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Mask Inpaints Using Downsampled Fast Fourier Convolution

Mask Inpaints Using Downsampled Fast Fourier Convolution

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symposium of Image, signal processing, and Artificial vision (STSIVA)

作者： Xiaoyang Gao Yifan Huang Penghui Gao Yong Zhang Tao Yang School of Information and Intelligence Engineering University of Sanya Hainan China Academician Workstation of Chumming Rong University of Sanya Hainan China

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

In recent years, image inpainting has seen significant advancements, with the LaMa model., introduced in 2021, standing out for its improvements in large-area inpainting. Yet, challenges such as inpainting complex geometries, processing high-resolution images swiftly, and achieving realism in filled areas persist. Existing solutions that incorporate diffusion models to enhance realism are hardware-intensive and unsuitable for high-resolution images. We propose a new image inpainting method, Downsampled Fast Fourier Convolution (DFFC), which addresses these challenges. Unlike LaMa's reliance on Fast Fourier Convolution (FFC) for early-stage global context capture without explicit downsampling, DFFC introduces a deep learning-driven downsampling module to reduce computational complexity and improve high-resolution image processing efficiency. This module allows for data volume reduction during processing while preserving global context, comprising: i) deep learning-based image downsampling, ii) an FFC-based inpainting architecture, iii) a high-perceptual-domain loss function, and iv) dynamic large-area mask training. Our method maintains the original model's performance, enhances processing speed, and reduces computational load.

关键词： Training Geometry Convolution Image processing computational modeling Computer architecture Diffusion models Informatics computational complexity Load modeling

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Computer vision for Astronomical Image Analysis

Computer Vision for Astronomical Image Analysis

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symposium of Image, signal processing, and Artificial vision (STSIVA)

作者： Sotiria Karypidou Ilias Georgousis George A. Papakostas MLV Research Group International Hellenic University Kavala Greece

ISBN: (纸本)9781665426565

Computer vision (CV) is undoubtedly one of the most popular forms of Artificial intelligence (AI) and its implementation has gained considerable ground in all aspects of our lives, from security and automotive, to the night sky observation and astronomy. In general, CV uses pattern recognition techniques for identifying objects in visual media (both static and moving images). The current archetype in CV is largely based on supervised AI, which uses large data sets of human-labelled images for training. Machine Learning (ML) and Deep Learning (DL) models in computer vision have undergone a period of extremely rapid development in recent past years; in particular for object recognition and localisation tasks. An area of study with great interest in practical applications that concerns this essay, is astronomical images analysis. However, one of the main challenges facing researchers these days is the existence of large quantities of annotated data sets, in the appropriate resolution and scale. This challenge consequently asks for huge amounts of storage and high computational power. In this paper, we systematically review and analyze different challenges faced by astronomers and continue with state-of-the-art methodologies that were conducted over the last decade.

关键词： Deep learning Training Computer vision Analytical models Visualization Image analysis Image resolution

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Part-level Action Parsing via a Pose-guided Coarse-to-Fine Framework

Part-level Action Parsing via a Pose-guided Coarse-to-Fine F...

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ieee International symposium on Circuits and Systems

作者： Xiaodong Chen Xinchen Liu Wu Liu Kun Liu Dong Wu Yongdong Zhang Tao Mei Universiy of Science nd Technoogy of Chin Hefei ChinJD.co Beijing ChinJD.co Beijing ChinJD.co Beijing ChinJD.co Beijing ChinUniversiy of Science nd Technoogy of Chin Hefei ChinJD.co Beijing Chin

ISBN: (数字)9781665484855

ISBN: (纸本)9781665484862

Action recognition from videos, i.e., classifying a video into one of the pre-defined action types, has been a popular topic in the communities of artificial intelligence, multimedia, and signal processing. However, existing methods usually consider an input video as a whole and learn models, e.g., Convolutional Neural Networks (CNNs), with coarse video-level class labels. These methods can only output an action class for the video, but cannot provide fine-grained and explainable cues to answer why the video shows a specific action. Therefore, researchers start to focus on a new task, Part-level Action Parsing (PAP), which aims to not only predict the video-level action but also recognize the frame-level fine-grained actions or interactions of body parts for each person in the video. To this end, we propose a coarse-to-fine framework for this challenging task. In particular, our framework first predicts the video-level class of the input video, then localizes the body parts and predicts the part-level action. Moreover, to balance the accuracy and computation in part-level action parsing, we propose to recognize the part-level actions by segment-level features. Furthermore, to overcome the ambiguity of body parts, we propose a pose-guided positional embedding method to accurately localize body parts. Through comprehensive experiments on a large-scale dataset, i.e., Kinetics-TPS, our framework achieves state-of-the-art performance and outperforms existing methods over 31.10% ROC score.

关键词： Circuits and systems Detectors Streaming media signal processing computational efficiency Convolutional neural networks Task analysis

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Improved Whale Optimization Algorithm via Cellular Automata

Improved Whale Optimization Algorithm via Cellular Automata

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symposium of Image, signal processing, and Artificial vision (STSIVA)

作者： Yuchao Gao Cheng Qian Zhenghang Tao Hu Zhou Jinran Wu Yang Yang College of Automation and College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing China School of Mathematical Sciences Queensland University of Technology Brisbane Australia

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

The idea of whale optimization algorithm comes from the unique hunting behavior of whales in the ocean. The whale optimization algorithm realizes the optimization of search by surrounding and attacking prey with bubbles. The algorithm has the characteristics of simple principles, good operation, easy implementation with few parameters and strong robustness. However, there are still some problems of its exploitation (convergence speed) in local space. To this end, we propose a new improved whale algorithm, which is based on cellular automata to find the optimal position, namely CA-WOA. Here, the cellular automata is introduced to enhance the local search ability and to speed up the convergence. According to experiments on seven benchmark functions and three truss design projects, we show that the proposed CA-WOA can obtain a better optimal solution with less computational cost.

关键词： Algorithms Whales Automata Benchmark testing Robustness Optimization Convergence

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Deeply Feature Fused Video Super-resolution Network

Deeply Feature Fused Video Super-resolution Network

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symposium of Image, signal processing, and Artificial vision (STSIVA)

作者： Jingmin Yang Zhensen Chen Li Xu School of Computer Science Minnan Normal University Zhang Zhou P. R. China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University Fuzhou P. R. China Key Laboratory of Data Science and Intelligence Application Fujian Province University Zhangzhou Fujian P. R. China

ISBN: (纸本)9781665426565

The video super-resolution (VSR) task refers to the use of corresponding low-resolution (LR) frames and multiple neighboring frames to generate high-resolution (HR) frames. An important step in VSR is to fuse the features of the reference frame with the features of the supporting frame. The existing VSR method does not make full use of the information provided by the distant neighboring frame, and usually fuses in a one-stage manner. In this paper, we propose a deep fusion video super-resolution network based on temporal grouping. We divide the input sequence into groups according to different frame rates to provide more accurate supplementary information, and the method aggregates temporal and spatial information at different stages of fusion.

关键词： Fuses Conferences computational modeling Aggregates Superresolution Task analysis Spatial resolution

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Constructing Multi-granular Knowledge Graph with Structure Similarity and Hierarchical Clustering

Constructing Multi-granular Knowledge Graph with Structure S...

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symposium of Image, signal processing, and Artificial vision (STSIVA)

作者： Ke Yang Jingyun Feng Zhiyu Xi Qiuke Zheng Huan Zhang Wei Deng China Railway Eryuan Engineering Group Co. Ltd. Chengdu China Dept. of Statistics Southern University of Science and Technology Shenzhen China School of Automation Science and Electrical Engineering Beihang University Beijing China School of Computer Science and Artificial Intelligence Southwestern University of Finance and Economics Chengdu China School of Statistics Southwestern University of Finance and Economics Chengdu China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

The structure of knowledge graph is basically flat and lack of hierarchies to make knowledge graph too general to deal with concrete reasoning problems. There are few work about hierarchical knowledge graph. In this paper, the entity and relation combined method is proposed to divide the entities into groups based on structure similarity. The hierarchical cluster method is proposed with the entity and relation combined embedding representation to construct a multi-granular knowledge graph. The experimental results show that the hierarchical division relied on combined embedding are significantly better than those based on the existing entity embedding methods in three evaluation indexes. The combined embedding can capture the structure information which is helpful to hierarchical knowledge graph accurately and reasonably.

关键词： Accuracy Large language models Clustering algorithms Knowledge graphs Cognition Classification algorithms Indexes Informatics

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Collaborative Filtering of Call for Papers

Collaborative Filtering of Call for Papers

引用

ieee symposium Series on computational intelligence

作者： He-Da Wang Ji Wu Multimedia Signal and Intelligent Information Processing Laboratory Department of Electronic Engineering Tsinghua University

ISBN: (纸本)9781479975617

Call for papers (CFPs) are notifications of academic events that invite researchers to submit their works. Traditionally, CFPs are handed out to researchers by mailing lists and web pages. With the number of conferences increasing, finding, reading and filtering out relevant CFPs become time consuming and need the assistance from information retrieval techniques. In this paper, we employ collaborative filtering to match relevant CFPs to researchers. Non-personalized, neighborhood-based and class-based methods are applied in CFP recommendation. We also propose a hybrid approach that utilizes conference series and submission deadlines of CFPs. The experiments on WikiCFP data set show that the class-based method outperforms both neighborhood-based and non-personalized methods, whereas the proposed hybrid approach has the best overall performance.

关键词： collaborative filtering paper Mailing lists Web pages HYBRID

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Optimizing Seed Set for New User Cold Start

Optimizing Seed Set for New User Cold Start

引用

ieee symposium Series on computational intelligence

作者： He-Da Wang Ji Wu Multimedia Signal and Intelligent Information Processing Laboratory Department of Electronic Engineering Tsinghua University

ISBN: (纸本)9781479975617

Users newly enter a recommender system can not get personalized recommendation due to the lack of personal profiles. An interview process that asks new users to rate a set of items (the seed set) will help user modeling and improve user experience. Traditional seed set generation approaches often concentrate on item-wise properties instead of aiming at finding the optimal seed set. We propose a simple random optimization technique to search for the optimal seed set, which considers the seed set as a whole and performs a random search by reducing the prediction error on validation set. By off-line experiments on the MovieLens 10M data set, we show that the proposed approach performs as well as the state-of-the-art method called GreedyExtend, and the proposed approach needs significantly less computational cost to reach the same prediction error as the best baseline on validation set.

关键词： Seed set Error of prediction Recommender systems user modelling Cold starting TEST SETS Consumers

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LEECAU-Net: A Latent Entropy-Quantized and Efficient Channel Attention-Aided U-Net Model for Microscopic Medical Image Segmentation

LEECAU-Net: A Latent Entropy-Quantized and Efficient Channel...

引用

symposium of Image, signal processing, and Artificial vision (STSIVA)

作者： Sanchita Das Gouranga Maity Diptarka Mandal Dmitrii Kaplun Alexander Voznesensky Ram Sarkar Dept. of CSE Jadavpur University Kolkata India Department of Applied Mathematics Defence Instituite of Advanced Technology Pune India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Dept. of Automation and Control Process St. Petersburg Electrotechnical University “LETI” St. Petersburg Russian Federation

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

Microscopic medical image segmentation is essential for detailed cellular-level analysis in computational pathology, offering insights into disease mechanisms. However, this task is particularly challenging due to the high similarity between different cellular classes and variability within each class, especially in densely packed or overlapping cell regions. To this end, we present LEECAU-Net (Latent Entropy-quantized and Efficient Channel Attention-aided U-Net), a U-Net-based model designed to improve segmentation accuracy through a dual-attention mechanism. Built on an Inceptionv4 backbone, LEECAU-Net's encoder integrates Latent Entropy-quantized Channel Attention (LECA) and Efficient Channel Attention (ECA) modules. The ECA module enhances the network's ability to focus on essential channel dependencies, while LECA leverages entropy quantification to prioritize channels rich in information, leading to better differentiation between similar structures. We have tested LEECAU-Net on three challenging publicly available datasets: Triple Negative Breast Cancer (TNBC), Multi-organ Nuclei Segmentation (MoNuSeg), and Cervical Nucleus Segmentation (CNS). Across these datasets, our model achieves higher performance metrics than numerous state-of-the-art approaches reported in the literature. The improved results underscore the effectiveness of LEECAU-Net's architecture in handling the complexity of microscopic medical images, making it a valuable tool for advancing automated cellular analysis in biomedical research. The source code and more results are made available at https://***/Cmatermedicalimageanalysis/LEECAU-Net.

关键词： Image segmentation Accuracy Microscopy computational modeling Biological system modeling Source coding Computer architecture Entropy Robustness Medical diagnostic imaging

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An Inter-Layer Volumetric Attention Network for Teeth Segmentation in Cross-FOV CBCT Dental Images

An Inter-Layer Volumetric Attention Network for Teeth Segmen...

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symposium of Image, signal processing, and Artificial vision (STSIVA)

作者： Fan Qin Xiaoguang Li Muqing Liu Zhiwei Huang Jianhui Han Li Zhuo School of Information Science Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China Department of Oral & Maxillofacial Radiology Peking University School & Hospital of Stomatology Beijing China Department of Oral and Maxillofacial Radiology School of Stomatology Capital Medical University Beijing China

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

Accurate teeth segmentation is the foundation of intelligent analysis in dental CBCT images and has significant application value for oral clinical practice. The existing teeth segmentation algorithms mainly focus on the oral image with a global field of view. However, in clinical data, CBCT oral data has significant differences in the field of views. This paper proposes a cross-view oral CBCT teeth segmentation network based on an inter-layer volumetric attention network to address the challenges of different perspectives. Firstly, a 3D inter-layer cross-attention mechanism was designed to handle different visual field features effectively. Secondly, a target scale normalization strategy based on physical resolution is introduced to align teeth in various field of views. Finally, a local random cropping training strategy is adopted to improve the robustness of the teeth segmentation network. In addition, this paper also excludes areas unrelated to teeth through ROI detection. The experimental results show that the proposed method achieves robust and accurate cross-field segmentation in clinical CBCT oral images.

关键词： Training Location awareness Image segmentation Visualization Accuracy Attention mechanisms Three-dimensional displays Teeth Robustness Dentistry

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