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检索条件"任意字段=Conference on Visual Communications and Image Processing 2005"

共 4614 条记录，以下是81-90 订阅

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Design and Implementation of image Description Model Using Artificial Intelligence Based Techniques 3rd

Design and Implementation of Image Description Model Using A...

引用

3rd International conference on Computational Electronics for Wireless communications, ICCWC 2023

作者： Ingale, Sumedh Bamnote, G.R. Prof. Ram Meghe Institute of Technology & Research Badnera Amravati India

ISBN: (纸本)9789819719457

The process that produces written descriptions that effectively represent the meaning and context of an image is known as image captioning. To integrate visual and textual data, it needs to blend computer vision and natural language processing methods. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), such as long-short-term memory (LSTM) networks, are two methods used for captioning images. High-level visual features from the input image are extracted by the CNN, and the RNN uses those features to generate the matching captions. Residual Network is a deep CNN architecture with exceptional results in a range of computer vision tasks, including the classification of images. ResNet has been used as the foundation for extracting picture features. ResNet and CNN abstracts an image's visual information via feature extraction and uses neural networks with recurrent architecture to provide meaningful, contextually appropriate captions. Machines are now able to comprehend and speak about the visual world in a manner similar to humans because of the integration of computer vision and natural language processing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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An Efficient ConvNet for Learned image Compression with Transformer-Style Architecture

An Efficient ConvNet for Learned Image Compression with Tran...

引用

2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Ruan, Haihang Wang, Feng Wang, Yan China Peking University School of Electronic and Computer Engineering China

ISBN: (纸本)9798350359855

Recently, transformer-based and convolution-based methods have achieved significant results in learned image compression. By comparing the design of convolutional network (convnet) and transformers, we replace the self-Attention with convolution to capture spatial and channel adaptability. We propose a simple attention module (SAM) with transformer style. Combining the proposed SAM with channel-wise and checkerboard entropy model, we propose an efficient end-To-end learned image compression method. It is a simple method but obtains strong result and efficient coding speed. Experiments demonstrate that our method achieves competitive results by comparing with previous learning-based methods and conventional image codecs. © 2023 IEEE.

关键词： image compression

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A Lightweight Tire Tread image Classification Network

A Lightweight Tire Tread Image Classification Network

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Zhang, Fenglei Li, Da Li, Shenghua Guan, Weili Liu, Meng Shandong Jianzhu Univ Jinan Peoples R China Monash Univ Elbourne Australia

ISBN: (纸本)9781665475921

VCIP 2022 "Tire pattern image classification based on lightweight network challenge" aims to design lightweight networks that correctly classify tire surface tread patterns and indentation images using less overhead. To this end, we present a novel lightweight tire tread classification network. Concretely, we adopt the ShuffleNet-V2-x0.5 network as our backbone. To reduce the computation complexity, we introduce the Space-To-Depth and Anti-Alias Downsampling modules to pre-process the input image. Moreover, to enhance the classification ability of our model, we adopt the knowledge distillation strategy by considering Vision Transformer as the teacher network. To ensure the robustness of our model, we pre-train it on imageNet and fine-tune the training set of the challenge. Experiments on the challenge dataset demonstrate that our model achieves superior performance, with 99.00% classification accuracy, 25.51M FLOPs, and 0.20M parameters.

关键词： tire tread image classification knowledge distillation lightweight image classification

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Ultra-High Resolution image Segmentation with Efficient Multi-Scale Collective Fusion

Ultra-High Resolution Image Segmentation with Efficient Mult...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Sun, Guohao Yan, Haibin Beijing Univ Posts & Telecommun Sch Automat Beijing Peoples R China

ISBN: (纸本)9781665475921

Ultra-high resolution image segmentation has attracted increasing attention recently due to its wide applications in various scenarios such as road extraction and urban planning. The ultra-high resolution image facilitates the capture of more detailed information but also poses great challenges to the image understanding system. For memory efficiency, existing methods preprocess the global image and local patches into the same size, which can only exploit local patches of a fixed resolution. In this paper, we empirically analyze the effect of different patch sizes and input resolutions on the segmentation accuracy and propose a multi-scale collective fusion (MSCF) method to exploit information from multiple resolutions, which can be end-to-end trainable for more efficient training. Our method achieves very competitive performance on the widely-used DeepGlobe dataset while training on one single GPU.

关键词： Semantic segmentation remote sensing ultra-high resolution convolutional neural networks

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Optimized MobileNetV2 Based on Model Pruning for image Classification

Optimized MobileNetV2 Based on Model Pruning for Image Class...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Xiao, Peng Pang, Yuliang Feng, Hao Hao, Yu Xian Univ Posts & Telecommunicat Ctr Image & Informat Proc (CIIP) Xian Peoples R China

ISBN: (纸本)9781665475921

Due to the large memory requirement and a large amount of computation, traditional deep learning networks cannot run on mobile devices as well as embedded devices. In this paper, we propose a new mobile architecture combining MobileNetV2 and pruning, which further decreases the Flops and number of parameters. The performance of MobileNetV2 has been widely demonstrated, and pruning operation can not only allow further model compression but also prevent overfitting. We have done ablation experiments at CIIP Tire Data for different pruning combinations. In addition, we introduced a global hyperparameter to effectively weigh the accuracy and precision. Experiments show that the accuracy of 98.3 % is maintained under the premise that the model size is only 804.5 KB, showing better performance than the baseline method.

关键词： model compression MobileNetV2 pruning

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MAiVAR: Multimodal Audio-image and Video Action Recognizer

MAiVAR: Multimodal Audio-Image and Video Action Recognizer

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IEEE International conference on visual communications and image processing (VCIP)

作者： Shaikh, Muhammad Bilal Chai, Douglas Islam, Syed Mohammed Shamsul Akhtar, Naveed Edith Cowan Univ 270 Joondalup Dr Perth WA 6027 Australia Univ Western Australia 35 Stirling Highway Perth WA 6009 Australia

ISBN: (纸本)9781665475921

Currently, action recognition is predominately performed on video data as processed by CNNs. We investigate if the representation process of CNNs can also be leveraged for multimodal action recognition by incorporating image-based audio representations of actions in a task. To this end, we propose Multimodal Audio-image and Video Action Recognizer (MAiVAR), a CNN-based audio-image to video fusion model that accounts for video and audio modalities to achieve superior action recognition performance. MAiVAR extracts meaningful image representations of audio and fuses it with video representation to achieve better performance as compared to both modalities individually on a large-scale action recognition dataset.

关键词： Large dataset

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A Learning-based Approach for Martian image Compression

A Learning-based Approach for Martian Image Compression

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Ding, Qing Xu, Mai Li, Shengxi Deng, Xin Shen, Qiu Zou, Xin Beihang Univ Beijing Peoples R China Nanjing Univ Nanjing Peoples R China Beijing Inst Spacecraft Syst Engn Beijing Peoples R China

ISBN: (纸本)9781665475921

For the scientific exploration and research on Mars, it is an indispensable step to transmit high-quality Martian images from distant Mars to Earth. image compression is the key technique given the extremely limited Mars-Earth bandwidth. Recently, deep learning has demonstrated remarkable performance in natural image compression, which provides a possibility for efficient Martian image compression. However, deep learning usually requires large training data. In this paper, we establish the first large-scale high-resolution Martian image compression (MIC) dataset. Through analyzing this dataset, we observe an important non-local self-similarity prior for Marian images. Benefiting from this prior, we propose a deep Martian image compression network with the non-local block to explore both local and non-local dependencies among Martian image patches. Experimental results verify the effectiveness of the proposed network in Martian image compression, which outperforms both the deep learning based compression methods and HEVC codec.

关键词： image compression Martian image deep learning

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Frequency-aware Learned image Compression for Quality Scalability

Frequency-aware Learned Image Compression for Quality Scalab...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Choi, Hyomin Racape, Fabien Hamidi-Rad, Shahab Ulhaq, Mateen Feltman, Simon Interdigital Emerging Technol Lab Los Altos CA 94022 USA Simon Fraser Univ Sch Engn Sci Burnaby BC Canada

ISBN: (纸本)9781665475921

Spatial frequency analysis and transforms serve a central role in most engineered image and video lossy codecs, but are rarely employed in neural network (NN)-based approaches. We propose a novel NN-based image coding framework that utilizes forward wavelet transforms to decompose the input signal by spatial frequency. Our encoder generates separate bitstreams for each latent representation of low and high frequencies. This enables our decoder to selectively decode bitstreams in a quality-scalable manner. Hence, the decoder can produce an enhanced image by using an enhancement bitstream in addition to the base bitstream. Furthermore, our method is able to enhance only a specific region of interest (ROI) by using a corresponding part of the enhancement latent representation. Our experiments demonstrate that the proposed method shows competitive rate-distortion performance compared to several non-scalable image codecs. We also showcase the effectiveness of our two-level quality scalability, as well as its practicality in ROI quality enhancement.

关键词： End-to-end compression learned image compression quality scalability wavelet decomposition

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MSCI: A Multi-Source Composite image Database for Compression Distortion Quality Assessment

MSCI: A Multi-Source Composite Image Database for Compressio...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Zhang, Xiaofang Xu, Zhuowei Lin, Zhiheng Wang, Miaohui Shenzhen Univ Coll Informat & Elect Engn Shenzhen Peoples R China Jimei Univ Coll Marine Equipment & Mech Engn Xiamen Peoples R China

ISBN: (纸本)9781665475921

With the rapid development of multi-sensor fusion technology in various industrial fields, many composite images closely related to human life have been produced. To meet the rapidly growing needs of various image-based applications, we have established the first multi-source composite image (MSCI) database for image quality assessment (IQA). Our MSCI database contains 80 reference images and 1600 distorted images, generated by four advanced compression standards with five distortion levels. In particular, these five distortion levels are determined based on the first five just noticeable difference (JND) levels. Moreover, we verify the IQA performance of some representative methods on our MSCI database. The experimental results show that the performance of the existing methods on the MSCI database needs to be further improved.

关键词： image quality assessment multi-sensor composite image compression distortion

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An Efficient Content-aware Downsampling-based Video Compression Framework

An Efficient Content-aware Downsampling-based Video Compress...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Jiang, Hao Chen, Li Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China

ISBN: (纸本)9781665475921

Recently, deep learning-based video compression algorithms have achieved competitive performance in Bjontegaard delta (BD) rate, especially those adopting super-resolution networks as post-processing modules in downsampling-based video compression (DBC) frameworks. However, limited by the non-differentiable characteristics of traditional codecs, DBC frameworks mainly focus on improving the performance of super-resolution modules while ignoring optimizing downscaling modules. It is crucial to improve video compression performance without introducing additional modifications to the decoder client in practical application scenarios. We propose a context-aware processing network (CPN) compatible with standard codecs with no computational burden introduced to the client, which preserves the critical information and essential structures during downscaling. The proposed CPN works as a precoder cascaded by standard codecs to improve the compression performance on the server before encoding and transmission. Besides, a surrogate codec is employed to simulate the degradation process of the standard codecs and backpropagate the gradient to optimize the CPN. Experimental results show that the proposed method outperforms latest pre-processing networks and achieves considerable performance compared with the latest DBC frameworks.

关键词： compact representation video precoding deep learning

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