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XAI-driven Model Improvements in Interpretable image Segmentation 2

XAI-driven Model Improvements in Interpretable Image Segment...

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Joint of the 2nd World conference on eXplainable Artificial Intelligence Late-Breaking Work, Demos and Doctoral Consortium, xAI-2024:LB/D/DC

作者： Gipiškis, Rokas Vilnius University Institute of Data Science and Digital Technologies 4 Akademijos St Vilnius Lithuania

Semantic image segmentation is the most fine-grained task in computer vision. Its applications range from autonomous vehicles to medical imaging. Despite its deployments in critical areas, interpretable image segmentation remains an underexplored field, especially when compared to explainable AI (XAI) solutions in classification and object detection. Even less attention has been paid to the use of XAI in non-explainability-related scenarios, where XAI methods are applied not for interpretability per se, but rather for other instrumental reasons, such as improving a model’s performance. Such use cases can potentially extend to AI safety, specifically in the case of adversarial attacks, self-supervised learning, neural architecture search (NAS), and continual learning (CL). Most of these areas have never been investigated in the context of interpretable segmentation. This work outlines key developments in the field of interpretable image segmentation, with a particular focus on XAI-driven model improvements. We also consider potential uses of interpretable image segmentation for model compression in the case of NAS, and instance-based memory compression in the case of CL. © 2024 Copyright for this paper by its authors.

关键词： Self-supervised learning

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Application of Deep Learning Mathematical Approaches for image Correctness 2

Application of Deep Learning Mathematical Approaches for Ima...

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2nd International conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Ganesh, D. Sudarsanam, P. Bindu, T. Hima Babu, J. Suresh Thrinath, B. V. Sai Sai, S. Reddy Mohan Babu Univ Erstwhile Sree Vidyanikethan Engn Coll Autonomous Tirupati Andhra Pradesh India BMS Inst Technol & Management Dept MCA Bengaluru Karnataka India Lakireddy Bali Reddy Coll Engn EEE Mylavaram Andhra Pradesh India Mohan Babu Univ CA Erstwhile Sree Vidyanikethan Engn Coll Autonomous Tirupati Andhra Pradesh India Mohan Babu Univ EEE Erstwhile Sree Vidyanikethan Engn Coll Autonomous Tirupati Andhra Pradesh India Mohan Babu Univ Dept CA Erstwhile Sree Vidyanikethan Engn Coll Autonomous Tirupati Andhra Pradesh India

ISBN: (纸本)9798331540661;9798331540678

The "image Correctness" project presents an innovative methodology that uses deep learning techniques to use unique techniques to improve text clarity in noisy photos. The quality of visual data, especially text within images, is important in many areas of today's digital world, which includes document processing, identification of images, and preservation systems. However, noise, discoloration, and blurriness are common problems in real-world image capture, making it difficult to extract and interpret textual information effectively. In order to overcome these difficulties, the "image Correctness" project uses an auto-encoder neural network architecture, which is an effective method for unsupervised learning, for reconstructing text images with clear text from noisy ones. The input image gets compressed into a latent representation during the encoding stage of the autoencoder model. This representation is then used to rebuild the clear text image during the decoding stage. By using this method, the system can improve text clarity and effectively filter out noise, which increases the image's overall accuracy. In conclusion, the "image Correctness" study shows how deep learning may solve issues with image clarity in a significant way, with useful applications in a wide variety of fields. The project intends to provide users with enhanced tools for image processing, interpretation, and analysis by offering a reliable method for improving text clarity in noisy images.

关键词： Quality Assessment Artifact Detection Restoration Enhancement Semantic Editing Classification Text Enhancement Auto-encoder

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Representation Learning for Semantic Scene Understanding 2

Representation Learning for Semantic Scene Understanding

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2nd International conference on Hybrid Human-Artificial Intelligence (HHAI)

作者： Farshad, Azade Tech Univ Munich Munich Germany Munich Ctr Machine Learning MCML Munich Germany

ISBN: (纸本)9781643683942;9781643683959

Recent advances in semantic scene understanding have underscored its growing significance in the field of computer vision. Enhanced representations can be achieved by incorporating semantic information derived from textual data and applying it to generative models for scene modeling. Nevertheless, the features extracted from text prompts may not seamlessly model a scene. Scene graphs offer a robust solution to address this challenge, serving as a powerful representation for semantic image generation and manipulation. In this study, we delve into the utilization of scene graphs for this purpose and propose novel methodologies to augment both the representation and learning processes involved in image generation and manipulation. For image generation, we examine meta-learning for producing images in unprecedented scenes and refine the generated images using an autoregressive scene graph generation model. In terms of image manipulation, we put forth a novel self-supervised method that eliminates the need for paired before-and-after data. Additionally, we boost image manipulation performance by disentangling latent and graph representations in a self-supervised manner. By evaluating the efficacy of our proposed approaches on a diverse range of publicly available benchmarks, we demonstrate their superiority, ultimately achieving state-of-the-art performance in the domain of semantic image generation and manipulation.

关键词： Scene Graphs Representation Learning image Generation and Manipulation

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Generative Adversarial Networks (GANs) in Computer-Generated imagery 2

Generative Adversarial Networks (GANs) in Computer-Generated...

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2nd IEEE International conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Shah, Sannan Ahmad Drabu, Shahhab Hasin Khan, Murtaza Mahdi Kirmani, Dawar Bin Qayoom Singh, Shailendra Narayan Amity University Computer Science and Engineering Department Aset Noida India

ISBN: (纸本)9798350369960

In order to estimate the model using the method it is necessary to train two different models simultaneously. The first model, called the model (D) is responsible, for determining whether a particular sample is from the training data or generated by another model called G. On the hand G aims to understand the underlying distribution of the data. By training G to maximize the likelihood of D making mistakes a competitive environment is established. There are applications for G and D but one particularly notable solution emerges. D consistently evaluates any input as if it came from training data while G effectively captures the details of that training data distribution. Notably this system can be trained using known techniques like backpropagation when implementing G and D as multilayer perceptrons. This eliminates the need for inference networks or intricate Markov chains, during both training and sample generation processes. Thorough evaluations of the generated samples confirm the potential of this framework as demonstrated through testing. This innovative approach offers an efficient means to capture data distributions, which could potentially revolutionize generative modeling. © 2023 IEEE.

关键词： Deep learning Generative Adversarial Network (GAN) Generator and discriminator image classification image synthesis image generation Machine learning algorithms Synthetic image generation

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Second International conference on Environmental Remote Sensing and Geographic Information Technology, ERSGIT 2023

Second International Conference on Environmental Remote Sens...

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2023 2nd International conference on Environmental Remote Sensing and Geographic Information Technology, ERSGIT 2023

ISBN: (纸本)9781510672949

The proceedings contain 59 papers. The topics discussed include: external deformation monitoring method for rockfill dam： a case study of the Henan Tianchi pumped storage power station;study on multiple cropping index of North China plain based on LAI time series;research on the communication strategy of data hub;study on vegetation changes in Baoji City based on remote sensing images;inversion of sea surface ocean current in South China Sea, based on machine learning method;retrieval and validation of asymmetric hurricane intensity by CYGNSS;linking feature combination and SWIN transformer model for extracting dragon fruit from Sentinel-2A remote sensing image;MLANet: a multi-level attention-based change detection method for remote sensing images;and automated extraction of active offshore oil/gas platforms based on high-spatial resolution SDGSAT-1/GLI data: a case study in the Northern Gulf of Mexico.

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2nd European Symposium on Artificial Intelligence in Manufacturing, ESAIM 2024

2nd European Symposium on Artificial Intelligence in Manufac...

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2nd European Symposium on Artificial Intelligence in Manufacturing, ESAIM 2024

ISBN: (纸本)9783031864889

The proceedings contain 29 papers. The special focus in this conference is on Artificial Intelligence in Manufacturing. The topics include: On the Use of Generative AI to Support In-Line Process Monitoring in Zero-Defect Manufacturing;Laser Metal Deposition (LMD) Process Monitoring: from 3D Visualization of Sensor data Towards Anomaly Detection;an Explainable Active Learning Approach for Enhanced Defect Detection in Manufacturing;Synthetic data for AI-Powered Ultrafast Laser Based Micro-structuring Method Description;Towards Explicable AI in Systemic Identification of Surrogate Models of Manufacturing Processes;Leveraging Generative AI for Synthetic data Generation: Improving 6-DOF Pose Estimation in Assembly Systems;CPPM Copilot: Proposing an AI-Based Assistant for Manual Assembly Tasks in a Flexible Production;generalized Authoring Tool for Computer Vision Machine Learning Application Deployments;collaborative Learning in Shared Production Environment Using Federated image Classification;An OPIS-Based Knowledge Engineering Framework for Collaborative Robotics;a Tool for Generating and Labelling Domain Randomised Synthetic images for Object Recognition in Manufacturing;human-Robot Interaction Through Egocentric Hand Gesture Recognition;advanced Computer Vision for Industrial Safety: Indoor Human Worker Localization Using Deep Learning;LOS data Set: A Large Scale Online Scheduling Benchmark for Flexible Job Shop Problems with Setup and Transportation Times;agent-Based Communication for Fault Diagnosis in Skill-Based Production Environments Using Messages Based on I4.0 Language and Asset Administration Shells;Decision Support System (DSS) for Manufacturing Engineering of Cans Rolling;AI Based Solutions for Manufacturing Mass Customization;Industrial Maintenance Optimization Based on the Integration of Large Language Models (LLM) and Augmented Reality (AR);On an AI-Based System Architecture to Generate Robotic Cells in VR Environments;Building on the Principles of LLM

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Delineation of Basement Structure of Alluvial Mega Fans using satellite gravity and multispectral data 2

Delineation of Basement Structure of Alluvial Mega Fans usin...

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2nd EAGE/Aqua Foundation Indian Near Surface Geophysics conference and Exhibition

作者： Singh, Swati Baranval, Nikhil K. Martha, Tapas R. Das, I.C. Balanagar Hyderabad India

ISBN: (纸本)9789462824904

The Himalayas gives rise to numerous river systems in India, which emerges from the mountainous catchment and enters into the adjoining plain. With the sudden drop in the energy, the river deposits its load, forming the conical landforms, known as Alluvial Fan. These depositional loading and landforms are dynamic in nature and are controlled by the climatic factors and tectonics of the region. Based on the size and characteristics, the larger extend of the fan deposits are also known as megafans. In this study, five megafans extending from west to east, as Yamuna-Ganga, Sharda, Gandhak, Kosi and Tista megafans are studied, with the aerial extent from Delhi to Jalpaigudi. Although individual megafans are well studied in terms of depositional patterns and morphology but a comprehensive study to investigate the fans using unified set of parameters to understand possible tectonic control on fan development is not well understood. Further, the role of basement depth and structures on the formation of the alluvial fan system is under studied. Thus, present study was attempted to relate the basement structural controls over the fan system using satellite gravity and multispectral data. The optical data (e.g. LAndSAT 8, Sentinel 2) derived image products like Band Ratios and Principal Components (PCs), along with the topographic data were used to delineate the fan boundaries. The Bouguer Gravity Anomaly (BGA) derived from EIGEN-6C4 model was used to understand the subsurface response. The Basement depth (BD) and tilt derivate from the BGA were calculated for the entire megafan region using the edge detection techniques, to delineate the shallow basement structures. The high sediment depth was observed towards the eastern margin i.e. towards Kosi and Tista megafan. The combined optical derived surface structures and shallow basement structure shows cumulatively complex tectonic features, thus indicating the predominate tectonic controls along the Kosi –Tista Megafan boundary

关键词： Partitions (building)

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Anchor neighborhood embedding based single-image super-resolution reconstruction with similarity threshold adjustment 2021

Anchor neighborhood embedding based single-image super-resol...

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2nd International conference on Artificial Intelligence and Information Systems (ICAIIS )

作者： Xiong, Min Song, Yun Xiang, Yongshen Xie, Bin Deng, Zhelin Changsha Univ Sci & Technol Hunan Prov Key Lab Intelligent Proc Big Data Tran Changsha 410114 Peoples R China Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha 410000 Hunan Peoples R China Hunan Prov Transportat Planning Survey & Amp Desi Changsha 410008 Peoples R China

ISBN: (纸本)9781450390200

Anchored neighborhood super-resolution (SR) reconstruction algorithms reconstruct a high-resolution (HR) image from a single low resolution (LR) image effectively by exploiting the non-local similarity in images. In this paper, we propose an anchored neighborhood reconstruction algorithm with a similarity threshold adaptive scheme to improve the reconstruction for the mapping matrix. In the proposed method, a similarity adjustment matrix is introduced to improve the similarity of the image blocks with high deviation in the neighborhood. Besides, a threshold function is applied to determine the weights for similar blocks. Following this function, larger weights are assigned to samples with low deviations and low coefficients are assigned to blocks with low similarity. This scheme is employed to prevent the blocks from being assigned inappropriate weights and benefit the reconstruction. Experimental results show that the proposed algorithm improves the image reconstruction quality with a low computational cost.

关键词： Anchored neighborhood embedding Super-resolution Block similarity Mapping matrix

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A real-time load positioning and recognition system integrating image segmentation 2

A real-time load positioning and recognition system integrat...

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2nd IEEE-Industrial-Electronics-Society Annual On-Line conference (ONCON)

作者： Liu, Shiyu Liu, Jie Chen, Shichao Quan, Jiahao Deng, Jiukai Wei, Shangwan Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China China Construct Third Enginneering Bur Grp Co Ltd Wuhan Peoples R China

ISBN: (纸本)9798350357974

Real-time positioning and recognition of loads lifted by tower cranes is essential for achieving intelligent tower crane operations in building construction process. Currently, the real-time collection system for load features employs a laser scanning technique, resulting in a large volume of data. It imposes high performance requirements on computing terminals, making it difficult to apply at a large scale in practical construction projects. Also, existing load positioning methods extract load features from the entire environmental dataset under complex construction site environments. It leads to difficulties in feature extraction due to the inclusion of redundant information. Furthermore, existing deep learning-based load recognition models are time-consuming to train. In this paper, a real-time load positioning and recognition system integrating image segmentation is proposed. Firstly, lifted loads of tower crane in construction sites is captured using a spherical network camera, and is transmitted to a computing terminal via the RTSP protocol in real-time. Secondly, the YOLOv5s algorithm is employed to eliminate environmental interference in tower crane surroundings, enabling the load positioning and segmentation based on the hook's location. Furthermore, the MobileNetV2 algorithm is utilized for load recognition to reduce computational cost. Lastly, a remote visual prototype system for load targets is designed and developed, providing real-time remote display and automatic recognition of tower crane loads. The proposed system has demonstrated excellent performance when tested on an engineering site dataset, focusing on tower crane usage in building construction for material handling.

关键词： load positioning and recognition system image segmentation real-time transmission building construction

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image Quality of CT Plain Scan of Urinary Calculi Based on Deep Learning Algorithm 2

Image Quality of CT Plain Scan of Urinary Calculi Based on D...

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2nd IEEE International conference on Artificial Intelligence of Things and Crowdsensing, AIoTCs 2022

作者： Wang, Yanfei The First Affiliated Hospital of Hebei North University Department of Radiology Zhangjiakou075000 China

ISBN: (纸本)9798350334104

With the large-scale development of urinary calculi, pathogenic microorganisms and bacteria have been effectively separated, making artificial diagnosis more difficult. Based on CT detection, this paper uses deep learning algorithm to establish a complex neural network model, and uses deep learning (FIR) technology to improve image quality through the construction of multi-layer forward transfer structure, neuron mutual association and other nonlinear training processes. At the same time, combined with CT data, it obtains the information feature points set of hidden layer concentration distribution and shape feature changes in real time, and optimizes the original preprocessing to realize image reconstruction and recognition, Then the algorithm is tested by simulation experiments. Finally, the test results show that the CT plain scan image quality of urinary calculi optimized by the deep learning algorithm is very good. This shows that the algorithm has excellent optimization ability. © 2022 IEEE.

关键词： image reconstruction

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