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共 18085 条记录，以下是151-160 订阅

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Spatially Nonuniform Filtering and Encoding of Visual Information in a Retinomorphic Hardware System

Spatially Nonuniform Filtering and Encoding of Visual Inform...

引用

international conference on Autonomous robots and Agents, ICARA

作者： Tatsuya Yamada Hiroyuki Yokota Yuki Hayashida Shinsuke Yasuakwa Dept. Human Intelligence Syst. Kyushu Institute of Technology Kitakyushu Fukuoka JAPAN Dept. Inform. Eng. Grad. Eng. Mie University Tsu Mie JAPAN

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Efficient information processing in biological early-vision systems is thought to rely on spatially nonuniform computations in the central and peripheral visual fields. Inspired by this functional segregation, we developed a digital hardware vision system that integrates two distinct spatiotemporal filters and spike encoders for the central and peripheral regions on a single image sensor plane. The system employs digital processing techniques to emulate graded potential signals and point-process spike outputs, mimicking the functionality of vertebrate retinas. System-level testing on actual hardware equipment verified region-specific response characteristics for the central and peripheral regions. This proposed approach advances the realization of retinomorphic efficient computations in future robotics and embedded vision systems.

关键词： Visualization Image coding robot vision systems Optimization methods Retina Hardware Spatiotemporal phenomena Computational efficiency robots Testing

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Out-of-Distribution Detectors: Not Yet Primed for Practical Deployment

Out-of-Distribution Detectors: Not Yet Primed for Practical ...

引用

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

作者： Changshun Wu Wendi Ding Xiaowei Huang Saddek Bensalem Université Grenoble Alpes Grenoble France University of Liverpool Liverpool UK

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Out-of-distribution (OoD) detectors work alongside deep neural networks (DNNs) to reduce their risks in eliciting wrong predictions. Unfortunately, OoD detectors built on data-centric designs are also subject to robustness issues, as the DNNs. This paper examines the practical robustness of OoD detectors, taking computer vision tasks as examples and considering natural input perturbations that may come from camera positions and lighting conditions. Our study incorporates extensive experiments over 2000+ settings and correlation studies, highlighting significant challenges in OoD detection robustness, e.g., OoD detectors’ robustness error rate in practical settings can be as high as 28%. The paper advances our understanding of OoD detectors’ applicability in real world and the interplay of their robustness with DNNs’ robustness, calling for novel methodology to design robust OoD detectors in broader signal processing tasks.

关键词： Computer vision Error analysis Perturbation methods Design methodology Lighting Detectors signal processing Reliability engineering Robustness Speech processing

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iReWindColor: vision Transformer with Residual Embedding and Window Encoder for Point-Interactive Image Colorization

iReWindColor: Vision Transformer with Residual Embedding and...

引用

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

作者： Hideyuki Ogura Masaaki Ikehara Faculty of Science and Engineering Keio University Yokohama Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Point-interactive image colorization is intended to colorize a grayscale image by allowing the user to specify colors at specific locations. The colors provided by the user (user hints) are propagated appropriately throughout the image to obtain a reasonably colorized image with minimal user effort. However, existing approaches result in excessive color propagation and color bleeding beyond the boundaries of the region. To address this problem, we propose iReWindColor. iReWindColor achieves fine colorization by using smaller patches for vision Transformer-based processing. Due to the increase in computational cost caused by reducing the patch size, the processing for the entire image is replaced by Window Encoder, which is the window-based processing. This allows fine colorization with small patches without increasing the computational cost. In addition, we propose Residual Embaedding to reduce colorization divergence between windows. Residual Embedding makes the network consider the entire image. Furthermore, for better utilizing user hints, we propose Encode Block that consists of both Self-Attention and CNNs. Adopting it helps to capture the interrelationships between image patches. Quantitative and qualitative results show that our approach outperforms existing methods for point-interactive image colorization and produces color images that accurately reflect user intent with less computation cost.

关键词： Computer vision Costs Image color analysis Computational modeling signal processing Gray-scale Transformers Computational efficiency Speech processing Hemorrhaging

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Closing the Communication Divide: Enhancing Sign Language Recognition with Gesture-to-Text Conversion Through Computer vision 8th

Closing the Communication Divide: Enhancing Sign Language Re...

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8th IFIP TC 12 international conference on Computer, Communication and signal processing, ICCCSP 2024

作者： Krishnakumar, R. Kapil, M. Muthu Lakshmi, V. Department of Information Technology St. Joseph’s College of Engineering OMR Tamil Nadu Chennai India

ISBN: (纸本)9783031736162

This paper introduces an innovative method specifically created for identifying and understanding fixed hand movements that represent symbols in American Sign Language (ASL). With the goal of improving communication and learning for those with hearing and speaking difficulties, this system is exceptionally useful for the deaf community to interact with technology. Utilizing the widespread use of ASL as a widely accepted form of communication, the system primarily concentrates on recognizing and converting static hand gestures that correspond to ASL letters into written output. Furthermore, it also includes the ability to convert text to speech, adding to its functionality. At the heart of our groundbreaking approach is the use of Principal Component Analysis (PCA) on still images of the ASL alphabet to accurately detect gestures. By utilizing the power of PCA to identify crucial elements, our system can effectively classify input images and provide accurate recognition of the corresponding ASL alphabet. This output is then presented as text, and can even be transformed into speech, making it a comprehensive tool for individuals with hearing and speech impairments to seamlessly communicate through computer technology. Significantly, this system eliminates the requirement for extra data collecting tools, making it more convenient for users. The strong recognition of ASL gestures, along with the ability to convert those gestures into text and sound, empowers those who are deaf or hard of hearing to effectively communicate through technology. © IFIP international Federation for Information processing 2025.

关键词： Gesture recognition

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6th IFIP TC 12 international conference on Intelligence Science, ICIS 2024

6th IFIP TC 12 International Conference on Intelligence Scie...

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6th IFIP TC 12 international conference on Intelligence Science, ICIS 2024

ISBN: (纸本)9783031712524

The proceedings contain 25 papers. The special focus in this conference is on Intelligence Science. The topics include: Detection of Depression in EEG signals Based on Convolutional Transformer and Adaptive Transfer Learning;twin Bounded Least Squares Support Vector Regression;MLEE: Event Extraction as Multi-label Classification Task at Token Level;research on Improvement of Sweeping Learning Chain Algorithm Based on Factor Space Theory;end-To-End Control of a Quadrotor Using Gaussian Ensemble Model-Based Reinforcement Learning;research on the Causal Forest Algorithm Based on Factor Space Theory;superpositioner – A Non-logical Computation Model;factor Analog Reasoning Model and Its Solution Research;research on Factor Support Vector Multi-classification Algorithm Based on Factor Space Theory;Improve LLM Inference Performance with Matrix Decomposition Strategies;trajectory Prediction of Unmanned Surface Vehicle Based on Improved Transformer;Deep Neural Network Based Relocalization of Mobile robot in Visual SLAM;A vision-Based Method for UAV Autonomous Landing Area Detection;research on Object Detection for Intelligent Sensing of Navigation Mark in Yangtze River;Cascaded Sliding-Window-Based Relativistic GAN Fusion for Perceptual and Consistent Video Super-Resolution;integration of Raman Spectroscopy, On-Line Microscopic Imaging and Deep Learning-Based Image Analysis for Real-Time Monitoring of Cell Culture Process;DRL-SLAM: Enhanced Object Detection Fusion with Improved YOLOv8;Driver Fatigue Recognition Based on EEG signal and Semi-supervised Learning;SC-EcapaTdnn: ECAPA-TDNN with Separable Convolutional for Speaker Recognition;Evolving Financial Markets: The Impact and Efficiency of AI-Driven Trading Strategies;DSFM Method: A New Approach to Enhancing Discrimination Ability on AI-Generated Datasets;enhancing Weakly Supervised Medical Segmentation via Heterogeneous Co-training with Box-Wise Augmentation and Pseudo-Label Filtering;FCGA-Former: A Hybrid Factor Space Cl

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18th international conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

18th International Conference on Wireless Artificial Intelli...

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18th international conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

ISBN: (纸本)9783031714696

The proceedings contain 108 papers. The special focus in this conference is on Wireless Artificial Intelligent Computing Systems and Applications. The topics include: Profinder: Towards Professionals Recognition on Mobile Devices for Users with Cognitive Decline;truthful Double Auction-Based Resource Allocation Mechanisms for Latency-Sensitive Applications in Edge Clouds;V2ICooper: Toward Vehicle-to-Infrastructure Cooperative Perception with Spatiotemporal Asynchronous Fusion;recommendation-Aware Collaborative Edge Caching Strategy in the Internet of Vehicles;towards Robust Internet of Vehicles Security: An Edge Node-Based Machine Learning Framework for Attack Classification;device-Edge-Cloud Collaborative Video Stream processing and Scheduling Strategy Based on Deep Reinforcement Learning;consistent Low-Latency Scheduling for Microsecond-Scale Tasks in Data Centers;multi-scale Data Reconstruction Based Policy Optimization Algorithm for Skill Learning;Joint Optimization Design of Intelligence Reflecting Surface Assisted MU-MISO System Based on Deep Reinforcement Learning;Secure Motion Verification for High Altitude Platforms with a Hybrid AOA-TDOA-FDOA Scheme;variational Autoencoder Based Automatic Clustering for Multivariate Time Series Anomaly Detection;deep Reinforcement Learning Based Economic Dispatch with Cost Constraint in Cyber Physical Energy System;inceptionNeXt Network with Relative Position Information for Microexpression Recognition;adMarks: Image Steganography Based on Adversarial Perturbation;a Review on Binary Code Analysis Datasets;low-Cost robot Path Planning Mechanism for Escaping from Dead Ends;towards Communication-Efficient Collaborative Perception: Harnessing Channel-Spatial Attention and Knowledge Distillation;improving Anomaly Scene Recognition with Large vision-Language Models;ReSU-Net: State Space Model for 3D Abdominal Multi-organ Segmentation;autocue : Targeted Textual Adversarial Attacks with Adversarial Prompts;universal Sign Language

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CrossHash: Cross-scale vision Transformer Hashing for Image Retrieval

CrossHash: Cross-scale Vision Transformer Hashing for Image ...

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

作者： Weigang Wang Zhongwen Guo Wenxiang Jiang Yujun Lan Wentao Ma Faculty of Information Science and Engineering Ocean University of China Qingdao China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Transformers have made significant progress in dealing with computer vision tasks. However, most existing vision Transformers (ViT) typically focus on single-scale information, limiting their capability to model interactions when processing multi-scale features. Moreover, the explicit mapping of continuous real-valued features to discrete hashing codes via a quantization layer is suboptimal for retrieval tasks. To overcome the above problem, we propose a novel deep hashing based on a Cross-scale Transformer named CrossHash, aiming to extract multi-scale features. Furthermore, a relative similarity quantization method is first introduced, which maximizes the similarity between the relative positional representation of continuous codes and the normalized centroids; this method effectively reduces quantization error. It optimizes feature distribution via contrastive learning loss, maximizing the inter-class distance and minimizing the intra-class distance of the learned feature. Extensive experiments on three benchmark datasets demonstrate that the proposed model outperforms other state-of-the-art deep hashing methods. Source code is available https://***/wwg1010/CrossHash.

关键词： Computer vision Quantization (signal) Codes Limiting Source coding Image retrieval Transformers Feature extraction Sensors Speech processing

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18th international conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

18th International Conference on Wireless Artificial Intelli...

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18th international conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

ISBN: (纸本)9783031714634

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GPPT: Gaussian Process-infused Prompt Tuning for vision-language Models

GPPT: Gaussian Process-infused Prompt Tuning for Vision-lang...

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

作者： Shijing Si Haixia Sun Jiawen Gu School of Economics and Finance Shanghai International Studies University Shanghai China Department of Mathematics Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Pre-trained vision-language models (VLMs) have achieved remarkable success in image classification tasks, leveraging efficient prompt tuning methods. However, the reliability of fine-tuned VLMs in safety-critical scenarios remains a concern due to the under-explored issue of confidence calibration. To address this limitation, we introduce Gaussian Process-infused Prompt Tuning (GPPT), a novel framework that integrates a Gaussian process into the hidden representations of images. By utilizing random Fourier features (RFF) and Laplace approximation, GPPT enables end-to-end training and seamless integration with existing prompt tuning methods. Our extensive experiments on 11 diverse downstream datasets demonstrate that GPPT achieves competitive performance with deep ensembles in both prediction accuracy and calibration, while requiring only a fraction of the inference time.

关键词： Training Uncertainty Gaussian processes signal processing Acoustics Calibration Reliability Speech processing Tuning Image classification

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Reward Generation via Large vision-Language Model in Offline Reinforcement Learning

Reward Generation via Large Vision-Language Model in Offline...

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

作者： Younghwan Lee Tung M. Luu Donghoon Lee Chang D. Yoo Electrical Engineering KAIST Daejeon South Korea Robotics Program KAIST Daejeon South Korea

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In offline reinforcement learning (RL), learning from fixed datasets presents a promising solution for domains where real-time interaction with the environment is expensive or risky. However, designing dense reward signals for offline dataset requires significant human effort and domain expertise. Reinforcement learning with human feedback (RLHF) has emerged as an alternative, but it remains costly due to the human-in-the-loop process, prompting interest in automated reward generation models. To address this, we propose Reward Generation via Large vision-Language Models (RG-VLM), which leverages the reasoning capabilities of LVLMs to generate rewards from offline data without human involvement. RG-VLM improves generalization in long-horizon tasks and can be seamlessly integrated with the sparse reward signals to enhance task performance, demonstrating its potential as an auxiliary reward signal.

关键词： Reinforcement learning signal processing Real-time systems Human in the loop Data models Cognition Acoustics Labeling Speech processing

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