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International Conference on computer Vision (ICCV)

作者： Zhikang Zou Xiaoqing Ye Liang Du Xianhui Cheng Xiao Tan Li Zhang Jianfeng Feng Xiangyang Xue Errui Ding Baidu Inc. China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University School of Computer Science Fudan University School of Data Science Fudan University

ISBN: (纸本)9781665428132

Low-cost monocular 3D object detection plays a fundamental role in autonomous driving, whereas its accuracy is still far from satisfactory. In this paper, we dig into the 3D object detection task and reformulate it as the sub-tasks of object localization and appearance perception, which benefits to a deep excavation of reciprocal information underlying the entire task. We introduce a Dynamic Feature Reflecting Network, named DFR-Net, which contains two novel standalone modules: (i) the Appearance-Localization Feature Reflecting module (ALFR) that first separates task-specific features and then self-mutually reflects the reciprocal features; (ii) the Dynamic Intra-Trading module (DIT) that adaptively realigns the training processes of various sub-tasks via a self-learning manner. Extensive experiments on the challenging KITTI dataset demonstrate the effectiveness and generalization of DFR-Net. We rank 1 st among all the monocular 3D object detectors in the KITTI test set (till March 16th, 2021). The proposed method is also easy to be plug-and-play in many cutting-edge 3D detection frameworks at negligible cost to boost performance. The code will be made publicly available.

关键词： Training Location awareness Solid modeling Three-dimensional displays Costs Object detection Detectors

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Game Generation via Large Language Models

Game Generation via Large Language Models

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IEEE Symposium on Computational Intelligence and Games, CIG

作者： Chengpeng Hu Yunlong Zhao Jialin Liu Department of Computer Science and Engineering Guangdong Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China LightSpeed Studios Tencent Shenzhen China

ISBN: (数字)9798350350678

ISBN: (纸本)9798350350685

Recently, the emergence of large language models (LLMs) has unlocked new opportunities for procedural content generation. However, recent attempts mainly focus on level generation for specific games with defined game rules such as Super Mario Bros. and Zelda. This paper investigates the game generation via LLMs. Based on video game description language, this paper proposes an LLM-based framework to generate game rules and levels simultaneously. Experiments demonstrate how the framework works with prompts considering different combinations of context. Our findings extend the current applications of LLMs and offer new insights for generating new games in the area of procedural content generation.

关键词： Video games Solid modeling Procedural generation Three-dimensional displays Large language models Prototypes Games

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Large Language Models can be Guided to Evade AI-Generated Text Detection

arXiv

引用

arXiv 2023年

作者： Lu, Ning Liu, Shengcai He, Rui Ong, Yew-Soon Wang, Qi Tang, Ke Guangdong Key Laboratory of Brain-Inspired Intelligent Computation China Department of Computer Science and Engineering Southern University of Science and Technology China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Singapore Singapore

Large language models (LLMs) have shown remarkable performance in various tasks and have been extensively utilized by the public. However, the increasing concerns regarding the misuse of LLMs, such as plagiarism and spamming, have led to the development of multiple detectors, including fine-tuned classifiers and statistical methods. In this study, we equip LLMs with prompts, rather than relying on an external paraphraser, to evaluate the vulnerability of these detectors. We propose a novel Substitution-based In-Context example Optimization method (SICO) to automatically construct prompts for evading the detectors. SICO is cost-efficient as it requires only 40 human-written examples and a limited number of LLM inferences to generate a prompt. Moreover, once a task-specific prompt has been constructed, it can be universally used against a wide range of detectors. Extensive experiments across three real-world tasks demonstrate that SICO significantly outperforms the paraphraser baselines and enables GPT-3.5 to successfully evade six detectors, decreasing their AUC by 0.5 on average. Furthermore, a comprehensive human evaluation show that the SICO-generated text achieves human-level readability and task completion rates, while preserving high imperceptibility. Finally, we propose an ensemble approach to enhance the robustness of detectors against SICO attack. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational linguistics

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EEG-based Emotion Recognition in Immersive Virtual Reality: Meeting the Requirement of Accuracy and Computational Efficiency 23

EEG-based Emotion Recognition in Immersive Virtual Reality: ...

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7th International Conference on Digital Signal Processing, ICDSP 2023

作者： Pei, Guanxiong Fan, Cunhang Li, Taihao Jin, Jia Wang, Rui Research Institute of Artificial Intelligence Zhejiang Lab China School of Computer Science and Technology Anhui University China Laboratory of Applied Brain and Cognitive Sciences Shanghai International Studies University China School of Business and Management Shanghai International Studies University China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences China

ISBN: (纸本)9781450398626

Virtual reality technology provides a strong sense of immersion and interactivity. It is widely used in the fields of anxiety relief, fear therapy, and depression regulation. However, objectively evaluating the emotional intervention effect of virtual reality technology is a difficult problem. The main purpose of this paper is to explore the use of EEG signals to identify individual emotional states in virtual reality scenarios and to improve the computational efficiency and recognition accuracy of emotional valence. To induce the target emotional state of the participants, we established a relatively standard emotion-induced virtual reality video library. The EEG data of the participants were collected synchronously as they watched the virtual reality video. The results show that the emotion recognition performance of multiple features (energy spectrum, differential entropy, differential asymmetry, and rational asymmetry) is better than that of a single feature. The radial basis function neural network (RBFNN) performed better than the deep belief network (DBN). RBFNN achieves the highest average classification accuracy of 91.1%. By combining the feature selection (F-test) method with the RBFNN, an ideal classification performance can be maintained with computational efficiency improvements. Furthermore, it is demonstrated that the features extracted from the theta band outperform features extracted from other bands in emotional valence decoding. These results may contribute to the application of EEG-based affective computing technology in the field of psychological rehabilitation and assessment. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Virtual reality

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Changes in the brain’s Intrinsic Organization in the Resting State with Real-Time fMRI Neurofeedback Training of Posterior Cingulate Cortex Activity

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Journal of Behavioral and brain science 2017年第13期7卷 655-673页

作者： Yubao Wang Jipeng Zhang Gaoyan Zhang Li Yao Zhiying Long State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Beijing Normal University Beijing China College of Information Science and Technology Beijing Normal University Beijing China School of Computer Science and Technology Tianjin Key Laboratory of Cognitive Computing and Application Tianjin University Tianjin China

Real-time functional magnetic resonance imaging (rtfMRI) technology has been widely used to train subjects to actively regulate the activity of specific brain regions. Although many previous studies have demonstrated that neurofeedback training alters the functional connectivity between brain regions in the task state and resting state, it is unclear how the regulation of the key hub of the default mode network (DMN) affects the topological properties of the resting-state brain network. The current study aimed to investigate what topological changes would occur in the large-scale intrinsic organization of the resting state after the real-time down-regulation of the posterior cingulate cortex (PCC). The results indicated that the down-regulation of the PCC in the DMN reduced the functional connectivity of the PCC with the nodes outside of the DMN and reduced functional connectivity between the superior medial frontal gyrus (SFGmed) and parahippocampal gyrus (PHG) in the experimental group. Moreover, the nodal graph properties of the SFGmed were reduced, while that of the PHG showed the opposite alteration after the down-regulation of the PCC. These findings possibly suggest that the regulation of the key hub of the DMN, the PCC, mainly changed the information transfer of the SFGmed and PHG.

关键词： Real-Time fMRI Neurofeedback Default Mode Network Posterior Cingulate Cortex Graph Theory

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EAS-SNN: End-to-End Adaptive Sampling and Representation for Event-based Detection with Recurrent Spiking Neural Networks

arXiv

引用

arXiv 2024年

作者： Wang, Ziming Wang, Ziling Li, Huaning Qin, Lang Jiang, Runhao Ma, De Tang, Huajin College of Computer Science and Technology Zhejiang University Hangzhou310027 China State Key Laboratory of Brain-Machine Intelligence Zhejiang University Hangzhou310027 China

Event cameras, with their high dynamic range and temporal resolution, are ideally suited for object detection in scenarios with motion blur and challenging lighting conditions. However, while most existing approaches prioritize optimizing spatiotemporal representations with advanced detection backbones and early aggregation functions, the crucial issue of adaptive event sampling remains largely unaddressed. Spiking Neural Networks (SNNs), operating on an event-driven paradigm, align closely with the behavior of an ideal temporal event sampler. Motivated by this, we propose a novel adaptive sampling module that leverages recurrent convolutional SNNs enhanced with temporal memory, facilitating a fully end-to-end learnable framework for event-based detection. Additionally, we introduce Residual Potential Dropout (RPD) and Spike-Aware Training (SAT) to regulate potential distribution and address performance degradation encountered in spike-based sampling modules. Empirical evaluation on neuromorphic detection datasets demonstrates that our approach outperforms existing state-of-the-art spike-based methods with significantly fewer parameters and time steps. For instance, our method yields a 4.4% mAP improvement on the Gen1 dataset, while requiring 38% fewer parameters and only three time steps. Moreover, the applicability and effectiveness of our adaptive sampling methodology extend beyond SNNs, as demonstrated through further validation on conventional non-spiking models. Code is available at Github. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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MyRoom: A Unity Plugin for Procedural and Interactive Indoor Scene Synthesis

MyRoom: A Unity Plugin for Procedural and Interactive Indoor...

引用

IEEE Symposium on Computational Intelligence and Games, CIG

作者： Haocheng Du Yunlong Zhao Shuo Huang Jiayu Bai Song Tian Jialin Liu Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Center for the Humanities Southern University of Science and Technology Shenzhen China

The demand for indoor synthesis has increased significantly in recent years because of the emergence of computational design. This work designs and develops MyRoom, a Unity plugin that can import layout datasets for indoor synthesis and procedurally generate and interactively design indoor scenes. MyRoom enables users to easily edit and visualize non-intuitive layout description data, making it easier to generate and manipulate indoor scenes in Unity. MyRoom provides a user-friendly interface and powerful tools for designing and optimizing indoor layouts, including an automatic layout generator, making it ideal for game developers, interior designers, and researchers in the digital world-building industry. With MyRoom, users can streamline the process of creating high-quality indoor scenes in games and achieve their design goals efficiently.

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A Surrogate-Assisted Evolutionary Framework for Expensive Multitask Optimization Problems

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

作者： Tan, Shenglian Wang, Yong Sun, Guangyong Pang, Tong Tang, Ke School of Automation Central South University Changsha China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan University Changsha China Department of Computer Science and Engineering Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

This paper proposes a surrogate-assisted evolutionary framework (called SELF) to solve expensive multitask optimization problems (ExMTOPs). SELF consists of two main phases: global knowledge transfer phase and local knowledge transfer phase. In the former, a multitask Gaussian process model (MTGP) is established by fusing previously evaluated solutions of multiple optimization tasks. MTGP can capture task-relevant information and the knowledge of landscapes. Then, differential evolution assisted with MTGP is proposed to preselect high-quality candidates. During the preselection, the knowledge of landscapes is transferred among multiple optimization tasks for locating promising regions quickly. In the latter, for each optimization task, Bayesian optimization is adopted to improve the quality of the best individual in the population. Moreover, the improved best individuals in the populations of multiple optimization tasks are adaptively transferred based on a transfer probability, which is computed through the task-relevant information provided by MTGP. By combining these two phases, SELF not only achieves the tradeoff between exploration and exploitation, but also utilizes the global and local knowledge transfer to improve the efficiency for solving ExMTOPs. We test SELF on seven benchmark test problems in the IEEE CEC2017 evolutionary multitask optimization competition. The results demonstrate that the performance of SELF is better than that of other seven advanced methods. In addition, we also apply SELF to deal with two real-world ExMTOPs. The designs provided by SELF exhibit the best performance among all the compared methods, verifying the potential of SELF in practical engineering applications. IEEE

关键词： Linear programming

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A Multi-objective Perspective Towards Improving Meta-Generalization

A Multi-objective Perspective Towards Improving Meta-General...

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International Joint Conference on Neural Networks (IJCNN)

作者： Weiduo Liao Ying Wei Qirui Sun Qingfu Zhang Hisao Ishibuchi Department of Computer Science City University of Hong Kong Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology School of Computer Science and Engineering Nanyang Technological University

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

To improve meta-generalization, i.e., accommodating out-of-domain meta-testing tasks beyond meta-training ones, is of significance to extending the success of meta-learning beyond standard benchmarks. Previous heterogeneous meta-learning algorithms have shown that tailoring the global meta-knowledge by the learned clusters during meta-training promotes better meta-generalization to novel meta-testing tasks. Inspired by this, we propose a novel multi-objective perspective to sharpen the compositionality of the meta-trained clusters, through which we have empirically validated that the meta-generalization further improves. Grounded on the hierarchically structured meta-learning framework, we formulate a hypervolume loss to evaluate the degree of conflict between multiple cluster-conditioned parameters in the two-dimensional loss space over two randomly chosen tasks belonging to two clusters and two mixed tasks imitating out-of-domain tasks. Experimental results on more than 16 few-shot image classification datasets show not only improved performance on out-of-domain meta-testing datasets but also better clusters in visualization.

关键词： Metalearning Visualization Neural networks Clustering algorithms Benchmark testing Standards Image classification

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Temporal Dynamic Synchronous Functional brain Network for Schizophrenia Diagnosis and Lateralization Analysis

arXiv

引用

arXiv 2023年

作者： Zhu, Cheng Tan, Ying Yang, Shuqi Miao, Jiaqing Zhu, Jiayi Huang, Huan Yao, Dezhong Luo, Cheng The Key Laboratory for Computer Systems of State Ethnic Affairs Commission Southwest Minzu University Chengdu China MOE Key Lab for Neuroinformation High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province The Clinical Hospital of Chengdu Brain Science Institute University of Electronic Science and Technology of China Chengdu611731 China

The available evidence suggests that dynamic functional connectivity (dFC) can capture time-varying abnormalities in brain activity in resting-state cerebral functional magnetic resonance imaging (rs-fMRI) data and has a natural advantage in uncovering mechanisms of abnormal brain activity in schizophrenia(SZ) patients. Hence, an advanced dynamic brain network analysis model called the temporal brain category graph convolutional network (Temporal-BCGCN) was employed. Firstly, a unique dynamic brain network analysis module, DSF-brainNet, was designed to construct dynamic synchronization features. Subsequently, a revolutionary graph convolution method, TemporalConv, was proposed, based on the synchronous temporal properties of feature. Finally, the first modular abnormal hemispherical lateralization test tool in deep learning based on rs-fMRI data, named CategoryPool, was proposed. This study was validated on COBRE and UCLA datasets and achieved 83.62% and 89.71% average accuracies, respectively, outperforming the baseline model and other state-of-the-art methods. The ablation results also demonstrate the advantages of TemporalConv over the traditional edge feature graph convolution approach and the improvement of CategoryPool over the classical graph pooling approach. Interestingly, this study showed that the lower order perceptual system and higher order network regions in the left hemisphere are more severely dysfunctional than in the right hemisphere in SZ and reaffirms the importance of the left medial superior frontal gyrus in SZ. Our core code is available at: https://***/swfen/Temporal-BCGCN. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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