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共 18110 条记录，以下是161-170 订阅

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

iReWindColor: Vision Transformer with Residual Embedding and...

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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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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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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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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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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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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)9783031714665

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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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Federated Prototype Guided Adaption for vision-Language Models

Federated Prototype Guided Adaption for Vision-Language Mode...

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

作者： Youchao Liu Dingjiang Huang School of Data Science and Engineering East China Normal University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Federated Learning (FL) is a new pivotal paradigm for decentralized training on heterogeneous data. Recently fine-tuning of vision-Language Models (VLMs) has been extended to the federated setting to improve overall performance. Unfortunately, in this case, FL still faces two critical challenges that hinder its actual performance: data distribution heterogeneity and high resource costs brought by large VLMs. In this paper, we introduce FedPGA, a prototype-guided method, for achieving performance improvements in the federated setting for VLMs. Concretely, we design a prototype-based adapter for the vision-language model, CLIP. The lightweight adapter updates the prior knowledge encoded in CLIP to enhance its adaption capability further and avoid the effects of data distribution heterogeneity in the federated setting. Simultaneously, small-scale operations can mitigate the computational and communication burden caused by large VLMs. Our comprehensive empirical evaluations of nine diverse image classification datasets show that our method is superior to existing FL methods under VLMs.

关键词： Training Adaptation models Costs Federated learning Computational modeling Prototypes signal processing Speech processing Faces Image classification

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LV-ReID: Large Language-vision Alignment Model for Text-based Person Re-identification

LV-ReID: Large Language-Vision Alignment Model for Text-base...

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

作者： Yinghui Xia Chao Wang Jinsong Yang HKUST(GZ) Wuhan University AutoAgents.ai

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Person Re-Identification (ReID) is a critical task in computer vision that involves identifying individuals across different cameras or video frames. It’s challenging due to variations in appearance, lighting, viewpoints, clothing, and occlusions. Text-based ReID adds complexity by requiring image retrieval or individual identification from a dataset based on text queries. The BLIP-2 model addresses these challenges by combining multi-modal alignment and matching into a single framework, using a pre-trained vision-language model with a Q-Former component to bridge the visual and textual modalities. This approach significantly boosts performance in multi-modal tasks and information retrieval, especially with large datasets. The LV-ReID framework, which incorporates BLIP-2, enhances text-based ReID by integrating retrieval and generation tasks. The experimental results show BLIP-2’s effectiveness in aligning and matching pedestrian images for information retrieval tasks. It demonstrates proficiency in multi-modal tasks and offers an efficient solution for text-based ReID by fusing visual and textual data, improving pedestrian identification accuracy in complex environments.

关键词： Visualization Pedestrians Image retrieval Streaming media signal processing Information retrieval Security Speech processing Standards Identification of persons

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