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Stereo image Super-Resolution Based on Pixel-Wise Knowledge Distillation Strategy

Stereo Image Super-Resolution Based on Pixel-Wise Knowledge ...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Ma, Li Li, Sumei Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728185514

In stereo image super-resolution (SR), it is equally important to utilize intra-view and cross-view information. However, most existing methods only focus on the exploration of cross-view information and neglect the full mining of intra-view information, which limits the reconstruction performance of these methods. Since single image SR (SISR) methods are powerful in intra-view information exploitation, we propose to introduce the knowledge distillation strategy to transfer the knowledge of a SISR network (teacher network) to a stereo image SR network (student network). With the help of the teacher network, the student network can easily learn more intra-view information. Specifically, we propose pixel-wise distillation as the implementation method, which not only improves the intra-view information extraction ability of student network, but also ensures the effective learning of cross-view information. Moreover, we propose a lightweight student network named Adaptive Residual Feature Aggregation network (ARFAnet). Its main unit, the ARFA module, can aggregate informative residual features and produce more representative features for image reconstruction. Experimental results demonstrate that our teacher-student network achieves state-of-the-art performance on all benchmark datasets.

关键词： Stereo image super-resolution knowledge distillation convolutional neural networks

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Deep learning for pedestrian behavior understanding 6

Deep learning for pedestrian behavior understanding

引用

6th International conference on Advanced Technologies for Signal and image processing, ATSIP 2022

作者： Gazzeh, Soulayma Douik, Ali ENISo University of Sousse Laboratory of Networked Objects Control and Communications Systems Sousse Tunisia

ISBN: (纸本)9781665451161

visual perception and road scene understanding are critical components of autonomous driving (AD). Detecting objects and predicting surrounding road user behavior are important tasks for robust and safe driving systems. With the rise of Machine Learning (ML) and Deep Learning (DL) methods using convolutional neural networks (CNN) , research on road scene understanding and motion recognition have made significant progress. In this paper, a real-time recognition of pedestrian's intention to cross is presented as a step towards autonomous driving. Our approach is constructed using a real-Time visual pedestrian tracker based on a one-stage object detector that gave a fast and robust learning produces dynamic and static features as inputs of a linear classifier for predicting crossing behavior. We apply our approach to public real-life benchmark - JAAD dataset "Joint Attention in Autonomous Driving."The experimental results have shown that our proposed method is efficient and competitive with other approaches. Overall, our system achieves an accuracy of 92.88%. © 2022 IEEE.

关键词： Autonomous vehicles

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Artificial Intelligence of Internet of Things and Virtual Reality Technology in the image Reconstruction of Film and Television Characters 4th

Artificial Intelligence of Internet of Things and Virtual Re...

引用

4th International conference on Machine Learning, image processing, Network Security and Data Sciences (MIND)

作者： Guo, Hanlin United Int Coll Div Culture & Creat Anshan 114000 Liaoning Peoples R China

ISBN: (纸本)9783031243660;9783031243677

The animation market structure continues to be optimized, and the animation economic cake continues to grow. Character image building is a very important work content, this part of the work is the most important part of the whole film and TVseries. Animation characters from the character, characteristics, character psychological activities, action and other aspects to show their own character image. All these features are innovative elements in film and television, which provide their own services for the development of film and television plot and make the development of film and television more visual. This paper studies the research on the image reconstruction of Internet of Things artificial intelligence and virtual reality technology in film and television character reconstruction, and points out the related content of the research on the image reconstruction of film and television character reconstruction. The test confirmed that artificial intelligence of the Internet of Things and virtual reality technology had excellent performance in the image reconstruction of film and television character role shaping.

关键词： Internet of things artificial intelligence Virtual reality technology Film and television character shaping image reconstruction research

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Intelligent Classification System for Recyclables based on image and Acoustic processing 4

Intelligent Classification System for Recyclables based on I...

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4th International conference on communications, Information System and Computer Engineering, CISCE 2022

作者： Lu, Hanning Quan, Letian Shi, Jia Hangzhou Dianzi University School of Communication Engineering Hangzhou310000 China Shanghai University School of Computer Engineering and Science Shanghai200444 China Chongqing Medical University School of Medical Information Engineering Chongqing400038 China

ISBN: (数字)9781665498487

ISBN: (纸本)9781665498487

It is of great significance to classify garbage through deep learning and image processing technology to realize garbage recycling and resource reuse. We propose a garbage classification system based on improved Faster R-CNN with audio-visual combination to achieve higher classification accuracy. We have introduced a new deep learning based system to effectively combine image and sound features for garbage classification. Based on this model, we implement an automatic garbage classification program with a graphical interface. We tried to add transparency and audio features for garbage sorting. The result shows that our method has high recognition accuracy. © 2022 IEEE.

关键词： image recognition

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Entropy-based Deep Product Quantization for visual Search and Deep Feature Compression

Entropy-based Deep Product Quantization for Visual Search an...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Niu, Benben Wei, Ziwei He, Yun Tsinghua Univ Dept Elect Engn Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781728185514

With the emergence of various machine-to-machine and machine-to-human tasks with deep learning, the amount of deep feature data is increasing. Deep product quantization is widely applied in deep feature retrieval tasks and has achieved good accuracy. However, it does not focus on the compression target primarily, and its output is a fixed-length quantization index, which is not suitable for subsequent compression. In this paper, we propose an entropy-based deep product quantization algorithm for deep feature compression. Firstly, it introduces entropy into hard and soft quantization strategies, which can adapt to the codebook optimization and codeword determination operations in the training and testing processes respectively. Secondly, the loss functions related to entropy are designed to adjust the distribution of quantization index, so that it can accommodate to the subsequent entropy coding module. Experimental results carried on retrieval tasks show that the proposed method can be generally combined with deep product quantization and its extended schemes, and can achieve a better compression performance under near lossless condition.

关键词： deep feature deep product quantization entropy image retrieval semi-supervised

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Research on training image recognition based on deep Convolutional neural networks: A case study of the hand-held insulated ladder recognition model 2

Research on training image recognition based on deep Convolu...

引用

2nd International conference on Signal processing, Computer Networks and communications, SPCNC 2023

作者： Wei, Chaobing Jiangmen power supply Bureau Guangdong Power Grid Corporation 529000 China

ISBN: (纸本)9798400716430

In this paper, we will introduce a series of work required for the design model of the insulation ladder identification technology, including the construction of the model training environment, the preparation of data sets, the training of the model, the visual operation of the model and the recognition result of the model. The identification model of walking insulation ladder in electric power construction will be developed based on PyTorch. We will ensure the safety of distribution network operations. In this paper, based on the existing YOLOv5 based target detection model, an improved design was carried out to enhance the accuracy of its detection of small targets, and the model was lightweight. Finally, the improved model was deployed to the edge device for hardware experiment verification. © 2023 ACM.

关键词： Ladders

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Weight-Based Mask for Domain Adaptation 48

Weight-Based Mask for Domain Adaptation

引用

48th IEEE International conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Lee, Eunseop Kim, Inhan Kim, Daijin Pohang University of Science and Technology Department of Computer Science and Engineering Korea Republic of

ISBN: (纸本)9781728163277

In computer vision, unsupervised domain adaptation (UDA) is an approach to transferring knowledge from a label-rich source domain to a fully-unlabeled target domain. Conventional UDA approaches have two problems. The first problem is that a class classifier can be biased to the source domain because it is trained using only source samples. The second is that previous approaches align image-level features regardless of foreground and background, although the classifier requires foreground features. To solve these problems, we introduce Weight-based Mask Network (WEMNet) composed of Domain Ignore Module (DIM) and Semantic Enhancement Module (SEM). DIM obtains domain-Agnostic feature representations via the weight of the domain discriminator and predicts categories. In addition, SEM obtains class-related feature representations using the classifier weight and focuses on the foreground features for domain adaptation. Extensive experimental results reveal that the proposed WEMNet outperforms the competitive accuracy on representative UDA datasets. © 2023 IEEE.

关键词： image classification

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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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Analyzing Time Complexity of Practical Learned image Compression Models

Analyzing Time Complexity of Practical Learned Image Compres...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Pan, Xiaohan Guo, Zongyu Chen, Zhibo Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Hefei Peoples R China

ISBN: (纸本)9781728185514

We have witnessed the rapid development of learned image compression (LIC). The latest LIC models have outperformed almost all traditional image compression standards in terms of rate-distortion (RD) performance. However, the time complexity of LIC model is still underdiscovered, limiting the practical applications in industry. Even with the acceleration of GPU, LIC models still struggle with long coding time, especially on the decoder side. In this paper, we analyze and test a few prevailing and representative LIC models, and compare their complexity with traditional codecs including H.265/HEVC intra and H.266/VVC intra. We provide a comprehensive analysis on every module in the LIC models, and investigate how bitrate changes affect coding time. We observe that the time complexity bottleneck mainly exists in entropy coding and context modelling. Although this paper pay more attention to experimental statistics, our analysis reveals some insights for further acceleration of LIC model, such as model modification for parallel computing, model pruning and a more parallel context model.

关键词： Time Complexity Analysis image Compression Parallel Computing Neural Networks

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See SIFT in a Rain: Divide-and-conquer SIFT Key Point Recovery from a Single Rainy image

See SIFT in a Rain: Divide-and-conquer SIFT Key Point Recove...

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IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Wang, Ping Wu, Wei Li, Zhu Liu, Yong Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Univ Missouri Dept Comp Sci & Elect Engn Kansas City MO USA

ISBN: (纸本)9781728185514

Scale-Invariant Feature Transform (SIFT) is one of the most well-known image matching methods, which has been widely applied in various visual fields. Because of the adoption of a difference of Gaussian (DoG) pyramid and Gaussian gradient information for extrema detection and description, respectively, SIFT achieves accurate key points and thus has shown excellent matching results but except under adverse weather conditions like rain. To address the issue, in the paper we propose a divide-and-conquer SIFT key points recovery algorithm from a single rainy image. In the proposed algorithm, we do not aim to improve quality for a derained image, but divide the key point recovery problem from a rainy image into two sub-problems, one being how to recover the DoG pyramid for the derained image and the other being how to recover the gradients of derained Gaussian images at multiple scales. We also propose two separate deep learning networks with different losses and structures to recover them, respectively. This divide-and-conquer scheme to set different objectives for SIFT extrema detection and description leads to very robust performance. Experimental results show that our proposed algorithm achieves state-of-the-art performances on widely used image datasets in both quantitative and qualitative tests.

关键词： SIFT key point recovery rainy image extrema detection and description deep learning

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