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Underwater Salient Object Detection Based on Swintransformer 5th

Underwater Salient Object Detection Based on Swintransformer

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5th China Intelligent Robotics Academic conference

作者： Jiao, Zhan Wang, Ruizi Fu, Bo Zhang, Tianzhuang Zhang, Ning Liaoning Vocat Coll Light Ind Dalian 116100 Peoples R China Liaoning Normal Univ Sch Comp Sci & Artificial Intelligence Dalian 116081 Peoples R China Northeast Normal Univ Training Ctr Minist Educ Studying Overseas Changchun Peoples R China

ISBN: (纸本)9789819616138;9789819616145

Underwater Salient Object Detection (USOD) has emerged as a specialized division of Salient Object Detection (SOD) to meet the increasing demand for underwater exploration. However, two major challenges hinder its development. First, current USOD datasets are limited in size and diversity, making them insufficient for robust model training and evaluation. Second, most existing deep learning-based USOD approaches rely on convolutional encoder-decoder architectures, which improve inference over traditional methods but still suffer from limited receptive fields and high computational costs. To address these challenges, we introduce a new USOD method based on the Swin transformer, which incorporates a multimodal feature fusion module to increase detection accuracy. Our model uses RGB images and depth maps to capture both semantic information and visual details. The multimodal feature fusion module dynamically adjusts feature importance through attention mechanisms, enhancing the ability of the model to extract meaningful information. By using a Swin transformer model with pretrained weights as the encoder backbone, the proposed method mitigates data scarcity issues and captures both the global context and long-range dependencies. The experimental results demonstrate that our approach achieves significant improvements in detecting salient objects in underwater environments.

关键词： Underwater image Salient object detection Swin transformer

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ATTIQA: Generalizable image Quality Feature Extractor Using Attribute-Aware Pretraining 17th

ATTIQA: Generalizable Image Quality Feature Extractor Using ...

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17th Asian conference on Computer Vision, ACCV 2024

作者： Kwon, Daekyu Kim, Dongyoung Ki, Sehwan Jo, Younghyun Lee, Hyong-Euk Kim, Seon Joo Yonsei University Seoul Korea Republic of Samsung Advanced Institue of Technology Suwon-si Korea Republic of

ISBN: (纸本)9789819609109

In no-reference image quality assessment (NR-IQA), the challenge of limited dataset sizes hampers the development of robust and generalizable models. Conventional methods address this issue by utilizing large datasets to extract rich representations for IQA. Also, some approaches propose vision language models (VLM) based IQA, but the domain gap between generic VLM and IQA constrains their scalability. In this work, we propose a novel pretraining framework that constructs a generalizable representation for IQA by selectively extracting quality-related knowledge from VLM and leveraging the scalability of large datasets. Specifically, we select optimal text prompts for five representative image quality attributes and use VLM to generate pseudo-labels. Numerous attribute-aware pseudo-labels can be generated with large image datasets, allowing our IQA model to learn rich representations about image quality. Our approach achieves state-of-the-art performance on multiple IQA datasets and exhibits remarkable generalization capabilities. Leveraging these strengths, we propose several applications, such as evaluating image generation models and training image enhancement models, demonstrating our model’s real-world applicability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： visual languages

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image Captioning Using VGG-16 and VGG-19: A Mixed Model Approach

Image Captioning Using VGG-16 and VGG-19: A Mixed Model Appr...

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International conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Dhar, Moloy Sen, Mrinmoy Chakraborty, Bidesh Sen, Shibaprasad Dholey, Milan Kumar Dey, Soumyanil Bhattacherjee, Soham Guru Nanak Institute of Technology Panihati India Haldia Institute of Technology Haldia India MCKV Institute of Engineering Howrah India Hyderabad India

ISBN: (纸本)9789819613472

In modern years, with concurrent development in the adoption of various social platform stages, image captioning is dominant in naturally defining the entire image into a sentence of natural language form. Captioning images (CIs) play a significant part in the computer-established community. CI is the method of naturally producing the regular language detailed document depiction of the image utilizing Artificial Intelligence (AI) methods. Computer vision (CV), natural language processing (NLP) is critical facets of image clarification scheme. Convolution Neural Network (CNN) is a component of CV, recycled characteristic extraction, object discovery, alternative NLP methods aid in producing the caption of the image in textual form. Reproducing acceptable image depiction by machine is a demanding exercise as it is situated at the time of object discovery, area, and semantic communications in human-intelligible expression as English. Our paper aims to establish an encoder-decoder (E-D) located hybrid captioning of image that utilize VGG-16, VGG-19, ResNet50, YOLO. ResNet50, VGG-16 are pre-prepared characteristic eradication models on numerous (more than 100,000) of images. YOLO is worn for actual time for action or event of object discovery. First, it excerpts the characteristics of images using VGG-19, VGG-16, YOLO, and ResNet50 and connects the outcome into an individual file. Finally, BiGRU and LSTM are worn for detailed analysis of document depiction of image. The suggested model is computed utilizing ROUGE, METEOR, BLEU scores. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Long short-term memory

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9th International conference on image processing and communications, IP and C 2017

9th International Conference on Image Processing and Communi...

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9th International conference on image processing and communications, IP and C 2017

ISBN: (纸本)9783319687193

The proceedings contain 22 papers. The special focus in this conference is on image processing and communications. The topics include: Binary line oriented histogram;image search enhanced by using external data sources and reasoning;linguistic description of images based on fuzzy histograms;using toboggan segmentation in detection of centers and radius of cell nuclei;evaluation of the pre-processing methods in image-based palmprint biometrics;on the way to perfect steganography;PET waste classification method and plastic waste database – wadaba;estimation of geometrical deformations of 3D prints using local cross-correlation and monte carlo sampling;quality assessment of 3D printed surfaces in fourier domain;the adaboost algorithm with linear modification of the weights;evaluation of hashing methods performance on binary feature descriptors;efficient parallelization methods of labeling algorithm;modeling telecommunication networks with the use of reference graphs;QoS concept for protecting network resources - a case study;acceleration of genome sequencing with intelligent cloud brokers;learning management systems on blended learning courses;technology and software for calculating correct normalization of correlation functions;running sports decision aid tool based on reinforcement learning approach;framework for evaluating QoE for remote control of autonomous cars in mobile wireless networks;practical advices for university teachers and comparison of visual descriptors for 3D reconstruction of non-rigid planar surfaces.

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M-RAT: a Multi-grained Retrieval Augmentation Transformer for image Captioning 17th

M-RAT: a Multi-grained Retrieval Augmentation Transformer f...

引用

17th Asian conference on Computer Vision, ACCV 2024

作者： Song, Jiayan Pan, Renjie Zhou, Jun Yang, Hua Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Lab of Digital Media Processing and Transmission Shanghai200240 China

ISBN: (纸本)9789819609079

Current encoder-decoder methods for image captioning mai-nly consist of an object detection module (two-stage), or rely on big models with large-scale datasets to improve the effectiveness, which leads to increasing computation costs and cannot introduce new external knowledge. In this paper, we propose a novel end-to-end method Multi-grained Retrieval Augmentation Transformer (M-RAT) that innovatively fuses retrieved text derived from a changeable datastore with input visual feature through a Multi-modal Aligned Encoder, and introduce a specialized attention mechanism, Multi-MSA, to exploit both local and global interactions for delicate fine-grained details. Additionally, we enhance the decoder generation ability by employing low-level and high-level fused embeddings. Experiments demonstrate that M-RAT achieves comparable performance to state-of-the-art baselines with remarkable accuracy and details, as well as showing excellent domain adaptability for novel objects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decoding

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5th International conference in image processing and communications, IP&C 2013

5th International Conference in Image Processing and Communi...

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5th International conference in image processing and communications, IP&C 2013

ISBN: (纸本)9783319016214

The proceedings contain 52 papers. The special focus in this conference is on image processing and communications. The topics include: Adaptive windowed threshold for box counting algorithm in cytoscreening applications;corneal endothelial grid structure factor based on coefficient of variation of the cell sides lengths;a distributed approach for development of deformable model-based segmentation methods;enhancement of low-dose CT brain scans using graph-based anisotropic interpolation;using of EM algorithm to image reconstruction problem with tomography noises;the perception of humanoid robot by human;a fast histogram estimation based on the Monte Carlo method for image binarization;adaptation of the combined image similarity index for video sequences;time-frequency analysis of image based on stockwell transform;2DKLT-based color image watermarking;knowledge transformations applied in image classification task;problems of infrared and visible-light images automatic registration;semantics driven table understanding in born-digital documents;contextual possibilistic knowledge diffusion for images classification;image feature extraction using compressive sensing;a modification of the parallel spline interpolation algorithms;local Eigen background substraction;polarization imaging in 3D shape reconstrucion;layer image components geometry optimization;extraction of data from limnigraf chart images;a simplified visual cortex model for efficient image codding and object recognition;fuzzy rule-based systems for optimizing power consumption in data centers;an algorithm for finding shortest path tree using ant colony optimization metaheuristic and a sensor network gateway for IP network and SQL database.

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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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International conference on image processing and communications, IP and C 2015

引用

International conference on image processing and communications, IPandC 2015

ISBN: (纸本)9783319238135

The proceedings contain 34 papers. The special focus in this conference is on image processing and communications. The topics include: Classifier selection uses decision profiles in binary classification task;2DHMM-based face recognition method;corner detection based on directional gradients;sensor fusion enhancement for mobile positioning systems;thermal face recognition;feature reduction using similarity measure in object detector learning with haar-like features;assessment of the brain coverage ratio in the postoperative craniosynostosis based on 3D CT scans;combined imaging system for taking facial portraits in visible and thermal spectra;the PUT surveillance database;automatic analysis of vehicle trajectory applied to visual surveillance;algorithmically optimized AVC video encoder with parallel processing of data;neural video compression based on SURF scene change detection algorithm;cell detection in corneal endothelial images using directional filters;the method of probabilistic nodes combination in 2D information retrieval, pattern recognition and biometric modeling;3-D reconstruction of real objects using an android device;methods of natural image preprocessing supporting the automatic text recognition using the OCR algorithms;fast machine vision line detection for mobile robot navigation in dark environments;adjustment of viterbi algorithm for line following robots;studentized range for spatio-temporal track-before-detect algorithm;real-time US image enhancement by forward-backward diffusion using GPU;face-based distributed visitor identification system;color space optimization for lacunarity method in analysis of papanicolaou smears and toward texture-based 3D level set image segmentation.

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Bibliometric Analysis of Road Distress Detection Based on Citespace 24

Bibliometric Analysis of Road Distress Detection Based on Ci...

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5th International conference on Computer Science and Management Technology, ICCSMT 2024

作者： Li, Yaoye Zhang, Yinbo He, Rong Ma, Lianxia Chen, Fang Xinjiang communications Construction Group Co. Ltd. Xinjiang Urumqi China Xinjiang Key Laboratory of Transportation Prefabricated Construction and Maintenance Xinjiang Urumqi China Xinjiang University Xinjiang Urumqi China

ISBN: (纸本)9798400709999

To analyze the research progress in the field of road disease detection, this article searched the Web of Science Core Collection database for papers related to road disease detection from 2000 to *** CiteSpace software, a visual analysis was conducted on the 247 retrieved documents, focusing on annual publication numbers, publishing countries, core institutions and authors, keywords, burst words, and timeline mapping to explore the current status and trends in this *** findings indicate that since 2004, the overall research trend in road disease detection has been on an upward trajectory, with a notable increase in the number of research papers published in 2021, reflecting a significant surge in research ***, China, the United States, and Italy have been the most prolific in research *** leading research institutions are primarily scientific and technological academies, with Chang'an University taking a dominant position in the field of road disease *** keyword co-occurrence analysis, burst detection, and keyword clustering analysis, it was determined that disease detection technology, deep learning and machine learning, image processing and intelligent analysis, and system integration and development have been the research hotspots in the road disease detection field from 2000 to *** is anticipated that future research will evolve towards the directions of performance evaluation, multimodal data fusion, and real-time monitoring and analysis. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Data fusion

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3rd International conference on Computing and Communication Networks, ICCCN 2023

3rd International Conference on Computing and Communication ...

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3rd International conference on Computing and Communication Networks, ICCCN 2023

ISBN: (纸本)9789819726707

The proceedings contain 60 papers. The special focus in this conference is on Computing and Communication Networks. The topics include: Enhancing Security in Wireless Sensor Networks: A Broadcast/Multicast Authentication Framework with Identity-Based Signature Schemes;TSFTO: A Two-Stage Fuzzy-Based Tasks Orchestration Algorithm for Edge and Fog Computing Environments;tracking Climatic Variations Through Smart IoT-Driven Approach: An Exploratory Analysis;Design and Manufacturing of an Efficient Low-Cost Fire Surveillance System Based on GSM communications and IoT Technology;a Comparative Analysis of Optimized Routing Protocols for High-Performance Mobile Ad Hoc Networks;integrating Sandpiper Optimization Algorithm and Secure Aware Routing Protocol for Efficient Cluster Head Selection in Wireless Sensor Networks;the Effects of Weather Conditions on Data Transmission in Free Space for Wireless Sensor Networks;Using Wireless Sensor Network Performance and Optimizing in Underground Mines with Virtual MIMO Antenna;a Novel Logistic Regression-Based Fire Detection Model Using IoT in Underground Coal Mines;An MQTT IoT Intrusion Detection System Using Deep-Learning;multi-objective Optimal Feature Selection for Cyber Security Integrated with Deep Learning;application of Gradient Boosting Classifier-Based Computational Intelligence to Detect Drug Addiction Threat in Society;insurance 4.0: Smart Insurance Technologies and Challenges Ahead;review on Vision Transformer for Satellite image Classification;primitive Roots and Euler Totient Function-Based Progressive visual Cryptography;approach for Fire Detection Using image processing;review of Various Approaches for Authorship Identification in Digital Forensics;machine Learning Model for Traffic Prediction and Pattern Extraction in High-Speed Optical Networks;improved Markov Decision Process in Wireless Sensor Network for Optimal Energy Consumption.

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