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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Dong, Junhao Xie, Xiaohua School of Computer Science and Engineering Sun Yat-sen University China Guangdong Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9781665438643

As a deep learning-based application, DeepFake can generate malicious images or videos through replacing the face of a source image with the target face, which poses a significant threat to social media. In this paper, we propose a scheme to prevent such tampering by exploring adversarial examples against DeepFake. Specifically, adversarial examples are produced by adding tailored distortion to source images. The added distortion is imperceptible to human vision but can mislead the generation of face-swapped images effectively. We present three novel adversarial attacks against DeepFake autoencoders from perspectives of adversarial transferability and latent representation. Our first method synthesizes universal perturbation, which is image-agnostic. By contrast, the latter two methods directly perform the preciser perturbation specific to a source image. Extensive experiments demonstrate the effectiveness of our adversarial examples against DeepFake in terms of both reference and non-reference image quality assessment. © 2021 IEEE

关键词： Deep learning

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Euler-Discretized GZ-Type Complex Neuronet computing Real-Time Varying Complex Matrix Inverse 36

Euler-Discretized GZ-Type Complex Neuronet Computing Real-Ti...

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第36届中国控制会议

作者： Yunong Zhang Guofu Wu Binbin Qiu Wan Li Penghao He School of Information Science and Technology Sun Yat-sen University(SYSU) Key Laboratory of Autonomous Systems and Networked Control Ministry of Education SYSU-CMU Shunde International Joint Reseach Institute Key Laboratory of Machine Intelligence and Advanced Computing(Sun Yat-sen University) Ministry of Education

ISBN: (纸本)9781538629185

In this paper,a GZ-type complex neuronet（generated by combining gradient dynamic system and Zhang dynamic system） is illustrated and discretized into an Euler-discretized（i.e.,discretized by Euler forward rule） GZ-type complex neuronet for computing real-time varying complex matrix *** results not only show the efficacy of the proposed Eulerdiscretized GZ-type neuronet but also verify that the error pattern is square.

关键词： GZ-Type Neuronet Complex Matrix Inverse Euler Forward Rule Computer Simulation Error Pattern

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Different reformulation, ZD tracking control and analysis of one-link rigid robot system with motor dynamics 15

Different reformulation, ZD tracking control and analysis of...

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15th IEEE International Conference on Mechatronics and Automation, ICMA 2018

作者： Guo, Jinjin Qiu, Binbin Zhang, Deyang Mao, Mingzhi Zhang, Yunong School of Information Science and Technology Sun Yat-sen University Guangzhou510006 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou510006 China

ISBN: (纸本)9781538660720

As machinery transmission devices, motors are commonly utilized in robot systems and may significantly affect the dynamic characteristics and stability of robot systems. In view of the fundamental role of the one-link rigid robot system, this paper investigates the tracking control of such a system with motor dynamics. Based on Zhang dynamics (ZD), a new kind of ZD controller is developed and investigated for achieving the tracking-control purpose. Besides, the characteristics and differences of the ZD method compared with other conventional methods are presented. Moreover, theoretical analysis and result are also presented to guarantee the global and exponential convergence performance of the ZD controller. © 2018 IEEE.

关键词： machinery

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Grasp as You Say: Language-guided Dexterous Grasp Generation 38

Grasp as You Say: Language-guided Dexterous Grasp Generation

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wei, Yi-Lin Jiang, Jian-Jian Xing, Chengyi Tan, Xian-Tuo Wu, Xiao-Ming Li, Hao Cutkosky, Mark Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Stanford University United States Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

This paper explores a novel task "Dexterous Grasp as You Say" (DexGYS), enabling robots to perform dexterous grasping based on human commands expressed in natural language. However, the development of this field is hindered by the lack of datasets with natural human guidance;thus, we propose a language-guided dexterous grasp dataset, named DexGYSNet, offering high-quality dexterous grasp annotations along with flexible and fine-grained human language guidance. Our dataset construction is cost-efficient, with the carefully-design hand-object interaction retargeting strategy, and the LLM-assisted language guidance annotation system. Equipped with this dataset, we introduce the DexGYSGrasp framework for generating dexterous grasps based on human language instructions, with the capability of producing grasps that are intent-aligned, high quality and diversity. To achieve this capability, our framework decomposes the complex learning process into two manageable progressive objectives and introduce two components to realize them. The first component learns the grasp distribution focusing on intention alignment and generation diversity. And the second component refines the grasp quality while maintaining intention consistency. Extensive experiments are conducted on DexGYSNet and real world environments for validation. © 2024 Neural information processing systems foundation. All rights reserved.

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Joint regression and learning from pairwise rankings for personalized image aesthetic assessment

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Computational Visual Media 2021年第2期7卷 241-252页

作者： Jin Zhou Qing Zhang Jian-Hao Fan Wei Sun Wei-Shi Zheng School of Electronics and Information Technology Sun Yat-sen UniversityGuangzhou 510006China. School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education(Sun Yat-sen University)Guangzhou 510006China Peng Cheng Laboratory Shenzhen 518000China

Recent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks(CNNs).However,these methods focus primarily on predicting generally perceived preference of an image,making them usually have limited practicability,since each user may have completely different preferences for the same *** address this problem,this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal *** achieve this in a coarse to fine manner,by joint regression and learning from pairwise ***,we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image *** then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores,and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based ***,we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge *** demonstrate that our method can effectively learn personalized image aesthetic preferences,clearly outperforming state-of-the-art ***,we show that the learned personalized image aesthetic benefits a wide variety of applications.

关键词： s personalized image aesthetic assessment deep convolutional neural networks pairwise ranking regression

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CONFIDENCE-GUIDED ADAPTIVE GATE AND DUAL DIFFERENTIAL ENHANCEMENT FOR VIDEO SALIENT OBJECT DETECTION

CONFIDENCE-GUIDED ADAPTIVE GATE AND DUAL DIFFERENTIAL ENHANC...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Chen, Peijia Lai, Jianhuang Wang, Guangcong Zhou, Huajun School of Computer Science and Engineering Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9781665438643

Video salient object detection (VSOD) aims to locate and segment the most attractive object by exploiting both spatial cues and temporal cues hidden in video sequences. However, spatial and temporal cues are often unreliable in real-world scenarios, such as low-contrast foreground, fast motion, and multiple moving objects. To address these problems, we propose a new framework to adaptively capture available information from spatial and temporal cues, which contains Confidence-guided Adaptive Gate (CAG) modules and Dual Differential Enhancement (DDE) modules. For both RGB features and optical flow features, CAG estimates confidence scores supervised by the IoU between predictions and the ground truths to re-calibrate the information with a gate mechanism. DDE captures the differential feature representation to enrich the spatial and temporal information and generate the fused features. Experimental results on four widely used datasets demonstrate the effectiveness of the proposed method against thirteen state-of-the-art methods. © 2021 IEEE

关键词： Object detection

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LiSeg: Lightweight Road-object Semantic Segmentation in 3D LiDAR Scans for Autonomous Driving

LiSeg: Lightweight Road-object Semantic Segmentation in 3D L...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Zhang, Wenquan Zhou, Chancheng Yang, Junjie Huang, Kai Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Ministry of Education China School of Data and Computer Science Sun Yat-sen University China

ISBN: (纸本)9781538644522

LiDAR based perception module plays an important role in autonomous driving. However, the present CNN models are designed for image processing but not LiDAR point clouds. The performances of such models are limited by the great memory consumption and heavy computation cost. In this work, a lightweight CNN model, Liseg, is proposed to perform real-time road-object semantic segmentation on LiDAR point cloud scans for autonomous driving. The model size of Liseg is several times smaller than others, while achieving high accuracy. © 2018 IEEE.

关键词： Autonomous vehicles

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Neighbor combinatorial attention for critical structure mining 29

Neighbor combinatorial attention for critical structure mini...

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29th International Joint Conference on Artificial intelligence, IJCAI 2020

作者： Zuo, Tanli Qiu, Yukun Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University China Peng Cheng Laboratory Shenzhen518005 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9780999241165

Graph convolutional networks (GCNs) have been widely used to process graph-structured data. However, existing GNN methods do not explicitly extract critical structures, which reflect the intrinsic property of a graph. In this work, we propose a novel GCN module named Neighbor Combinatorial ATtention (NCAT) to find critical structure in graph-structured data. NCAT attempts to match combinatorial neighbors with learnable patterns and assigns different weights to each combination based on the matching degree between the patterns and combinations. By stacking several NCAT modules, we can extract hierarchical structures that is helpful for down-stream tasks. Our experimental results show that NCAT achieves state-of-the-art performance on several benchmark graph classification datasets. In addition, we interpret what kind of features our model learned by visualizing the extracted critical structures. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Benchmarking

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AM³Net: Adaptive Mutual-Learning-Based Multimodal Data Fusion Network

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IEEE Transactions on Circuits and Systems for Video Technology 2022年第8期32卷 5411-5426页

作者： Wang, Jinping Li, Jun Shi, Yanli Lai, Jianhuang Tan, Xiaojun Sun Yat-sen University School of Intelligent Systems Engineering Guangzhou510006 China Sun Yat-sen University School of Computer Science and Engineering Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou510006 China Zhuhai519082 China

Multimodal data fusion, e.g., hyperspectral image (HSI) and light detection and ranging (LiDAR) data fusion, plays an important role in object recognition and classification tasks. However, existing methods pay little attention to the specificity of HSI spectral channels and the complementarity of HSI and LiDAR spatial information. In addition, the utilized feature extraction modules tend to consider the feature transmission processes among different modalities independently. Therefore, a new data fusion network named AM3Net is proposed for multimodal data classification;it includes three parts. First, an involution operator slides over the input HSI's spectral channels, which can independently measure the contribution rate of the spectral channel of each pixel to the spectral feature tensor construction. Furthermore, the spatial information of HSI and LiDAR data is integrated and excavated in an adaptively fused, modality-oriented manner. Second, a spectral-spatial mutual-guided module is designed for the feature collaborative transmission among spectral features and spatial information, which can increase the semantic relatedness connection through adaptive, multiscale, and mutual-learning transmission. Finally, the fused spatial-spectral features are embedded into a classification module to obtain the final results, which determines whether to continue updating the network weights. Experimental evaluations on HSI-LiDAR datasets indicate that AM3Net possesses a better feature representation ability than the state-of-the-art methods. Additionally, AM3Net still maintains considerable performance when its input is replaced with multispectral and synthetic aperture radar data. The result indicates that the proposed data fusion framework is compatible with diversified data types. © 1991-2012 IEEE.

关键词： Feature extraction

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A cooperative spectrum sensing method based on clustering algorithm and signal feature 4th

A cooperative spectrum sensing method based on clustering al...

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4th International Conference on Cloud computing and Security, ICCCS 2018

作者： Zhang, Shunchao Wang, Yonghua Wan, Pin Zhang, Yongwei Li, Xingcheng School of Automation Guangdong University of Technology Guangzhou510006 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-Sen University Guangzhou510006 China

ISBN: (纸本)9783030000059

To solve the problem that the threshold is difficult to calculate in the spectrum sensing method of random matrix, this paper proposes a spectrum sensing method based on the combination of clustering algorithm and signal features. At the same time, in order to improve the performance of feature estimation and the detection performance under the condition of small number of cooperative users, a concept based on stochastic matrix splitting and reorganization is introduced to logically increase the number of cooperative users. In order to further obtain the information from the signal matrix and improve the feature accuracy, the signal perceived by each secondary user (SU) is decomposed into I and Q (IQ) components. Firstly, the signal matrix is split and reassembled and IQ decomposed. Then the covariance matrices of split matrix and matrix after IQ decomposition are calculated respectively and the corresponding eigenvalues are obtained. Then, the features are formed into a feature vector. Finally, the algorithm classifies these feature vector. The simulation experiments under different signal characteristics and different clustering algorithms show that the proposed method can effectively improve the performance of spectrum sensing. © Springer Nature Switzerland AG 2018.

关键词： Clustering algorithms

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