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共 18111 条记录，以下是241-250 订阅

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Keypoint Aware Masked Image Modelling

Keypoint Aware Masked Image Modelling

引用

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

作者： Madhava Krishna A. V. Subramanyam Department of CSE IIIT Delhi India Department of ECE IIIT Delhi India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

SimMIM is a widely used method for pretraining vision transformers using masked image modeling. However, despite its success in fine-tuning performance, it has been shown to perform sub-optimally when used for linear probing. We propose an efficient patch-wise weighting derived from keypoint features which captures the local information and provides better context during SimMIM’s reconstruction phase. Our method, KAMIM, improves the top-1 linear probing accuracy from 16.12% to 33.97%, and finetuning accuracy from 76.78% to 77.3% when tested on ImageNet-1K dataset with a ViT-B trained for the same number of epochs while taking the same wall-clock-time. We conduct extensive testing on different datasets, keypoint extractors, and model architectures and observe that patch-wise weighting augments linear probing performance for larger pretraining datasets. We also analyze the learned representations of a ViT-B trained using KAMIM and observe that they behave similar to contrastive learning with longer attention distances. Our code is available at https://***/madhava20217/KAMIM.

关键词： Training Computer vision Accuracy Contrastive learning Detectors signal processing Transformers Speech processing Image reconstruction Testing

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Point Cloud Registration via Reconstruction with Local Geometry Information Aggregation

Point Cloud Registration via Reconstruction with Local Geome...

引用

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

作者： Zewei Pan Linsen Li Tongxin Yuan Jiarong Yang School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Technology Shandong University Qingdao China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Point cloud registration is a fundamental yet challenging task in computer vision and robotics. While framing it as a reconstruction problem has shown promise, traditional reconstruction approaches rely on positional encodings to encode positional information, which inadequately capture the intricate geometric and positional relationships between point cloud pairs, leading to suboptimal registration results. To overcome this limitation, we introduce a Local Geometry Information Aggregation (LGIA) module that effectively captures both global positional context and fine-grained local geometric details. Furthermore, to better utilize the geometric and positional information inherent in point clouds, we propose a salient point sampling strategy that increases the proportion during the downsampling process. Subsequently, we assign these salient points higher weights during patch matching. Experiments demonstrate the superiority of our method on 3DMatch, 3DLoMatch and KITTI datasets, which achieves SOTA results at three metrics of FRM, RRT and RTE, and especially 98.5% FRM on the 3DMatch dataset.

关键词： Point cloud compression Geometry Measurement Computer vision signal processing Robustness Encoding Acoustics Computational efficiency 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...

引用

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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MST-HA: Multi-Modal signal Fusion with Bayesian Optimization for Robust Industrial robot Joint Health Assessment

MST-HA: Multi-Modal Signal Fusion with Bayesian Optimization...

引用

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

作者： Haoyu Wang Zilong Yin Bin Chen Xiyue Yan Chenyu Zhou Beibei Zhang Xinyuan Li Guangmeng Xue Haichao Xu University of Shanghai for Science and Technology Shanghai China Shanghai University of Engineering Science Shanghai China Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper presents a novel multi-modal deep learning framework for industrial robot joint health assessment and prediction, leveraging non-invasive signal fusion and Bayesian optimization. The proposed method addresses the challenges of comprehensive joint state monitoring in complex industrial environments without disrupting normal operations. We integrate Hall-effect current sensors, external accelerometers, and joint encoders to collect multi-modal data, including motor currents, vibrations, and kinematic information. A novel Adaptive Multi-Receptive Field Attention Network (AMRFAN) is employed to extract features from each modality, while a synchrosqueezing transform (SST) is utilized to capture time-frequency characteristics. An attention mechanism dynamically adjusts the weights of different modalities, and a bidirectional long short-term memory (BiLSTM) network models the temporal dependencies in the fused features. To enhance model performance and generalization, we implement a Bayesian optimization framework for hyperparameter tuning. Furthermore, we incorporate a Bayesian neural network to quantify prediction uncertainties, providing reliability metrics for decision-making processes. Experimental results on a four-axis industrial robot demonstrate that our framework achieves a 98.3% accuracy in joint health state classification and a mean absolute error of 4.2 in remaining useful life prediction, outperforming state-of-the-art single-modality methods. The proposed approach offers a robust, adaptable solution for real-time health monitoring and predictive maintenance of industrial robot joints, potentially improving manufacturing efficiency and reliability.

关键词： Vibrations Uncertainty Industrial robots Feature extraction Motors Real-time systems Bayes methods Optimization Monitoring Predictive maintenance

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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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UAV-VLA: vision-Language-Action System for Large Scale Aerial Mission Generation 25

UAV-VLA: Vision-Language-Action System for Large Scale Aeria...

引用

ACM/IEEE international conference on Human-robot Interaction (HRI)

作者： Oleg Sautenkov Yasheerah Yaqoot Artem Lykov Muhammad Ahsan Mustafa Grik Tadevosyan Aibek Akhmetkazy Miguel Altamirano Cabrera Mikhail Martynov Sausar Karaf Dzmitry Tsetserukou Skoltech Moscow Russia

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

The UAV-VLA (Visual-Language-Action) system is a tool designed to facilitate communication with aerial robots. By integrating satellite imagery processing with the Visual Language Model (VLM) and the powerful capabilities of GPT, UAV-VLA enables users to generate general flight paths-and-action plans through simple text requests. This system leverages the rich contextual information provided by satellite images, allowing for enhanced decision-making and mission planning. The combination of visual analysis by VLM and natural language processing by GPT can provide the user with the path-and-action set, making aerial operations more efficient and accessible. The newly developed method showed the difference in the length of the created trajectory in 22% and the mean error in finding the objects of interest on a map in 34.22 m by Euclidean distance in the K-Nearest Neighbors (KNN) approach. Additionally, the UAV-VLA system generates all flight plans in just 5 minutes and 24 seconds, making it 6.5 times faster than an experienced human operator. The code is available here: https://***/sautenich/uav-vla

关键词： Visualization Navigation Human-robot interaction Nearest neighbor methods Autonomous aerial vehicles Natural language processing Satellite images Planning Trajectory robots

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Leveraging Multimodal Methods and Spontaneous Speech for Alzheimer’s Disease Identification

Leveraging Multimodal Methods and Spontaneous Speech for Alz...

引用

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

作者： Yifan Gao Long Guo Hong Liu College of Computer Science National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Chengdu China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Cognitive impairment detection through spontaneous speech is a promising avenue for early diagnosis of Alzheimer’s disease (AD) and mild cognitive impairment (MCI), where timely intervention can significantly improve patient outcomes. The PROCESS Grand Challenge at ICASSP 2025 addresses these tasks by promoting innovative classification and regression methods for detecting cognitive decline. In this paper, we propose a multimodal fusion strategy that combines interpretable linguistic features with temporal embeddings extracted from pretrained models. Our approach achieves an F1-score of 0.649 for the classification task (predicting healthy, MCI, dementia) and an RMSE of 2.628 for the regression task (MMSE score prediction), securing the top overall ranking in the competition.

关键词： Technological innovation Linguistics signal processing Feature extraction Acoustics Alzheimer's disease Speech processing

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Contrastive Learning via Randomly Generated Deep Supervision

Contrastive Learning via Randomly Generated Deep Supervision

引用

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

作者： Shibo Wang Zili Ma Ka-Hou Chan Yue Liu Tong Tong Qinquan Gao Guangtao Zhai Xiaohong Liu Tao Tan Faculty of Applied Sciences Macao Polytechnic University China Ischemaview Inc Menlo Park CA USA College of Physics and Information Engineering Fuzhou University Fuzhou China John Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Unsupervised visual representation learning has gained significant attention in the computer vision community, driven by recent advancements in contrastive learning. Most existing contrastive learning frameworks rely on instance discrimination as a pretext task, treating each instance as a distinct category. However, this often leads to intra-class collision in a large latent space, compromising the quality of learned representations. To address this issue, we propose a novel contrastive learning method that utilizes randomly generated supervision signals. Our framework incorporates two projection heads: one handles conventional classification tasks, while the other employs a random algorithm to generate fixed-length vectors representing different classes. The second head executes a supervised contrastive learning task based on these vectors, effectively clustering instances of the same class and increasing the separation between different classes. Our method, Contrastive Learning via Randomly Generated Supervision(CLRGS), significantly improves the quality of feature representations across various datasets and achieves state-of-the-art performance in contrastive learning tasks.

关键词： Representation learning Visualization Computer vision signal processing algorithms Clustering algorithms Contrastive learning signal processing Vectors Robustness Speech processing

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DKD2L: Dual Knowledge Distillation Dynamic Learning for sketch-based 3D shape retrieval

DKD2L: Dual Knowledge Distillation Dynamic Learning for sket...

引用

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

作者： Yawen Su Jing Bai Gan Lin School of Computer Science and Engineering North Minzu University Yinchuan China The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Sketch-based 3D shape retrieval has become a prominent area of research in computer vision, confronting challenges related to the inherent diversity and abstraction of sketches, as well as inter-domain discrepancies. This paper introduces a novel approach called Dual Knowledge Distillation Dynamic Learning (DKD 2 L) aimed at enhancing the extraction of spatio-temporal features for sketch-based 3D shape retrieval. We develop a temporal feature extraction network to effectively capture the dynamic temporal characteristics of sketches and improve retrieval efficiency through temporal knowledge distillation. Additionally, to tackle intra-class variation and inter-class imbalance, we apply semantic knowledge distillation, enabling the 3D shape network to guide the sketch network in capturing common semantic information. This approach facilitates precise cross-modal alignment and enhances retrieval accuracy. Extensive experiments on two benchmark datasets demonstrate that DKD 2 L surpasses existing state-of-the-art methods.

关键词： Knowledge engineering Computer vision Three-dimensional displays Shape Semantics signal processing Benchmark testing Feature extraction Acoustics Speech processing

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Dynamic SpikFormer: Low-Latency & Energy-Efficient Spiking Neural Networks with Dynamic Time Steps for vision Transformers

Dynamic SpikFormer: Low-Latency & Energy-Efficient Spiking N...

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

作者： Gourav Datta Zeyu Liu Anni Li Peter A. Beerel Dept. of Electrical Computer & Systems Engineering Case Western Reserve University Cleveland USA Ming Hsieh Dept. of Electrical and Computer Engineering University of Southern California Los Angeles USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Spiking Neural Networks (SNNs) have emerged as a popular spatio-temporal computing paradigm for complex vision tasks. Recently proposed SNN training algorithms have significantly reduced the number of time steps (down to 1) for improved latency and energy efficiency, however, they target only convolutional neural networks (CNN). These algorithms, when applied to the recently spotlighted vision transformers (ViT), either require a large number of time steps or fail to converge. Based on the analysis of the histograms of the ANN and SNN activation maps, we hypothesize that each ViT block has a different sensitivity to the number of time steps. We propose a novel training framework that dynamically allocates the number of time steps to each ViT module depending on a trainable score assigned to each timestep. In particular, we generate a scalar binary time step mask that filters spikes emitted by each neuron in a leaky-integrate-and-fire (LIF) layer. The resulting SNNs have high activation sparsity and require only accumulate operations (AC), except for the input embedding layer, in contrast to expensive multiply-and-accumulates (MAC) needed in traditional ViTs. This yields significant improvements in energy efficiency. We evaluate our training framework and resulting SNNs on image recognition tasks including CIFAR10, CIFAR100, and ImageNet with different ViT architectures. We obtain a test accuracy of 95.97% with 4.97 time steps with direct encoding on CIFAR10.

关键词： Training Computer vision Sensitivity Neurons signal processing algorithms Spiking neural networks Transformers Energy efficiency Convolutional neural networks Speech processing

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