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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Dong, Pengfei Hai, Renwei Shen, Yanqing Zhu, Ziyu Chen, Shitao Xin, Jingmin Zheng, Nanning Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Shaanxi Xi'an710049 China Shunan Academy of Artificial Intelligence Zhejiang Ningbo China

ISBN: (纸本)9798331505929

In recent years, event cameras, as a type of bio-inspired sensor, have become increasingly important in the fields of robotics and autonomous driving. Given that events inherently lack depth information, a multi-sensor solution is applied to combine events and LiDAR scans, and its performance highly depends on precise calibration between different sensors. However, due to the distinctive representation of event cameras, which only capture dynamic objects without texture structure, most previous calibration methods for event cameras have complex setups and controlled lighting conditions. In this paper, we propose a novel extrinsic calibration method by simulating events with LiDAR, which does not require any equipment beyond the sensors themselves. Specifically, we first extract LiDAR events from the continuous raw point clouds. Then a clustering algorithm is applied to refine this event data and identify motion centroids. Finally, a two-step optimization process is proposed: an initial rough calibration based on these centroids and a subsequent fine calibration achieved through nearest neighbor matching between LiDAR events and camera events. The experimental results validate that our algorithm achieves high accuracy and robustness in simulations and real-world experiments. © 2024 IEEE.

关键词： Nearest neighbor search

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Improved Capon Estimator for High-Resolution DOA Estimation and Its Statistical Analysis

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1716-1729页

作者： Weiliang Zuo Jingmin Xin Changnong Liu Nanning Zheng Akira Sano IEEE the National Key Laboratory of Human-Machine Hybrid Augmented Intelligence the National Engineering Research Center for Visual Information and Applicationsand the Institute of Artificial Intelligence and RoboticsXi’an Jiaotong UniversityXi’an 710049China the Data Center China Construction BankBeijing 100033China the Department of System Design Engineering Keio UniversityYokohama 223-8522Japan

Despite some efforts and attempts have been made to improve the direction-of-arrival(DOA)estimation performance of the standard Capon beamformer(SCB)in array processing,rigorous statistical performance analyses of these modified Capon estimators are still *** paper studies an improved Capon estimator(ICE)for estimating the DOAs of multiple uncorrelated narrowband signals,where the higherorder inverse(sample)array covariance matrix is used in the Capon-like cost *** establishing the relationship between this nonparametric estimator and the parametric and classic subspace-based MUSIC(multiple signal classification),it is clarified that as long as the power order of the inverse covariance matrix is increased to reduce the influence of signal subspace components in the ICE,the estimation performance of the ICE becomes equivalent to that of the MUSIC regardless of the signal-to-noise ratio(SNR).Furthermore the statistical performance of the ICE is analyzed,and the large-sample mean-squared-error(MSE)expression of the estimated DOA is *** the effectiveness and the theoretical analysis of the ICE are substantiated through numerical examples,where the Cramer-Rao lower bound(CRB)is used to evaluate the validity of the derived asymptotic MSE expression.

关键词： Capon beamformer direction-of-arrival(DOA)estimation large-sample mean-squared-error(MSE) subspace-based methods uniform linear array

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Fake Face Detection Based on Fusion of Spatial Texture and High-Frequency Noise

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Chinese Journal of Electronics 2025年第1期34卷 212-221页

作者： Dengyong Zhang Feifan Qi Jiahao Chen Jiaxin Chen Rongrong Gong Yuehong Tian Lebing Zhang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication Engineering Changsha University of Science and Technology School of Computer and Communication Engineering Changsha University of Science and Technology Changsha Social Work College Changkuangao Beijing Technology Co. Ltd. School of Computer and Artificial Intelligence Huaihua University

The rapid development of the Internet has led to the widespread dissemination of manipulated facial images, significantly impacting people's daily lives. With the continuous advancement of Deepfake technology, the generated counterfeit facial images have become increasingly challenging to distinguish. There is an urgent need for a more robust and convincing detection method. Current detection methods mainly operate in the spatial domain and transform the spatial domain into other domains for analysis. With the emergence of transformers, some researchers have also combined traditional convolutional networks with transformers for detection. This paper explores the artifacts left by Deepfakes in various domains and, based on this exploration, proposes a detection method that utilizes the steganalysis rich model to extract high-frequency noise to complement spatial features. We have designed two main modules to fully leverage the interaction between these two aspects based on traditional convolutional neural networks. The first is the multi-scale mixed feature attention module, which introduces artifacts from high-frequency noise into spatial textures, thereby enhancing the model's learning of spatial texture features. The second is the multi-scale channel attention module, which reduces the impact of background noise by weighting the features. Our proposed method was experimentally evaluated on mainstream datasets, and a significant amount of experimental results demonstrate the effectiveness of our approach in detecting Deepfake forged faces, outperforming the majority of existing methods.

关键词： Deepfakes Adaptation models Attention mechanisms Noise Transforms Transformers Feature extraction Internet Convolutional neural networks Faces

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LSTM with AhI-model based Kalman Filter for Lithium-ion Battery State of Charge Estimation

LSTM with AhI-model based Kalman Filter for Lithium-ion Batt...

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2024 China Automation Congress, CAC 2024

作者： Luo, Xiangyong Dang, Lujuan Yang, Meng Chen, Badong National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China

ISBN: (纸本)9798350368604

Accurate state of charge (SOC) estimation of lithium-ion battery in battery management system (BMS) is important for the safety and reliability of electronic devices and electric vehicles. This paper proposes a LSTM-extended Kalman filter based on AhI-model (LSTM-EA), which constrains SOC result from LSTM with an AhI-model based Kalman filter. AhI-model is based on Ampere-hour integration method and requires simpler structure to obtain estimated SOC with higher accuracy and robustness. In the experiments, the public datasets from the Center for Advanced Life Cycle engineering (CALCE) are used to validate the SOC estimation performance. The mean absolute error (MAE) and maximum error of the proposed method are less than 2.51% and 8.82% respectively. © 2024 IEEE.

关键词： State of charge

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Multi-grained Correspondence Learning of Audio-language Models for Few-shot Audio Recognition 24

Multi-grained Correspondence Learning of Audio-language Mode...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhao, Shengwei Xu, Linhai Liu, Yuying Du, Shaoyi National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Shaanxi Xi'an China

ISBN: (纸本)9798400706868

Large-scale pre-trained audio-language models excel in general multi-modal representation, facilitating their adaptation to downstream audio recognition tasks in a data-efficient manner. However, existing few-shot audio recognition methods based on audio-language models primarily focus on learning coarse-grained correlations, which are not sufficient to capture the intricate matching patterns between the multi-level information of audio and the diverse characteristics of category concepts. To address this gap, we propose multi-grained correspondence learning for bootstrapping audio-language models to improve audio recognition with few training samples. This approach leverages generative models to enrich multi-modal representation learning, mining the multi-level information of audio alongside the diverse characteristics of category concepts. Multi-grained matching patterns are then established through multi-grained key-value cache and multi-grained cross-modal contrast, enhancing the alignment between audio and category concepts. Additionally, we incorporate optimal transport to tackle temporal misalignment and semantic intersection issues in fine-grained correspondence learning, enabling flexible fine-grained matching. Our method achieves state-of-the-art results on multiple benchmark datasets for few-shot audio recognition, with comprehensive ablation experiments validating its effectiveness. © 2024 ACM.

关键词： Semantics

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General Planning Method for Humanoid Collaborative Robots using Large Language Models

General Planning Method for Humanoid Collaborative Robots us...

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2024 China Automation Congress, CAC 2024

作者： Gao, Runqin Shi, Jiamin Tian, Shu Chen, Shitao National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China

ISBN: (纸本)9798350368604

Humanoid robots, capable of simulating human behavior and decision-making, have garnered extensive attention due to their anthropomorphic structures and versatile functional designs. Traditionally, actions and high-level decision-making in robots have been manually achieved by developers, requiring modifications and adaptations for different task requirements, thus lacking autonomous decision-making capabilities. With the rapid advancement of deep learning technologies, large language models trained on internet data have provided new solutions for robot intelligence, demonstrating effective applications in high-level task planning for robots. Based on this, we propose a general high-level task planning method using large language models, implemented on a mobile single-arm humanoid robot as the platform. This method consists of two core components: high-level decision planning and low-level action execution, coupled through the robot’s atomic action interface. The robot autonomously completes sequential action planning by receiving natural language task instructions and executes long-term, complex, general indoor collaborative tasks. By deploying on a real robot, we conducted sub-module tests and overall system performance tests, successfully achieving the process of sorting and organizing tasks in an indoor experimental environment. © 2024 IEEE.

关键词： Anthropomorphic robots

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Semantic-Aware Late-Stage Supervised Contrastive Learning for Fine-Grained Action Recognition

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5912-5924页

作者： Pan, Yijun Zhao, Quan Zhang, Yueyi Wang, Zilei Sun, Xiaoyan Wu, Feng University of Science and Technology of China School of Artificial Intelligence and Data Science Hefei230026 China University of Science and Technology of China School of Information Science and Technology Hefei230026 China University of Science and Technology of China National Engineering Laboratory for Brain-Inspired Intelligence Technology and Application Hefei230026 China

Fine-grained action recognition typically faces challenges with lower inter-class variances and higher intra-class variances. Supervised contrastive learning is inherently suitable for this task, as it can decrease intra-class feature distances while increasing inter-class ones. However, directly applying it into fine-grained action recognition encounters two main problems. The first problem stems from the heavy training cost associated with supervised contrastive learning, which requires numerous training epochs, each involving double augmentation views per instance. To address this issue, we propose the late-stage supervised contrastive learning (late-SC) strategy, which effectively reduces the number of training epochs needed for the contrastive learning process. The second problem is that supervised contrastive loss does not explicitly consider the semantic distances between fine-grained actions when adjusting representation distances. This results in less reasonable and efficient adjustments to the representation space. To overcome this limitation, we introduce the semantic-aware temperature adaptation (STA) mechanism, enhancing the suitability of the supervised contrastive loss for fine-grained action recognition. We conduct experiments on several benchmark datasets for fine-grained action recognition, including Epic-Kitchens-55/100, SomethingSomething-V1, and Diving48-V2. The results demonstrate that our proposed method (referred to as LSC-STA) consistently enhances performance across various base feature extractors, without introducing additional inference overhead and incurring only a marginal increase in training expenses. © 1991-2012 IEEE.

关键词： Contrastive Learning

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Socially-Compatible Motion Planning of Autonomous Vehicles with Verification on Real Human data 27

Socially-Compatible Motion Planning of Autonomous Vehicles w...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Zhang, Chenxi Chen, Shitao Shi, Jiamin Xin, Jingmin Zheng, Nanning National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Shaanxi Xi'an710049 China

ISBN: (纸本)9798331505929

One of the fundamental challenges for autonomous vehicles in complex dynamic scenarios is planning human-like trajectories in a socially acceptable manner. Existing methods, focused solely on self-interest and implicitly modeling a singular driving style, prove inadequate for the deployment of autonomous vehicles in mixed traffic environments while maintaining social acceptability. This paper presents the Socially-Compatible Motion Planning (SCMP) framework, which dynamically captures the spatiotemporal interaction characteristics of traffic participants for various driving tasks and adaptively adjusts the driving style of autonomous vehicles. Guided by driving style, the motion planning module considers vehicle physical constraints, safety constraints and comfort constraints, thereby generating human-like trajectories that incorporate multiple driving styles. The proposed method was rigorously validated and evaluated using the real-world dataset nuPlan, where it demonstrated exceptional performance in various driving scenarios and tasks. © 2024 IEEE.

关键词： Motion planning

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EFormer-VPR: Fusing Events and Frames with Transformer for Visual Place Recognition 27

EFormer-VPR: Fusing Events and Frames with Transformer for V...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Zhang, Yangjing Shen, Yanqing Zhu, Ziyu Hai, Renwei Chen, Shitao Zheng, Nanning National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Shaanxi Xi'an710049 China

ISBN: (纸本)9798331505929

Visual place recognition (VPR) is a challenging task faced by mobile robots and autonomous driving systems. In scenarios with glare or high-speed motion, image blurring makes it difficult for traditional cameras to perform reliable and accurate place recognition. In contrast, event cameras can capture target motion information without blur in high-speed motion scenes, but lack texture information in low-speed motion scenes. To leverage the complementary characteristics of these two sensors and improve the performance and robustness of VPR algorithms, we propose EFormer- VPR, which fuses target motion events and frames with transformer. This method firstly preprocesses the events stream within an adaptive time window using a clustering method, then uses transformer-based networks to extract features from motion frames and events separately and fuse them through a scoring module. Finally, features are aggregated using a VLAD layer and the whole pipeline is supervised by a triplet ranking loss. To verify the effectiveness of the proposed algorithm, we compare it with other VPR methods on the event-based driving datasets (Brisbane-Event-VPR, NeuroGPR) with challenging scenarios. Experimental results show that on the Brisbane- Event- Vprand NeuroGPR datasets, our method achieves state-of-the-art performance. © 2024 IEEE.

关键词： Mobile robots

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Refiner: Fine-grained Cross-modal Concepts Refinement for Compositional Zero-Shot Learning

Refiner: Fine-grained Cross-modal Concepts Refinement for Co...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Xiao Jing, Haodong Chen, Hui Ma, Yongqiang Zheng, Nanning National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Shaanxi China

ISBN: (纸本)9798350368741

Recent Compositional Zero-Shot Learning (CZSL) methods increasingly adopt the pre-trained vision-language models to capture the contextual relations between image and text spaces. However, the single-class-token design from Transformer-based encoder inevitably captures contextual information from unrelated objects and background, thus hindering the modeling of fine-grained class-specific visual features. Suffering from cross-modal gap, prior methods also struggle to improve compositional recognition performance. To address these issues, we propose a fine-grained cross-modal concepts refinement framework, termed as Refiner, which comprises two pivotal components: (i) the fine-grained concepts refinement of image embeddings to capture state-object context within visual scenes, and (ii) the cross-modal information fusion to mitigate the modality gap. By leveraging learnable query vectors to capture region-specific semantic information pertinent to composition labels, our approach refines visual representations with fine-grained state-object context information. As for cross-modal information fusion, we construct a robust image-to-text mapping by aligning visual embeddings with states, objects, and compositions, respectively. Extensive experiments demonstrate that our Refiner achieves new state-of-the-art performance across all popular benchmarks in both closed- and open-world settings. © 2025 IEEE.

关键词： Compositional Zero-shot Learning Cross-Modal Fusion Fine-Grained Refinement Multimodal Models

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