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CNN-BASED FAST CU PARTITIONING ALGORITHM FOR VVC INTRA CODING 29

CNN-BASED FAST CU PARTITIONING ALGORITHM FOR VVC INTRA CODIN...

引用

ieee international conference on image processing (ICIP)

作者： Xu, Jun Wu, Guoqing Zhu, Chen Huang, Yan Song, Li Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Over a year has passed since the finalization of Versatile Video Coding (H.266/VVC), yet it is still far from practical deployment, a major reason being the excessive complexity. The flexible and sophisticated quad-tree with nested multi-type tree partitioning structure in VVC provides considerable performance gains while bringing about an exponential increase in encoding time. To reduce the coding complexity, this paper proposes a Convolutional Neural Network (CNN) based fast Coding Unit (CU) partitioning algorithm for intra coding, which accelerates CU partition through predicting the partition modes with texture information and terminating redundant modes in advance. Corresponding classifiers are designed for different CU sizes to improve prediction accuracy. Low rate-distortion performance degradation is guaranteed by introducing performance loss due to misclassification into the loss function. Experiments show that the proposed method can save encoding time ranging from 38.39% to 62.33% with 0.92% to 2.36% bit rate increase.

关键词： Video compression VVC intra coding CU partition CNN complexity reduction

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Single image Deraining Based on Denoising Diffusion Implicit Models 20

Single Image Deraining Based on Denoising Diffusion Implicit...

引用

20th international conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Chang, Luyao Zhang, Zhi College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9798350356328

To address the challenges of prolonged sampling time and the limitation to process specific image sizes in current image deraining research, we propose a novel single image deraining approach based on denoising diffusion implicit models. The method adopts a variant of the U-Net fully convolutional network architecture based on SR3, concatenating rainy images of arbi-trary sizes and noise images along the channel dimension to iteratively obtain high-resolution rain-free images. Specifically, we in-corporate residual blocks from BigGAN-deep to replace traditional residual blocks, thereby deepening the network to enhance model generalization. Additionally, we employ smooth noise esti-mation via image overlapping block processing to improve image fidelity. Introducing deterministic acceleration sampling, we expe-dite the sampling process by uniformly selecting time steps. Comprehensive testing on synthetic and real datasets demonstrates that the proposed method achieves deraining for images of any size, yielding significant improvements in peak signal-to-noise ra-tio and structural similarity. The generated images preserve more complete details, showing superior deraining efficacy. © 2024 ieee.

关键词： image denoising

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YOLO-SG: SALIENCE-GUIDED DETECTION OF SMALL OBJECTS IN MEDICAL imageS 29

YOLO-SG: SALIENCE-GUIDED DETECTION OF SMALL OBJECTS IN MEDIC...

引用

ieee international conference on image processing (ICIP)

作者： Han, Rong Liu, Xiaohong Chen, Ting Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Object detection, a crucial component of medical image analysis, provides physicians with an interpretable auxiliary diagnostic basis. Although existing object detection models have had great success with natural images, the growing resolution of medical images makes the problem especially challenging because of the increased expectations to exploit the image details and discover small targets in images. For instance, lesions are occasionally diminutive relative to high-resolution medical images. To address this problem, we present YOLO-SG, a salience-guided (SG) deep learning model that improves small object detection by attending to detailed regions via a generated salience map. YOLO-SG performs two rounds of detection: coarse detection and salience-guided detection. In the first round of coarse detection, YOLO-SG detects objects using a deep convolutional detection model and proposes a salience map utilizing the context surrounding objects to guide the subsequent round of detection. In the second round, YOLO-SG extracts salient regions from the original input image based on the generated salience map and combines local detail with global context information to improve the object detection performance. The experimental results demonstrate that YOLO-SG outperforms the state-of-the-art models, especially when detecting small objects.

关键词： medical image analysis salience map high-resolution image object detection

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Advancements in Natural Language Understanding and applications: A Study on ChatGPT with Novel Approaches to Fairness and Accuracy in Education 4

Advancements in Natural Language Understanding and Applicati...

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4th international signal processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Guo, Siyuan Johns Hopkins University 3501 SAINT PAUL ST APT 836 Baltimore BaltimoreMD21218-2742 United States

ISBN: (纸本)9798331528676

Natural Language processing (NLP) has evolved significantly since the 1950s, with early research focusing on tasks like machine translation, information retrieval, and text summarization. Initially, most NLP research emphasized syntactic analysis, but the challenge of understanding and processing unstructured text remained. Recent advancements in large-scale language models (LLMs), such as ChatGPT, have revolutionized the field by enabling zero-shot execution of various NLP tasks without task-specific training. ChatGPT, based on the Transformer architecture, represents a major breakthrough in NLP, generating high-quality natural language responses and self-correcting based on dialogue context. This paper investigates the applications of ChatGPT in educational tasks, focusing on enhancing the fairness and accuracy of its responses. Fairness refers to ensuring that users receive consistent and unbiased answers to the same queries, while accuracy involves improving the precision of ChatGPT's outputs, particularly in complex educational tasks such as scientific literature review and content comprehension. We propose novel solutions, including dynamic model weighting for fairness and integrating external knowledge bases to boost accuracy. Experiments conducted on educational tasks demonstrate significant improvements in both fairness and accuracy, showing that the proposed methods enable ChatGPT to provide more reliable and consistent educational support. Our findings suggest that, with these innovations, ChatGPT can play a pivotal role in improving educational outcomes by offering fair access to quality learning content while addressing accuracy challenges. Future work will explore expanding this approach to other fields where NLP plays a crucial role. ©2024 ieee.

关键词： Zero-shot learning

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CCL: CLASS-WISE CURRICULUM LEARNING FOR CLASS IMBALANCE PROBLEMS. 29

CCL: CLASS-WISE CURRICULUM LEARNING FOR CLASS IMBALANCE PROB...

引用

ieee international conference on image processing (ICIP)

作者： Escudero-Vinolo, Marcos Lopez-Cifuentes, Alejandro Univ Autonoma Madrid Video Proc & Understanding Lab Madrid Spain

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Computer vision datasets usually present long-tailed training distributions where the classes are not represented with the same number of training samples. This so-called class imbalance problem hinders the proper learning of inference models, biasing them towards over-represented classes and decreasing their generalization. Adopted solutions to tackle the effect of class imbalance are based on weighting the training loss according to the number of class samples, leading to regimes where low-represented classes guide the learning just accounting for their cardinal number. To also incorporate class complexity in the process, we propose a novel training scheme called CCL: Class-wise Curriculum Learning. Classes are first sorted based on a difficulty criterion which not only accounts for the number of training samples but also for their training outcomes. The curriculum is then used to guide the training: easy classes are fed first and-incrementally, the more difficult ones are added. The proposed approach is validated for image classification using long-tailed datasets. Results show that when the proposed Class-wise Curriculum Learning scheme is used, trained models outperform specific state-of-the-art methods devoted to handle the class imbalance problem. The code, data and reported models described along this paper are publicly available at https://***/vpulab/CCL

关键词： Class imbalance Curriculum learning Sample scoring Training pace image Classification

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Book Cover Synthesis from the Summary 19

Book Cover Synthesis from the Summary

引用

19th ieee/ACS international conference on Computer Systems and applications (AICCSA)

作者： Haque, Emdadul Khan, Md Faraz Kabir Jubair, Mohammad Imrul Anjum, Jarin Niloy, Abrar Zahir Ahsanullah Univ Sci & Technol Dhaka Bangladesh Univ Colorado Boulder CO 80309 USA

ISBN: (纸本)9798350310085

The cover is the face of a book and is a point of attraction for the readers. Designing book covers is an essential task in the publishing industry. One of the main challenges in creating a book cover is representing the theme of the book's content in a single image. In this research, we explore ways to produce a book cover using artificial intelligence based on the fact that there exists a relationship between the summary of the book and its cover. Our key motivation is the application of text-to-image synthesis methods to generate images from given text or captions. We explore several existing text-to-image conversion techniques for this purpose and propose an approach to exploit these frameworks for producing book covers from provided summaries. We construct a dataset of English books that contains a large number of samples of summaries of existing books and their cover images. In this paper, we describe our approach to collecting, organizing, and pre-processing the dataset to use it for training models. We apply different text-to-image synthesis techniques to generate book covers from the summary and exhibit the results in this paper.

关键词： Book cover synthesis Book summary Generative Adversarial Networks Text-to-image synthesis

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Principles of Diffusion Models and Their applications on Medicine 13

Principles of Diffusion Models and Their Applications on Med...

引用

13th international conference on Information Technology in Medicine and Education, ITME 2023

作者： Wang, Guangyi Cai, Yuren Liu, Pei Xia, Jincun Su, Songzhi Xiamen University Department of Artificial Intelligence Xiamen China Xiamen Medical College Departrnent of Public Health and Medical Technology Xiamen China

ISBN: (纸本)9798350319156

Diffusion models, as a paradigm of generative models, have achieved unprecedented success in the field of image generation since 2020, owing to their advantage of simplicity in optimization. They have been widely applied to various tasks in fields such as image, text, and speech processing. Notably, due to a series of breakthroughs achieved by diffusion models in natural image generation, the field of medical imaging has actively explored the potential of diffusion models. This paper begins with a detailed introduction to the principles of diffusion models. It then categorizes the applications of diffusion models in the field of medical imaging into various tasks, including generation, 3D reconstruction, segmentation, image-to-image translation, and classification, highlighting the main methods for each task. Finally, the paper summarizes the research progress of diffusion models in the field of medical imaging, discusses key challenges and open issues, and proposes promising directions for future improvements. © 2023 ieee.

关键词： image segmentation

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Fusing IP vendor Palmprint Biometric with Encoded Hash for Hardware IP Core Protection of image processing Filters

Fusing IP vendor Palmprint Biometric with Encoded Hash for H...

引用

2023 international conference on Microelectronics, ICM 2023

作者： Sengupta, Anirban Anshul, Aditya Thakur, Sumer Kothari, Chirag Indian Institute of Technology Indore Indian Statistical Institute Kolkata Computer Science and Engg. India Indian Institute of Technology Indore Computer Science and Engg. India Indian Institute of Technology Indore India

ISBN: (纸本)9798350380828

image processing filters offer several significant applications, making them a crucial component of various consumer electronics and multimedia systems. These image filters are designed as dedicated reusable intellectual property (IP) cores using high level synthesis (HLS) framework. These image processing hardware IPs are essential components of several system-on-chips (SoCs) used in mission-critical applications. Therefore, protecting these image filter hardware IPs from an IP vendor's perspective is crucial against an adversary's false IP ownership claim in the context of globalized design supply chain. This paper presents a novel hardware IP protection (IPP) technique by fusing the IP vendor's palmprint biometric and his/her encoded hash to generate secret security constraints for embedding. This embedded security mark acts as a detective countermeasure during the IP conflict resolution process. The proposed approach outperforms recent IPP techniques, as evident by a lower probability of coincidence, stronger tamper tolerance, and stronger entropy value. © 2023 ieee.

关键词： Anthropometry

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STVP: A Spatiotemporal Visual Perception Method for User-generated Content Video Quality Assessment

STVP: A Spatiotemporal Visual Perception Method for User-gen...

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2023 ieee international conference on Visual Communications and image processing, VCIP 2023

作者： Ai, Da Lu, Mingyue Wang, Jiahao Liu, Ying Xi'An University of Posts and Telecommunications Center for Image and Information Processing Xi'an China Xi'An University of Posts and Telecommunications School of Communication and Information Engineering Xi'an China

ISBN: (纸本)9798350359855

With the popularity and development of short video applications, the behavior of using mobile devices to shoot and share user-generated content (UGC) videos has become increasingly common. Video quality assessment (VQA) is critical in guaranteeing end-user viewing experiences. UGC-VQA is a challenging problem due to the complexity and variety of distortion types of UGC videos and the absence of reference videos. To improve the consistency of UGC-VQA results and human subjective ratings, in this paper, we propose a UGC-VQA method based on spatiotemporal visual perception (STVP). Firstly, a hierarchical feature fusion module was added to the feature extraction network to realize the fusion of low-level visual features and high-level semantic features, and obtain the quality perception features with rich visual information. Then, we use the self-Attention to weight different frames to distinguish their importance. The long short-Term memory (LSTM) network and the time pool are used to model long-Term dependencies and temporal memory effects. Experimental results on UGC-VQA datasets show that the proposed method achieves a performance improvement of nearly 2%, and its evaluation results are more consistent with human visual perception. © 2023 ieee.

关键词： Semantics

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Sugarcane Classification of image processing and Convolutional Neural Networks 2

Sugarcane Classification of Image Processing and Convolution...

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2nd international conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023

作者： Kavitha, K. Umasankari, E. IFET College of Engineering Dept of Computer Science Villupuram India

ISBN: (纸本)9798350348002

Sugarcane is a significant crop with several uses in the food, bio-energy, and bio-based product sectors. Many elements, including climate, soil fertility, and plant diseases, can have an impact on the quality of sugarcane. In this study, it suggest a sugarcane classification system based on convolutional neural networks (CNNs) and image processing to automatically evaluate sugarcane crops' quality automatically. Using image processing techniques, the system uses digital photographs of sugarcane to extract attributes including color, texture, and form. These attributes are then sent into a CNN to be categorized into various grades or quality levels. By testing the proposed system using a sizable collection of photographs of sugarcane, comparing the outcomes to those attained using conventional visual inspection techniques. The findings indicate that the suggested system can classify sugarcane crops with a high degree of accuracy and dependability. The sugarcane industry may employ the suggested approach to increase the efficacy and precision of sugarcane quality evaluation. This research highlights the possibility of fusing deep learning models with image processing methods for agricultural applications. © 2023 ieee.

关键词： Support vector machines

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