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Competition for roadside camera monocular 3D object detection

National science Review 2023年第6期10卷 34-37页

作者： Jinrang Jia Yifeng Shi Yuli Qu Rui Wang Xing Xu Hai Zhang Baidu Inc. Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University College of Software Jilin University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION Accurate environment perception is a critical topic in autonomous driving and intelligent *** environmental perception methods mostly rely on on-board ***,limited by the installation height,thereare problems such as blind spots and unstable long-range perception in vehicle perceptual ***,with the rapid improvement of intelligent infrastructure,it has become possible to use roadside cameras for traffic environment *** from the increased height when compared with on-boardsensors,roadside cameras can obtain a larger perceptual field of view and realize long-range observation.

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A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimization 27

A First-Order Multi-Gradient Algorithm for Multi-Objective B...

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27th European Conference on artificial intelligence, ECAI 2024

作者： Ye, Feiyang Lin, Baijiong Cao, Xiaofeng Zhang, Yu Tsang, Ivor W. Department of Computer Science and Engineering Southern University of Science and Technology China Australian Artificial Intelligence Institute University of Technology Sydney Australia The Hong Kong University of Science and Technology Guangzhou China School of Artificial Intelligence Jilin University China Shanghai Artificial Intelligence Laboratory China Centre for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781643685489

In this paper, we study the Multi-Objective Bi-Level Optimization (MOBLO) problem, where the upper-level subproblem is a multi-objective optimization problem and the lower-level subproblem is for scalar optimization. Existing gradient-based MOBLO algorithms need to compute the Hessian matrix, causing the computational inefficient problem. To address this, we propose an efficient first-order multi-gradient method for MOBLO, called FORUM. Specifically, we reformulate MOBLO problems as a constrained multi-objective optimization (MOO) problem via the value-function approach. Then we propose a novel multi-gradient aggregation method to solve the challenging constrained MOO problem. Theoretically, we provide the complexity analysis to show the efficiency of the proposed method and a non-asymptotic convergence result. Empirically, extensive experiments demonstrate the effectiveness and efficiency of the proposed FORUM method in different learning problems. In particular, it achieves state-of-the-art performance on three multi-task learning benchmark datasets. © 2024 The Authors.

关键词： Optimization algorithms

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A concise and visual computational interpretation of emotional expression in hand-drawn lines 3

A concise and visual computational interpretation of emotion...

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3rd International Conference on Machine Vision, Automatic Identification, and Detection, MVAID 2024

作者： Li, Jiayi Li, Yize Lin, Yunhui Lyu, Ruimin School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214000 China Jiangsu Key Laboratory of Media Design and Software Technology Wuxi214000 China

ISBN: (纸本)9781510681880

In the realms of visual arts and psychology, it's acknowledged that lines and their properties significantly impact human emotional responses. Historically, various characteristics of lines have been proposed for emotional expression research, yet, the pivotal technique in computer vision, Scale-Invariant Feature Transform (SIFT), has frequently been disregarded. computing SIFT key points and their orientations on lines unveils the morphological traits of lines and deciphers their connection with specific emotional states. This method is not only concise in computation but also offers intuitive visualizations and explanations. Our investigation delves into the crucial role of SIFT features in conveying emotions from a computational vision standpoint. By scrutinizing SIFT features extracted from hand-drawn line images, we examined the trends in the quantity and orientation of feature points, and vividly illustrated how these points influence line morphology. Logistic regression analysis has further exposed the linkage between feature points and emotional dimensions such as pleasure and arousal. The results underscore the significance of SIFT features in emotional expression, facilitating the advancement of affective computing applications, as well as emotion recognition and analysis grounded in line features. © 2024 SPIE.

关键词： Regression analysis

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DEEM: Dynamic Experienced Expert Modeling for Stance Detection 30

DEEM: Dynamic Experienced Expert Modeling for Stance Detecti...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Wang, Xiaolong Wang, Yile Cheng, Sijie Li, Peng Liu, Yang Department of Computer Science and Technology Tsinghua University Beijing China Tsinghua University Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China Gansu China

ISBN: (纸本)9782493814104

Recent work has made a preliminary attempt to use large language models (LLMs) to solve the stance detection task, showing promising results. However, considering that stance detection usually requires detailed background knowledge, the vanilla reasoning method may neglect the domain knowledge to make a professional and accurate analysis. Thus, there is still room for improvement of LLMs reasoning, especially in leveraging the generation capability of LLMs to simulate specific experts (i.e., multi-agents) to detect the stance. In this paper, different from existing multi-agent works that require detailed descriptions and use fixed experts, we propose a Dynamic Experienced Expert Modeling (DEEM) method which can leverage the generated experienced experts and let LLMs reason in a semi-parametric way, making the experts more generalizable and reliable. Experimental results demonstrate that DEEM consistently achieves the best results on three standard benchmarks, outperforms methods with self-consistency reasoning, and reduces the bias of LLMs. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Modeling languages

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MAXIMUM n-TIMES COVERAGE FOR VACCINE DESIGN 10

MAXIMUM n-TIMES COVERAGE FOR VACCINE DESIGN

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10th International Conference on Learning Representations, ICLR 2022

作者： Liu, Ge Dimitrakakis, Alexander Carter, Brandon Gifford, David Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States

We introduce the maximum n-times coverage problem that selects k overlays to maximize the summed coverage of weighted elements, where each element must be covered at least n times. We also define the min-cost n-times coverage problem where the objective is to select the minimum set of overlays such that the sum of the weights of elements that are covered at least n times is at least τ. Maximum n-times coverage is a generalization of the multi-set multi-cover problem, is NP-complete, and is not submodular. We introduce two new practical solutions for n-times coverage based on integer linear programming and sequential greedy optimization. We show that maximum n-times coverage is a natural way to frame peptide vaccine design, and find that it produces a pan-strain COVID-19 vaccine design that is superior to 29 other published designs in predicted population coverage and the expected number of peptides displayed by each individual's HLA molecules. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Vaccines

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Safe Control for Soft-Rigid Robots with Self-Contact Using Control Barrier Functions

Safe Control for Soft-Rigid Robots with Self-Contact Using C...

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IEEE International Conference on Soft Robotics (RoboSoft)

作者： Zach J. Patterson Wei Xiao Emily Sologuren Daniela Rus Computer Science and Artificial Intelligence Laboratory MIT.

Incorporating both flexible and rigid components in robot designs offers a unique solution to the limitations of traditional rigid robotics by enabling both compliance and strength. This paper explores the challenges and solutions for controlling soft-rigid hybrid robots, particularly addressing the issue of self-contact. Conventional control methods prioritize precise state tracking, inadvertently increasing the system's overall stiffness, which is not always desirable in interactions with the environment or within the robot itself. To address this, we investigate the application of Control Barrier Functions (CBFs) and High Order CBFs to manage self-contact scenarios in serially connected soft-rigid hybrid robots. Through an analysis based on Piecewise Constant Curvature (PCC) kinematics, we establish CBFs within a classical control framework for self-contact dynamics. Our methodology is rigorously evaluated in both simulation environments and physical hardware systems. The findings demonstrate that our proposed control strategy effectively regulates self-contact in soft-rigid hybrid robotic systems, marking a significant advancement in the field of robotics.

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Respiratory sound classification using multi-resolution features 24

Respiratory sound classification using multi-resolution feat...

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24th International Congress on Acoustics, ICA 2022

作者： Seo, HyungJin Jeong, Chang-Hyeon Kim, DaHye Woo, JongMin Yoo, In-Chul Yook, Dongsuk Artificial Intelligence Laboratory Department of Computer Science Korea University Korea Republic of

With the advance of deep learning technologies, medical image analysis using deep neural networks is achieving very high performance. On the other hand, while sounds contain medical information just as images, medical sound analysis such as automatic identification of adventitious respiratory sound has still been a challenging problem. Most of previous studies for automatic identification of adventitious respiratory sound mainly focused on applying various deep neural networks to the respiratory sound classification using the features developed for automatic speech recognition. In this paper, after the close analysis of adventitious respiratory sounds, we propose to consider a multi-resolution feature to capture the characteristics of widely varying adventitious respiratory sounds. The efficiency of the proposed method is evaluated using the International Conference on Biomedical and Health Informatics (ICBHI) 2017 dataset. © 2022 Proceedings of the International Congress on Acoustics. All rights reserved.

关键词： Deep neural networks

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Carrying Out CNN Channel Pruning in a White Box

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IEEE Transactions on Neural Networks and Learning Systems 2023年第10期34卷 7946-7955页

作者： Zhang, Yuxin Lin, Mingbao Lin, Chia-Wen Chen, Jie Wu, Yongjian Tian, Yonghong Ji, Rongrong Xiamen University Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen361005 China Youtu Laboratory Tencent Shanghai200233 China National Tsing Hua University Department of Electrical Engineering The Institute of Communications Engineering Hsinchu30013 Taiwan Peng Cheng Laboratory Shenzhen518000 China Peking University Department of Computer Science and Technology Beijing100871 China School of Informatics and the Institute of Artificial Intelligence Xiamen University Media Analytics and Computing Laboratory Department of Artificial Intelligence Xiamen361005 China

Channel pruning has been long studied to compress convolutional neural networks (CNNs), which significantly reduces the overall computation. Prior works implement channel pruning in an unexplainable manner, which tends to reduce the final classification errors while failing to consider the internal influence of each channel. In this article, we conduct channel pruning in a white box. Through deep visualization of feature maps activated by different channels, we observe that different channels have a varying contribution to different categories in image classification. Inspired by this, we choose to preserve channels contributing to most categories. Specifically, to model the contribution of each channel to differentiating categories, we develop a class-wise mask for each channel, implemented in a dynamic training manner with respect to the input image's category. On the basis of the learned class-wise mask, we perform a global voting mechanism to remove channels with less category discrimination. Lastly, a fine-tuning process is conducted to recover the performance of the pruned model. To our best knowledge, it is the first time that CNN interpretability theory is considered to guide channel pruning. Extensive experiments on representative image classification tasks demonstrate the superiority of our White-Box over many state-of-the-arts (SOTAs). For instance, on CIFAR-10, it reduces 65.23% floating point operations per seconds (FLOPs) with even 0.62% accuracy improvement for ResNet-110. On ILSVRC-2012, White-Box achieves a 45.6% FLOP reduction with only a small loss of 0.83% in the top-1 accuracy for ResNet-50. Code is available at https://***/zyxxμWhite-Box. © 2012 IEEE.

关键词： Visualization

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Intelligent cache and buffer optimization for mobile VR adaptive transmission in 5G edge computing networks

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Digital Communications and Networks 2024年第5期10卷 1234-1244页

作者： Junchao Yang Ali Kashif Bashir Zhiwei Guo Keping Yu Mohsen Guizani Chongqing Key Laboratory of Intelligent Perception and BlockChain Technology School of Artificial IntelligenceChongqing Technology and Business UniversityChongqing400067China Department of Computing and Mathematics Manchester Metropolitan UniversityUK Woxsen School of Business Woxsen UniversityHyderabad 502345India Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Graduate School of Science and Engineering Hosei UniversityTokyo184-8584Japan Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence(MBZUAI)United Arab Emirates

Virtual Reality(VR)is a key industry for the development of the digital economy in the *** VR has advantages in terms of mobility,lightweight and cost-effectiveness,which has gradually become the mainstream implementation of *** this paper,a mobile VR video adaptive transmission mechanism based on intelligent caching and hierarchical buffering strategy in Mobile Edge Computing(MEC)-equipped 5G networks is proposed,aiming at the low latency requirements of mobile VR services and flexible buffer management for VR video adaptive *** support VR content proactive caching and intelligent buffer management,users’behavioral similarity and head movement trajectory are jointly used for viewpoint *** tile-based content is proactively cached in the MEC nodes based on the popularity of the VR ***,a hierarchical buffer-based adaptive update algorithm is presented,which jointly considers bandwidth,buffer,and predicted viewpoint status to update the tile chunk in client ***,according to the decomposition of the problem,the buffer update problem is modeled as an optimization problem,and the corresponding solution algorithms are ***,the simulation results show that the adaptive caching algorithm based on 5G intelligent edge and hierarchical buffer strategy can improve the user experience in the case of bandwidth fluctuations,and the proposed viewpoint prediction method can significantly improve the accuracy of viewpoint prediction by 15%.

关键词： Virtual reality Adaptive transmission Edge cache Buffer management 5G Mobile edge computing

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Hypernym Information and Sentiment Bias Probing in Distributed Data Representation 23

Hypernym Information and Sentiment Bias Probing in Distribut...

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15th International Conference on Machine Learning and Computing, ICMLC 2023

作者： Acquaye, Frank Lawrence Nii Adoquaye Latipov, Insan-Aleksandr Kertész-Farkas, Attila Department of Data Analysis and Artificial Intelligence Laboratory on AI for Computational Biology Faculty of Computer Science HSE University Moscow Russia

ISBN: (纸本)9781450398411

Neural word embedding vectors have been exhaustively investigated by probing tasks, whether they contain semantic and syn- tactic information. Perhaps the most popular task is a test on gender relation "king - man + woman ≈ queen", other probings include tests on singular/plural relation (apple∼apples), analogy (good:better∼rough:X), purity of the clusters of word embeddings (categorization). Here, we propose two novel probing tasks to evaluate the compositionality of word embeddings. First, we propose probing tests to evaluate whether word embeddings contain structural information about their hypernyms;more specifically, whether it is possible to predict the hypernym (e.g. color) of a word (e.g. red) using word embedding. Our experimental results show that a simple logistic regression can predict well the hypernyms of words;therefore, making the word embeddings linearly separable with respect to their hypernyms. Second, we also provide a methodology to investigate whether word embeddings encode undesired sentiment. For instance, we show that the word embedding of American or Euro- pean are surrounded by more positive words than the embeddings around the word "Chinese". Thus, we conclude that word embed- dings also can capture sentiment, leading to undesired machine bias in downstream applications. © 2023 ACM.

关键词： Embeddings

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