检索结果-内蒙古大学图书馆

Progressive Human Motion Generation Based on Text and Few Motion Frames

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Zeng, Ling-An Wu, Gaojie Wu, Ancong Hu, Jian-Fang Zheng, Wei-Shi Sun Yat-sen University School of Artificial Intelligence Guangdong Zhuhai519082 China Sun Yat-sen University School of Computer Science and Engineering Guangdong Guangzhou510275 China Sun Yat-sen University School of Computer Science and Engineering China Guangdong Key Laboratory of Information Security Technology China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing China Peng Cheng Laboratory China

Although existing text-to-motion (T2M) methods can produce realistic human motion from text description, it is still difficult to align the generated motion with the desired postures since using text alone is insufficient for precisely describing diverse postures. To achieve more controllable generation, an intuitive way is to allow the user to input a few motion frames describing precise desired postures. Thus, we explore a new Text-Frame-to-Motion (TF2M) generation task that aims to generate motions from text and very few given frames. Intuitively, the closer a frame is to a given frame, the lower the uncertainty of this frame is when conditioned on this given frame. Hence, we propose a novel Progressive Motion Generation (PMG) method to progressively generate a motion from the frames with low uncertainty to those with high uncertainty in multiple stages. During each stage, new frames are generated by a Text-Frame Guided Generator conditioned on frame-aware semantics of the text, given frames, and frames generated in previous stages. Additionally, to alleviate the train-test gap caused by multi-stage accumulation of incorrectly generated frames during testing, we propose a Pseudo-frame Replacement Strategy for training. Experimental results show that our PMG outperforms existing T2M generation methods by a large margin with even one given frame, validating the effectiveness of our PMG. Code is available here. © 1991-2012 IEEE.

关键词： Controllable Motion Generation Human Motion Text2Motion Generation

来源：评论

学校读者我要写书评

暂无评论

A Novel Carbon Paste Electrode for Convenient and Efficient Auditory Brainstem Response Acquisition: A Pilot Study 1

引用

15th International Conference on Sensing Technology, ICST 2022

作者： Wang, Xin Tian, Qiong Xu, Yangjie Zhu, Mingxing Wang, Yingying He, Yuchao Chen, Shixiong Liu, Zhiyuan Li, Guanglin CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg1855 Luxembourg School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen518055 China

ISBN: (数字)9783031298714

ISBN: (纸本)9783031298707

Auditory Brainstem Response (ABR) is the commonly used tool in Otology for clinical hearing loss and disease diagnoses. Wet electrodes, served as the gold standard, are widely used in clinical settings to acquire ABR signals for superior performance. However, it is a complex and time-consuming process to fix the electrodes by applying bandages or adhesive tapes. Moreover, wet electrodes might cause allergy and uncomfortable for patients, especially for children. Therefore, a novel anhydrous carbon paste electrode (CPE) with adhesion and biocompatibility was proposed to solve the aforementioned problems. The impedance of unfixed CPE and Conductive Paste-based electrode was recorded, and the ABR waveforms obtained by both two methods were systematically compared to further investigate and verify the feasibility of the proposed unfixed CPE. The results showed that the proposed CPE could achieve high-quality ABR recording under unfixed condition, which was comparable with the gold standard method. Besides, the analyses of the latency and amplitude of the ABRs showed that unfixed CPE could acquire higher amplitudes which would help with wave recognition, while the latency was similar. This proved that the unfixed CPE could obtain ABR with the same information compared with that of Paste-based ABR, since the physicians always made the diagnosis by analyzing the latency. Furtherly, we compared the correlation coefficient of the ABRs under different averaging times, and the results showed that unfixed CPE was comparable, even better than the conventional method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electrodes

来源：评论

学校读者我要写书评

暂无评论

SA-GCNN: Spatial Attention Based Graph Convolutional Neural Network for Pedestrian Trajectory Prediction

SA-GCNN: Spatial Attention Based Graph Convolutional Neural ...

引用

2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Li, Xuesong Zhang, Qieshi Wang, Wanting Tang, Jian Liu, Dong Cheng, Jun Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Beijing China The Chinese University of Hong Kong Hong Kong Co. Ltd China

ISBN: (纸本)9798350325706

Accurate predicting the trajectories of moving pedestrians is a key technology in automatic driving system, which is challenging due to the complex interactions in pedestrians. Recent studies have shown that Spatio-Temporal (ST) graph has great ability to capture interactions between pedestrians. However, these methods neglect pedestrian's limited vision and contains many invalid interactions. In order to tackle this issue, we proposed a Spatial Attention based Graph Convolutional Neural Network (SA-GCNN), which uses SA module to construct ST graph and focus on the most useful interactions. Meanwhile, SA-GCNN introduces temporal convolution module to capture temporal dependency between ST graphs. Moreover, the Graph Convolutional Network (GCN) and Temporal Convolution Network (TCN) are combined to extract graph features and decode multi-modal trajectories. Our model is trained on the widely-accepted benchmark datasets ETH and UCY. The empirical findings demonstrate that our SA-GCNN surpasses the performance of existing state-of-the-art methods used for comparison, suggests that our proposed model exhibits enhanced proficiency in capturing pedestrian interactions. © 2023 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Labrador: Response Guided Directed Fuzzing for Black-box IoT Devices

Labrador: Response Guided Directed Fuzzing for Black-box IoT...

引用

IEEE Symposium on Security and Privacy

作者： Hangtian Liu Shuitao Gan Chao Zhang Zicong Gao Hongqi Zhang Xiangzhi Wang Guangming Gao Information Engineering University Tsinghua University Laboratory for Advanced Computing and Intelligence Engineering Zhongguancun Laboratory Henan Key Laboratory of Information Security University of Electronic Science and Technology of China

ISBN: (数字)9798350331301

ISBN: (纸本)9798350331318

Fuzzing is a popular solution to finding vulnerabilities in software including IoT firmware. However, due to the challenges of emulating or rehosting firmware, some IoT devices (e.g., enterprise-level devices) can only be fuzzed in a black-box manner, which makes fuzzers blind and inefficient due to missing feedbacks (e.g., code coverage or distance). In this paper, we present a novel response guided directed fuzzing solution Labrador, able to test black-box IoT devices efficiently. Specifically, we leverage the network response to infer the execution trace of firmware and deduce the code coverage of testing. Second, we leverage the test case (i.e., request) and its response to estimate the distance to the target sensitive code (i.e., sink). Lastly, we further leverage the distance to guide test case mutation, which efficiently drives directed fuzzing toward candidate vulnerable code. We have implemented a prototype of Labrador and evaluated it on 14 different enterprise-level IoT devices. Results showed that Labrador significantly outperforms state-of-the-art (SOTA) solutions. It finds 44X more vulnerabilities than SNIPUZZ, BOOFUZZ and FIRM-AFL and 8.57X more vulnerabilities than SaTC. In total, it discovered 79 unknown vulnerabilities, of which 61 were assigned with CVEs.

关键词： Privacy Codes Closed box Prototypes Fuzzing Drives Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Grasp as you say: language-guided dexterous grasp generation 24

Grasp as you say: language-guided dexterous grasp generation

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

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

ISBN: (纸本)9798331314385

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Compactness and Separateness Driven Fuzzy Clustering Validity Index Called TLW 18th

Compactness and Separateness Driven Fuzzy Clustering Validit...

引用

18th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2023

作者： Tang, Yiming Wang, Xiang Li, Bing Hu, Xianghui Xie, Wenjun Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Anhui Hefei230601 China Engineering Research Center of Safety Critical Industry Measure and Control Technology Ministry of Education Hefei University of Technology Anhui Hefei230601 China

ISBN: (纸本)9789819996391

The design of validity index of fuzzy clustering has always been a historical problem in fuzzy clustering field. When the distribution of cluster centers is very close, it is difficult for the existing fuzzy clustering validity indexes to obtain a reasonable cluster number, and the separation mechanism of these indexes is too simple. In order to solve the above problems, we propose a novel fuzzy clustering validity index called TLW (Tang-Li-Wang) index. Firstly, compactness is expressed as the ratio of the membership weighted distance value to the sample variance of the dataset. Secondly, the sum of the maximum distance between cluster centers and the mean distance is used in separateness, and the sample variance of cluster centers is introduced, and the two are multiplied to describe the separateness. Thirdly, on the basis of considering compactness and separateness, the introduction of cluster number can alleviate the phenomenon that the index value may change monotonically with the increase of cluster number. Finally, the classical FCM (Fuzzy C-Mean) algorithm is used to conduct experiments on indexes. Comparative experiments and analyses were carried out on 17 typical datasets and 12 clustering validity indexes. From the experimental results of normal simple datasets and high-dimensional difficult datasets, the proposed index shows some advantages. All in all, these results verify that the proposed TLW index has better accuracy and stronger stability. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy clustering

来源：评论

学校读者我要写书评

暂无评论

Generative Visual Commonsense Answering and Explaining with Generative Scene Graph Constructing

arXiv

引用

arXiv 2025年

作者： Yuan, Fan Fang, Xiaoyuan Quan, Rong Li, Jing Bi, Wei Xu, Xiaogang Li, Piji College of Artificial Intelligence Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing211106 China College of Artificial Intelligence Nanjing University of Aeronautics and Astronautics Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Nanjing211106 China Department of Computing The Hong Kong Polytechnic University Hong Kong The Chinese University of Hong Kong Hong Kong

Visual Commonsense Reasoning, which is regarded as one challenging task to pursue advanced visual scene comprehension, has been used to diagnose the reasoning ability of AI systems. However, reliable reasoning requires a good grasp of the scene’s details. Existing work fails to effectively exploit the real-world object relationship information present within the scene, and instead overly relies on knowledge from training memory. Based on these observations, we propose a novel scene-graph-enhanced visual commonsense reasoning generation method named G2, which first utilizes the image patches and LLMs to construct a location-free scene graph, and then answer and explain based on the scene graph’s information. We also propose automatic scene graph filtering and selection strategies to absorb valuable scene graph information during training. Extensive experiments are conducted on the tasks and datasets of scene graph constructing and visual commonsense answering and explaining, respectively. Experimental results and ablation analysis demonstrate the effectiveness of our proposed framework. Copyright © 2025, The Authors. All rights reserved.

关键词： Question answering

来源：评论

学校读者我要写书评

暂无评论

Survey on the Improvement and Application of HHL Algorithm

Survey on the Improvement and Application of HHL Algorithm

引用

2022 International Conference on Computer Modelling and Big Data Analytics, CMBDA 2022

作者： Liu, Xiaonan Xie, Haoshan Liu, Zhengyu Zhao, Chenyan State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University ZhengZhou450000 China School of Computer and Artificial Intelligence ZhengZhou University ZhengZhou450000 China

Quantum computing is a new computing mode that follows the laws of quantum mechanics to control quantum information units for computation. In terms of computational efficiency, due to the existence of quantum mechanical superposition, some known quantum algorithms can process problems faster than traditional general-purpose computers. HHL algorithm is an algorithm for solving linear system problems. Compared with classical algorithms in solving linear equations, it has an exponential acceleration effect in certain cases and as a sub-module, it is widely used in some machine learning algorithms to form quantum machines learning algorithms. However, there are some limiting factors in the use of this algorithm, which affect the overall effect of the algorithm. How to improve it to make the algorithm perform better has become an important issue in the field of quantum computing. This paper summarizes the optimization and improvement of HHL algorithm since it was proposed, and the application of HHL algorithm in machine learning, and discusses some possible future improvements of some subroutines in HHL algorithm. © Published under licence by IOP Publishing Ltd.

关键词： Linear systems

来源：评论

学校读者我要写书评

暂无评论

Graph Prompt Clustering

引用

IEEE Transactions on Pattern Analysis and machine intelligence 2025年第7期PP卷 PP页

作者： Chen, Man-Sheng Lai, Pei-Yuan Liao, De-Zhang Wang, Chang-Dong Lai, Jian-Huang Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing China Guangdong University of Technology School of Information Engineering Guangzhou China South China Technology Commercialization Center Guangzhou China

Due to the wide existence of unlabeled graph-structured data (e.g. molecular structures), the graph-level clustering has recently attracted increasing attention, whose goal is to divide the input graphs into several disjoint groups. However, the existing methods habitually focus on learning the graphs embeddings with different graph reguralizations, and seldom refer to the obvious differences in data distributions of distinct graph-level datasets. How to characteristically consider multiple graph-level datasets in a general well-designed model without prior knowledge is still challenging. In view of this, we propose a novel Graph Prompt Clustering (GPC) method. Within this model, there are two main modules, i.e., graph model pretraining as well as prompt and finetuning. In the graph model pretraining module, the graph model is pretrained by a selected source graph-level dataset with mutual information maximization and self-supervised clustering regularization. In the prompt and finetuning module, the network parameters of the pretrained graph model are frozen, and a groups of learnable prompt vectors assigned to each graph-level representation are trained for adapting different target graph-level datasets with various data distributions. Experimental results across six benchmark datasets demonstrate the impressive generalization capability and effectiveness of GPC compared with the state-of-the-art methods. © 1979-2012 IEEE.

关键词： Graph embeddings

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：