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检索条件"任意字段=International Conference on Robot Vision and Signal Processing"

共 18110 条记录，以下是241-250 订阅

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Development of a Miniature Self-Driving Car Using SoC and NPU

Development of a Miniature Self-Driving Car Using SoC and NP...

引用

IEEE international conference on Consumer Electronics (ICCE)

作者： Akira Kojima Department of Computer and Network Engineering Hiroshima City University Hiroshima City Japan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

For the ICCE2025 design contest, we are developing a miniature self-driving car system using Raspberry Pi5 and Hailo-8L Neural processing Unit (NPU). Previously, we had implemented the object detection part on AMD/Xilinx FPGA using DPU softcore IP. Now the hardcore NPU chip dedicated to neural network processing is available, we are implementing the object detection part using the NPU and building our system. We compare the performance of the NPU implementation with that of the previous FPGA implementation.

关键词： Neural networks robot vision systems Buildings Object detection Cameras Data models Autonomous automobiles IP networks Field programmable gate arrays Consumer electronics

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RWKVMatch: vision RWKV-based Multi-scale Feature Matching Network for Unsupervised Deformable Medical Image Registration

RWKVMatch: Vision RWKV-based Multi-scale Feature Matching Ne...

引用

international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zixuan He Jing Tang Zitong Zhao Zeyu Gong School of Future Technology Huazhong University of Science and Technology Wuhan School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Medical image registration is essential for integrating information from diverse imaging modalities for clinical diagnosis and treatment planning. Despite significant advancements, achieving efficient and precise deformable image registration remains a formidable challenge. In this study, we propose a novel medical image registration model, RWKVMatch, which employs global attention and cross-fusion mechanism based on the vision-RWKV module to address complex deformations in medical images effectively. Additionally, the elastic transformation from data augmentation techniques is integrated into the model architecture to enhance its capability to handle multi-scale features and improve robustness to geometric variations in image registration. Experimental evaluations on two medical image registration datasets indicate the effectiveness of our approach, surpassing existing state-of-the-art methods in terms of registration accuracy and computational efficiency. These findings underscore the potential of RWKVMatch as a highly effective tool for medical image registration.

关键词： Deformable models Image registration Deformation Computational modeling signal processing Robustness Data models Planning Speech processing Medical diagnostic imaging

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KABON: Knowledge Aggregation with vision-Language Model for Black-Box Open-Set Domain Adaptation

KABON: Knowledge Aggregation with Vision-Language Model for ...

引用

international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zhixin Zeng Yusen Zhang Ji Wang College of Computer Science and Technology National University of Defense Technology

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In this paper, we aim to tackle the challenging Black-Box Open-Set Domain Adaptation (BB-OSDA) task. BB-OSDA enables conducting Open-Set Domain Adaptation (OSDA) with solely a black-box source model, broadening the application scope of OSDA. Inspired by the significant success of pre-trained large vision-language (ViL) models in various applications, we propose a novel method, termed Knowledge Aggregation for Black-box Open-set domain adaptatioN (KABON), which leverages the power of ViL models to solve the BB-OSDA problem. Specifically, we first devise a novel knowledge aggregation approach to harness both the generic knowledge from the ViL model and the task-specific knowledge from the black-box source model. Subsequently, we utilize a Gaussian Mixture Model (GMM) with entropy criterion to divide samples of target domain into shared or novel classes. Furthermore, a self-correction strategy is proposed to refine the division of shared and novel classes. Finally, we leverage the divided samples through entropy minmax learning to simultaneously achieve shared classes adaptation and novel classes detection. Experiments conducted on multiple benchmark datasets demonstrate the effectiveness of our proposed method.

关键词： Adaptation models Closed box signal processing Benchmark testing Entropy Data models Acoustics Speech processing Gaussian mixture model

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Leveraging Visual Captions for Enhanced Zero-Shot HOI Detection

Leveraging Visual Captions for Enhanced Zero-Shot HOI Detect...

引用

international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yanqing Zeng Yunyao Mao Zhenbo Lu Wengang Zhou Houqiang Li EEIS Department University of Science and Technology of China Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Zero-shot Human-Object Interaction (HOI) detection aims to identify both seen and unseen HOI categories in an image. Most existing methods rely on semantic knowledge distilled from CLIP to find novel interactions but fail to fully exploit the powerful generalization ability of vision-language models, leading to impaired transferability. In this paper, we introduce a novel framework for zero-shot HOI detection. We first utilize vision-language models (VLMs) to generate visual captions from multiple perspectives, including humans, objects, and environments, to enhance interaction understanding. Then, we propose a multi-modal fusion encoder to fully leverage these visual captions. Additionally, to equip the HOI detector with a thorough consideration of contextual information in the image, we design a novel multi-branch HOI network that aggregates features at the instance, union, and global levels. Experiments on prevalent benchmarks demonstrate that our model achieves promising performance under a variety of zero-shot settings. The source codes are available at https://***/aqingcv/VC-HOI.

关键词： Visualization Aggregates Source coding Semantics Detectors Benchmark testing signal processing Robustness Speech processing Knowledge transfer

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Efficient Co-Approximate Parallel Compressive Depth Reconstruction on FPGA

Efficient Co-Approximate Parallel Compressive Depth Reconstr...

引用

international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yun Wu John McAllister School of Electrical Electronic and Computer Engineering Queen’s University Belfast

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Efficient depth image reconstruction from sparse samples is crucial for machine perception applications, such as robotics, vehicle assistance and autonomy. It demands fast processing speed with low power consumption for sensing quality and safety, as well as cost reduction for FPGA and solid state implementations, within constrained resource budgets on edge devices. A new co-approximate framework of parallel approximate compressive depth reconstruction engine on FPGA is proposed using ℓ 1 solvers, proximal gradient decent (PGD), with instrumented frequency and voltage scaling during the iterative optimization process. By evaluating various number of parallel approximate processing units for the depth image reconstruction engine, up to 51% further power saving is achieved, and 421× speed up of parallel processing compared to the baseline, henceforth the efficiency is elevated over 43×.

关键词： Image coding Power demand Instruments Voltage robot sensing systems Throughput Sensors Image reconstruction Field programmable gate arrays Engines

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Robust CLIP-Guided Deep Thinking: A Two-Stage Optimization Strategy for Enhancing Adversarial Robustness and Reliability in LVLMs

Robust CLIP-Guided Deep Thinking: A Two-Stage Optimization S...

引用

international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yize Sui Wanrong Huang Wenjing Yang Chaofan Zhao Jing Ren Ji Wang College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Large vision-Language models (LVLMs) have demonstrated remarkable performance in a wide range of vision-language tasks as an efficient input/output system. However, the lack of adversarial robustness at the input side and the widespread hallucination phenomenon at the output side significantly undermine user trust in them. Current solutions to the former tend to sacrifice the general performance of LVLMs, while solving the latter requires a large amount of engineering costs. To address these challenges, we propose a two-stage optimization strategy called RCDT (Robust CLIP-guided Deep Thinking), which aims to enhance the adversarial robustness of LVLMs with minimal general performance loss while reducing hallucinations. First, we introduce a constrained adversarial fine-tuning approach for CLIP to limit the general performance loss during the enhancement of robustness. Furthermore, this CLIP is used to think deeply about the output process of LVLMs to reduce hallucinations. Experiments show that RCDT not only reduce general performance loss by more than half while maintaining adversarial robustness compared to the baselines, but also demonstrate good performance in mitigating hallucinations.

关键词： Costs Pipelines signal processing Robustness Acoustics Speech processing Optimization

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Image Captioning Using Scene Graph Generation

Image Captioning Using Scene Graph Generation

引用

international conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Shiny S Abisheak A Balaji P Rohit K Department of Artificial Intelligence Mepco Schlenk Engineering College Sivakasi India

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

Understanding and generating natural language descriptions from images is a fundamental challenge in vision-language tasks within artificial intelligence. This paper introduces a novel image captioning framework that integrates scene graph generation to improve the semantic richness of generated captions. The proposed method employs the Relation Transformer (RelTR) model to extract structural representations from visual scenes in the form of subject-predicate-object triplets. A transformer-based captioning model then utilizes these structured scene graphs to produce fluent and contextually accurate captions. Experimental evaluations on the Visual Genome dataset demonstrate that our approach yields superior semantic coherence and captioning accuracy compared to traditional image-to-text models. The incorporation of relational scene understanding results in captions that are more contextually informed and descriptive.

关键词： Wireless communication Visualization Accuracy Semantics Genomics signal processing Transformers SPICE Bioinformatics Context modeling

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Improving Multimodal Human Pose Estimation by Adversarial Modality Enhancement†

Improving Multimodal Human Pose Estimation by Adversarial Mo...

引用

international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jiangnan Xia Qilong Wu Yanyin Guo Yi Li Jianghan Cheng Junwei Li Zhiyuan Zhang College of Information Science and Electronic Engineering Zhejiang University China School of Computing and Information Systems Singapore Management University Singapore NingboTech University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Human pose estimation in computer vision predominantly focuses on the visible modality, with limited research on the infrared modality. No existing methods demonstrate robust performance across both modalities, missing their complementary strengths. This gap arises from the lack of a multimodal benchmark and the difficulty of developing robust multimodal capabilities. To address this, we introduce MMPD, a novel visible-infrared multimodal pose benchmark with high-quality annotations for both modalities. Leveraging MMPD, we expose the limitations of state-of-the-art methods due to modality variance. To overcome this challenge, we propose a novel method-agnostic scheme called AMMPE. By employing the Modality Adversarial Enhancement Stage and Modality Interaction Stage, AMMPE easily incorporates multimodal information without additional pose annotations and enhances effective modality interaction. Extensive experiments demonstrate that AMMPE improves performance in both visible and infrared modalities, achieving excellent modality robustness. The code and benchmark is avaible at: https://***/ICANDOALLTHINGSSS/Adversarial-Multi-Modality-Pose-Estimation

关键词： Degradation Computer vision Codes Annotations Pose estimation Benchmark testing signal processing Robustness Acoustics Speech processing

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DUNE: Sim2Real Transfer for Depth-based Navigation in Unstructured Dynamic Indoor Environments

DUNE: Sim2Real Transfer for Depth-based Navigation in Unstru...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Chaoyi Xu Wen Liu Jilong Wang Lei Ma Fan Yin Zhongliang Deng School of Electronic Engineering Beijing University of Posts and Telecommunications China Jack Baskin School of Engineering University of California Santa Cruz Santa Cruz CA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Collision-free navigation in dynamic environments, especially with moving pedestrians, is crucial for mobile robots. This paper introduces DUNE, a depth-based policy trained in simulation for collision-free navigation of Ackermann mobile robots in unstructured indoor environments. DUNE uses a CNN-LSTM to encode depth vision and past actions, while an actor-critic network controls velocity and steering. Additionally, a depth filter bridges the sim-to-real gap. Unlike prior works relying on LIDAR and complex algorithms, DUNE achieves state-of-the-art performance with only egocentric depth perception and lightweight neural networks, both in simulation and real-world tasks.

关键词： Pedestrians Laser radar Navigation Heuristic algorithms Neural networks signal processing algorithms signal processing Indoor environment Mobile robots Speech processing

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Bayesian Nonparametric Clustering for Source Counting with a Small Aperture Microphone Array

Bayesian Nonparametric Clustering for Source Counting with a...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Kunkun SongGong Pufen Zhang Xiongwei Zhang Wenwu Wang Meng Sun Chong Jia Yihao Li Army Engineering University China Centre for Vision Speech and Signal Processing University of Surrey U.K

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Source counting (SC) in an indoor environment is an important problem in computational auditory scene analysis. However, the problem is challenging, especially when reverberation and ambient noise are present in the environment. To address this problem, we propose an augmented Bayesian non-parametric (ABNP) clustering algorithm for source counting based on sound intensity (SI) captured by a small aperture microphone array. The core idea is to incorporate an infinite Gaussian mixture model (IGMM) and a time-frequency (TF) augmented weight selection and update scheme for sound intensity estimation. The use of IGMM enables the exemption of the maximum number of sources assumed in previous methods. Experiments on both simulated and real-world data show the improved performance by the proposed method as compared with the state of the art baseline methods.

关键词： Time-frequency analysis Array signal processing signal processing algorithms Clustering algorithms Apertures Microphone arrays Bayes methods Arrays Reverberation Speech processing

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