing objective function values, i.e., convergence, through an adaptively adjusted relaxation factor. Further, we develop a new constrained multiobjective optimization evolutionary algorithm (CMOEA) based on relaxations on both constraints and objectives. The proposed algorithm evolves one population by the constraint relaxation technique to preserve promising infeasible solutions and the other population by both objective and constraint relaxation techniques to preserve promising infeasible dominated solutions. In this way, our method can overcome the drawback of existing CHTs. Besides, an archive update strategy is designed to maintain encountered feasible solutions by the two populations to approximate the CPF. Experiments on challenging benchmark problems and real-world problems have demonstrated the superiority or at least competitiveness of our proposed CMOEA. Moreover, to verify the generality of the objective relaxation technique, we embed it into two existing CMOEA frameworks and the results show that it can significantly improve the performance in handling challenging problems. © 2020 IEEE.

关键词： Constrained optimization

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Generating (Semi-)active Schedules for Dynamic Multi-mode Project Scheduling Using Genetic Programming Hyper-heuristics 25th

Generating (Semi-)active Schedules for Dynamic Multi-mode P...

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25th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2025 Held as Part of EvoStar 2025

作者： Tian, Yuan Mei, Yi Zhang, Mengjie Centre for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington Wellington6012 New Zealand

ISBN: (纸本)9783031868481

Dynamic multi-mode resource-constrained project scheduling problem (DMRCPSP) is crucial for effectively managing complex projects where activities have multiple options of resource demand and durations are uncertain. Efficient solutions to this problem are vital for industrial applications, where optimal scheduling can significantly impact project costs and timelines. Genetic programming hyper-heuristic (GPHH) has been applied to evolve effective scheduling heuristics to make real-time decisions. However, current decision-making procedures for solving DMRCPSP primarily generate non-delay schedules, which are often suboptimal. This paper proposes two new decision-making procedures to generate (semi-)active schedules. The core idea is to expand the decision set and allow activities that cannot immediately start to reserve resources for future execution. We further developed GPHH to evolve scheduling heuristics based on these procedures and a comparison was conducted with existing decision-making procedures. The results demonstrated that the proposed GP approaches managed to evolve rules that generate more effective (semi-)active schedules than the best-known non-delayed schedules in real-time. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Genetic programming

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A New Concordance Correlation Coefficient based Fitness Function for Genetic Programming for Symbolic Regression 13

A New Concordance Correlation Coefficient based Fitness Func...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Xu, Jizhong Chen, Qi Xue, Bing Zhang, Mengjie Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

ISBN: (纸本)9798350308365

Coefficients learning has long been challenging in genetic programming based symbolic regression (GPSR). Recent GPSR methods employ Pearson correlation coefficient for fitness assessment with post-hoc linear scaling for coefficient learning. However, this approach often leads to sub-optimal coefficient learning and inadequate consideration of nonlinear relationships between input variables and outputs. To solve those issues, this study introduces an innovative approach to integrating the Concordance Correlation Coefficient (CCC) into GPSR. Unlike Pearson correlation, CCC can effectively assess both linear and non-linear agreements between two sets of variables. Experimental results on eight regression datasets highlight the potential of CCC as a promising fitness function for GPSR without the need of a more advanced coefficient optimisation method for linear scaling. © 2024 IEEE.

关键词： Contrastive Learning

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A Cooperative Coevolution Neural Architecture Search Approach for Evolving Convolutional Neural Networks

A Cooperative Coevolution Neural Architecture Search Approac...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Londt, Trevor Gao, Xiaoying Mei, Yi Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

ISBN: (纸本)9798400704956

Evolutionary Neural Architecture Search (ENAS) automates convolutional neural network (CNNs) construction. Cooperative Coevolution (COCE) divides an optimisation problem into sub-problems. Our COCE ENAS algorithm decomposes a CNN into sub-architectures, evolving each sub-architecture separately but in cooperation. Tested on Fashion-MNIST, CIFAR-10, AG's News, and Yelp Reviews Full datasets, our proposed algorithm surpasses several state-of-the-art NAS methods in performance. Our algorithm is the first COCE-based ENAS algorithm that focuses on cooperatively evolving the four CNN sub-architectures and is effective in both image and text classification tasks. © 2024 held by the owner/author(s).

关键词： Convolutional neural networks

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Fake Face Detection Based on Fusion of Spatial Texture and High-Frequency Noise

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Chinese Journal of Electronics 2025年第1期34卷 212-221页

作者： Dengyong Zhang Feifan Qi Jiahao Chen Jiaxin Chen Rongrong Gong Yuehong Tian Lebing Zhang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication Engineering Changsha University of Science and Technology School of Computer and Communication Engineering Changsha University of Science and Technology Changsha Social Work College Changkuangao Beijing Technology Co. Ltd. School of Computer and Artificial Intelligence Huaihua University

The rapid development of the Internet has led to the widespread dissemination of manipulated facial images, significantly impacting people's daily lives. With the continuous advancement of Deepfake technology, the generated counterfeit facial images have become increasingly challenging to distinguish. There is an urgent need for a more robust and convincing detection method. Current detection methods mainly operate in the spatial domain and transform the spatial domain into other domains for analysis. With the emergence of transformers, some researchers have also combined traditional convolutional networks with transformers for detection. This paper explores the artifacts left by Deepfakes in various domains and, based on this exploration, proposes a detection method that utilizes the steganalysis rich model to extract high-frequency noise to complement spatial features. We have designed two main modules to fully leverage the interaction between these two aspects based on traditional convolutional neural networks. The first is the multi-scale mixed feature attention module, which introduces artifacts from high-frequency noise into spatial textures, thereby enhancing the model's learning of spatial texture features. The second is the multi-scale channel attention module, which reduces the impact of background noise by weighting the features. Our proposed method was experimentally evaluated on mainstream datasets, and a significant amount of experimental results demonstrate the effectiveness of our approach in detecting Deepfake forged faces, outperforming the majority of existing methods.

关键词： Deepfakes Adaptation models Attention mechanisms Noise Transforms Transformers Feature extraction Internet Convolutional neural networks Faces

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