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arXiv 2023年

作者： Chen, Xiuwei Chang, Xiaobin School of Artificial Intelligence Sun Yat-Sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

The rehearsal strategy is widely used to alleviate the catastrophic forgetting problem in class incremental learning (CIL) by preserving limited exemplars from previous tasks. With imbalanced sample numbers between old and new classes, the classifier learning can be biased. Existing CIL methods exploit the long-tailed (LT) recognition techniques, e.g., the adjusted losses and the data re-sampling methods, to handle the data imbalance issue within each increment task. In this work, the dynamic nature of data imbalance in CIL is shown and a novel Dynamic Residual Classifier (DRC) is proposed to handle this challenging scenario. Specifically, DRC is built upon a recent advance residual classifier with the branch layer merging to handle the model-growing problem. Moreover, DRC is compatible with different CIL pipelines and substantially improves them. Combining DRC with the model adaptation and fusion (MAF) pipeline, this method achieves state-of-the-art results on both the conventional CIL and the LT-CIL benchmarks. Extensive experiments are also conducted for a detailed analysis. The code is publicly available Copyright © 2023, The Authors. All rights reserved.

关键词： Pipelines

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RL-Based USV Path Planning under the Marine Multimodal Features Considerations

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IEEE Internet of Things Journal 2025年第11期12卷 15274-15287页

作者： Lin, Quanbao Gou, Huaxing Tian, Peidong Zuo, Tian-Yu Zhang, Hanzhong Wang, Xin Sun, Poly Z. H. Shanghai Jiao Tong University School of Aeronautics and Astronautics Shanghai200240 China Southeast University School of Computer Science and Engineering Nanjing211189 China Shanghai University School of Microelectronics Shanghai200444 China Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Guangdong Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Guangdong Shenzhen518055 China East China Normal University School of Psychology and Cognitive Science Shanghai200062 China Shanghai Jiao Tong University Department of Industrial Engineering Shanghai200240 China

Path planning is an important step in ensuring the safety of Unmanned Surface Vehicle (USV) navigation and executing missions quickly and efficiently. However, current USV path planning methods lack comprehensive consideration of electronic nautical charts and meteorological data, resulting in planned paths being unable to fully utilize marine environmental conditions, which may easily lead to collisions and long navigation times. Based on the above considerations, our study designs a USV path planning system that comprehensively considers the multimodal information from electronic nautical charts and meteorological data. The system consists of three parts: (1) image processing module, (2) meteorological analysis module, and (3) path planning module. In detail, the image processing module obtains the geographical feature information from the electronic chart and constructs a static obstacle environment. The meteorological analysis module obtains the meteorological feature information from meteorological data and constructs a dynamic meteorological vector field environment. The path planning module introduces a designed double deep Q-Network (DQN) structure, a multivariate weighted Dueling network, and a priority sampling mechanism to enhance the DQN algorithm for promising performance in USV path planning. Extensive experiments illustrate the superior performance of the proposed fusion DQN algorithm. Furthermore, the feasibility of the entire path planning system is confirmed. © 2014 IEEE.

关键词： Unmanned surface vehicles

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Optimization of a Piezoelectric Bending Beam-based Human Knee Energy Harvester 20

Optimization of a Piezoelectric Bending Beam-based Human Kne...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Liao, Haisu Wang, Yidi Shi, Qiuyu Huang, Ruisen Gao, Fei Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy System

ISBN: (纸本)9798350320831

The concept of extracting energy from the human body to recharge depleted energy sources has gained significant attention. Recently, scientists have focused on harnessing biomechanical energy from the human body while walking. The aim of this study is to optimize a piezoelectric bending beam-based human knee energy harvester to enhance performance. Specifically, the shape of the bending beam (the substrate: a carbon fiber plate) is optimized to modulate the deformation, such that the average strain of the piezoelectric macro fiber composites (MFC) attached to the bending beam is improved to generate more electricity. In addition, the effects of the substrate's thickness and the hinge base's height for the bending beam on the output power are also analyzed. Experimental testing on a subject is conducted to validate our design and test the performance. The harvester generates an average power output of 24.55 mW when walking at 4 km/h on a treadmill. The harvester's total weight is 278g. © 2023 IEEE.

关键词： Energy harvesting

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On Interval Perturbation of the α-Universal Triple I Algorithm for Unified Interval Implications 1

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3rd International Conference on Artificial intelligence Logic and Applications, AILA 2023

作者： Tang, Yiming Huang, Yifan Qi, Zhiyuan Zhang, Li Gao, Jianwei Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Hefei230601 China College of Overseas Education Nanjing Tech University Nanjing211816 China

ISBN: (数字)9789819978694

ISBN: (纸本)9789819978687

In the fuzzy field, Zadeh put forward the CRI (compositional rule of inference) algorithm to deal with fuzzy reasoning. Later, Wang put forward the triple I algorithm, which is logically more complete than CRI. Thenceforward, Tang found that the harsh conditions of the triple identical implication operators lead to low system performance, so the α-universal triple I (α-UTI) algorithm is proposed. In actual applications, the quality of fuzzy inference algorithm directly is determined by the robustness of inference algorithm. This study researches the interval robustness of the α-UTI algorithm under the interval-valued environment. The interval perturbation performance of the α-UTI algorithm is respectively revealed for the R-interval implication, the triangular modulus interval implication, the S-interval implication and the QL-interval implication. It is observed that the α-UTI algorithm has good interval perturbation performance for single rule or multi-rules, in which unified interval implications are employed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Fuzzy inference

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FodFoM: Fake Outlier Data by Foundation Models Creates Stronger Visual Out-of-Distribution Detector

arXiv

引用

arXiv 2024年

作者： Chen, Jiankang Deng, Ling Gan, Zhiyong Zheng, Wei-Shi Wang, Ruixuan School of Computer Science and Engineering Sun Yat-sen University Guangdong Guangzhou China Peng Cheng Laboratory Guangdong Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Guangdong Guangzhou China China United Network Communications Corporation Limited Guangdong Branch Guangdong Guangzhou China School of Computer Science and Engineering Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangdong Guangzhou China

Out-of-Distribution (OOD) detection is crucial when deploying machine learning models in open-world applications. The core challenge in OOD detection is mitigating the model’s overconfidence on OOD data. While recent methods using auxiliary outlier datasets or synthesizing outlier features have shown promising OOD detection performance, they are limited due to costly data collection or simplified assumptions. In this paper, we propose a novel OOD detection framework FodFoM that innovatively combines multiple foundation models to generate two types of challenging fake outlier images for classifier training. The first type is based on BLIP-2’s image captioning capability, CLIP’s vision-language knowledge, and Stable Diffusion’s image generation ability. Jointly utilizing these foundation models constructs fake outlier images which are semantically similar to but different from in-distribution (ID) images. For the second type, GroundingDINO’s object detection ability is utilized to help construct pure background images by blurring foreground ID objects in ID images. The proposed framework can be flexibly combined with multiple existing OOD detection methods. Extensive empirical evaluations show that image classifiers with the help of constructed fake images can more accurately differentiate real OOD images from ID ones. New state-of-the-art OOD detection performance is achieved on multiple benchmarks. The code is available at https://***/Cverchen/ACMMM2024-FodFoM. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

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Grasp Region Exploration for 7-DoF Robotic Grasping in Cluttered Scenes

Grasp Region Exploration for 7-DoF Robotic Grasping in Clutt...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Zibo Chen Zhixuan Liu Shangjin Xie Wei-Shi Zheng School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Robotic grasping is a fundamental skill for robots, but it is quite challenging in cluttered scenes. In cluttered scenes, the precise prediction of high-quality grasp configurations such as rotation and grasping width while avoiding collisions is essential. To accomplish this, the grasp detection models require the capabilities of stronger fine-grained information extracted around the grasp points. However, due to the computational resource restriction, point clouds are usually downsampled in existing networks, which inevitably make some potentially important points discarded. To overcome this problem, we propose a Grasp Region Exploration module to explore the area covered by high-quality grasps. Based on the grasp region, we enhance the point density around the grasp points to mitigate the loss of information caused by downsampling. Furthermore, we devise the Grasp Region Attention module to dynamically aggregate features of various points within the grasp region, such as the grasp point and contact points. The proposed method achieves state-of-the-art performance on the large-scale GraspNet-1Billion dataset. We also conduct real-world experiments on a Franka Emika Panda robot and show that the robot can grasp objects in cluttered scenes with a high success rate.

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Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis

arXiv

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arXiv 2024年

作者： Lu, Yanzuo Zhang, Manlin Ma, Andy J. Xie, Xiaohua Lai, Jianhuang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangzhou China

Diffusion model is a promising approach to image generation and has been employed for Pose-Guided Person Image Synthesis (PGPIS) with competitive performance. While existing methods simply align the person appearance to the target pose, they are prone to overfitting due to the lack of a high-level semantic understanding on the source person image. In this paper, we propose a novel Coarse-to-Fine Latent Diffusion (CFLD) method for PGPIS. In the absence of image-caption pairs and textual prompts, we develop a novel training paradigm purely based on images to control the generation process of a pre-trained text-to-image diffusion model. A perception-refined decoder is designed to progressively refine a set of learnable queries and extract semantic understanding of person images as a coarse-grained prompt. This allows for the decoupling of fine-grained appearance and pose information controls at different stages, and thus circumventing the potential overfitting problem. To generate more realistic texture details, a hybrid-granularity attention module is proposed to encode multi-scale fine-grained appearance features as bias terms to augment the coarse-grained prompt. Both quantitative and qualitative experimental results on the DeepFashion benchmark demonstrate the superiority of our method over the state of the arts for PGPIS. Code is available at https://***/YanzuoLu/CFLD. Copyright © 2024, The Authors. All rights reserved.

关键词： Textures

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Rethinking the membrane dynamics and optimization objectives of spiking neural networks 24

Rethinking the membrane dynamics and optimization objectives...

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

作者： Hangchi Shen Qian Zheng Huamin Wang Gang Pan College of Artificial Intelligence Southwest University and Chongqing Key Laboratory of Brain Inspired Computing and Intelligent Chips The State Key Lab of Brain-Machine Intelligence and College of Computer Science and Technology Zhejiang University

ISBN: (纸本)9798331314385

Despite spiking neural networks (SNNs) have demonstrated notable energy efficiency across various fields, the limited firing patterns of spiking neurons within fixed time steps restrict the expression of information, which impedes further improvement of SNN performance. In addition, current implementations of SNNs typically consider the firing rate or average membrane potential of the last layer as the output, lacking exploration of other possibilities. In this paper, we identify that the limited spike patterns of spiking neurons stem from the initial membrane potential (IMP), which is set to 0. By adjusting the IMP, the spiking neurons can generate additional firing patterns and pattern mappings. Furthermore, we find that in static tasks, the accuracy of SNNs at each time step increases as the membrane potential evolves from zero. This observation inspires us to propose a learnable IMP, which can accelerate the evolution of membrane potential and enables higher performance within a limited number of time steps. Additionally, we introduce the last time step (LTS) approach to accelerate convergence in static tasks, and we propose a label smooth temporal efficient training (TET) loss to mitigate the conflicts between optimization objective and regularization term in the vanilla TET. Our methods improve the accuracy by 4.05% on ImageNet compared to baseline and achieve state-of-the-art performance of 87.80% on CIFAR10-DVS and 87.86% on N-Caltech101. The code is available at https://***/StephenTaylor1998/IMP-SNN.

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Chalcogenide integrated nanophotonics and its potential for quantum photonics

Chalcogenide integrated nanophotonics and its potential for ...

引用

2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and Optical Communications, IPOC 2024

作者： Lei, Kunhao Xu, Kai Bao, Kangjian Weng, Jiawei Li, Lan Lin, Hongtao The State Key Lab of Brain-Machine Intelligence Key Laboratory of Micro-Nano Electronics and Smart System of Zhejiang Province College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China The Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province School of Engineering Westlake University Zhejiang Hangzhou310030 China Institute of Advanced Technology Westlake Institute for Advanced Study Zhejiang Hangzhou310024 China

Due to the high optical nonlinearity and nonvolatile phase-changing properties, chalcogenide integrated nanophotonics are emerging for entangling photon generation and nonvolatile quantum states operation. We will rev... 详细信息

ISBN: (纸本)9798350379266

关键词： Silicon photonics

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Consistent Prompting for Rehearsal-Free Continual Learning

Consistent Prompting for Rehearsal-Free Continual Learning

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhanxin Gao Jun Cen Xiaobin Chang School of Artificial Intelligence Sun Yat-sen University China Cheng Kar-Shun Robotics Institute The Hong Kong University of Science and Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Continual learning empowers models to adapt autonomously to the ever-changing environment or data streams without forgetting old knowledge. Prompt-based approaches are built on frozen pre-trained models to learn the task-specific prompts and classifiers efficiently. Existing prompt-based methods are inconsistent between training and testing, limiting their effectiveness. Two types of inconsistency are revealed. Test predictions are made from all classifiers while training only focuses on the current task classifier without holistic alignment, leading to Classifier inconsistency. Prompt inconsistency indicates that the prompt selected during testing may not correspond to the one associated with this task during training. In this paper, we propose a novel prompt-based method, Consistent Prompting (CPrompt), for more aligned training and testing. Specifically, all existing classifiers are exposed to prompt training, resulting in classifier consistency learning. In addition, prompt consistency learning is proposed to enhance prediction robustness and boost prompt selection accuracy. Our Consistent Prompting surpasses its prompt-based counterparts and achieves state-of-the-art performance on multiple continual learning benchmarks. Detailed analysis shows that improvements come from more consistent training and testing. Our code is available at https://***/Zhanxin-Gao/CPrompt.

关键词： Training Continuing education Adaptation models Computer vision Accuracy Limiting Robustness

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