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Squeeze-and-Remember Block 23

Squeeze-and-Remember Block

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23rd IEEE International conference on machine learning and applications, ICMLA 2024

作者： Cakaj, Rinor Mehnert, Jens Yang, Bin Robert Bosch GmbH University of Stuttgart Leonberg71229 Germany Robert Bosch GmbH Leonberg71229 Germany Iss University of Stuttgart Stuttgart70550 Germany

ISBN: (纸本)9798350374889

Convolutional neural networks (CNNs) are important for many machine learning tasks. They are built with different types of layers: convolutional layers that detect features, dropout layers that help to avoid over-reliance on any single neuron, and residual layers that allow the reuse of features. However, CNNs lack a dynamic feature retention mechanism similar to the human brain's memory, limiting their ability to use learned information in new contexts. To bridge this gap, we introduce the 'Squeeze-and-Remember' (SR) block, a novel architectural unit that gives CNNs dynamic memory-like functionalities. The SR block selectively memorizes important features during training, and then adaptively re-applies these features during inference. This improves the network's ability to make contextually informed predictions. Empirical results on imageNet and Cityscapes datasets demonstrate the SR block's efficacy: integration into ResNet50 improved top-1 validation accuracy on imageNet by 0.52% over dropout2d alone, and its application in DeepLab v3 increased mean Intersection over Union in Cityscapes by 0.20%. These improvements are achieved with minimal computational overhead. This show the SR block's potential to enhance the capabilities of CNNs in image processing tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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5th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

5th EAI International Conference on Cognitive Computing and ...

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5th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

ISBN: (纸本)9783031770777

The proceedings contain 102 papers. The special focus in this conference is on Cognitive Computing and Cyber Physical Systems. The topics include: Comparative Analysis Between Fuzzy Theorem and KNN Methodology for Fault and Anomaly Detection;Production Classification in E-Commerce Based on Product Descriptions with Natural Language processing (NLP) and machine learning Models;defense Strategy Security Mechanism for Sensor networks;an Efficient Approach for Food Demand Forecasting Using an Ensemble Technique and Statistical Analysis;identification of Different Medicinal Plants Using machine learning and image processing;Leveraging the Power of MRMR in machine learning Models for Multi Class Classification of Rice to Promote Sustainable and Efficient Smart Farming;performance Analysis of Word Recognition System Using Tensor Flow;A Distinct Artificial Feed Forward neural Network (AF2NN) Model for Predicting Compressive Strength of Geo-Polymer Concrete;Performance Assessment of Deep learning-Models for Kidney Tumor Segmentation using CT images;investigation of Quantum machine learning for Smart Eco System Focusing on Energy Optimization;Classification of Skin Cancer Using CNN with Transformer Layer;a Unified Approach to Smart Coconut Farming with IoT and Deep learning for Recommendation of Pesticides and Fertilizers;classifying the Severity of Diabetic Retinopathy Using Deep learning;Exploring Prominent Convolutional neural Network Frameworks to Identify COVID-19 Deceases by Using Medical images;non-invasive Technique for Detecting Glycosuria Through image processing and Deep learning Approaches;deep learning Strategies for Multiclass Skin Disease Classification;AI-Driven Glaucoma Susceptibility Assessment and Lifestyle Guidance;Deep learning in Cartoon Moderation: Distinguishing Child-Friendly Content with CNN Architectures;THz Wideband Metamaterial Absorber for Different applications.

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Advanced Detection of Copy-Move Forgery Using Deep Convolutional neural Network Features Integrated with machine learning-Based Classification Techniques 3rd

Advanced Detection of Copy-Move Forgery Using Deep Convoluti...

引用

3rd International conference on machine learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Al-khuzaie, Mohammed Y. Chohan, Jasgurpreet Singh Sabah, Hawraa Ali Dey, Protyay Verma, Deeksha Hussein, Laith College of Technical Engineering National University of Science and Technology Dhi Qar Nasiriyah64001 Iraq School of Mechanical Engineering Rayat Bahra University Mohali India NIMS School of Computing Science and Artificial Intelligence NIMS University Rajasthan Rajasthan Jaipur India Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri- Punjab Mohali140307 India College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (纸本)9789819624676

Technological advancements have significantly contributed to the creation and processing of digital images through various image manipulation software available today. Consequently, the need for effective image forgery detection techniques has become paramount to distinguish between authentic and manipulated images. image forensics is utilized in various fields, including national intelligence agencies, scientific publications, and social networks. One specific type of image manipulation, known as copy-move forgery, involves copying and pasting a portion of an image within the same image, resulting in an altered version of the original. In this study, we introduce a lightweight convolutional neural network (CNN) architecture combined with Support Vector machine (SVM) classification for copy-move forgery detection (CMFD). This approach uses a similarity measure across feature patches to identify regions containing forgeries. We evaluated the effectiveness of our model using the challenging MICCF600 dataset commonly employed in CMFD research. The proposed model effectively reduces false positives, thereby enhancing pixel-level accuracy and reliability in CMFD applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Pixel-Grounded Prototypical Part networks

Pixel-Grounded Prototypical Part Networks

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IEEE/CVF Winter conference on applications of Computer Vision (WACV)

作者： Carmichael, Zachariah Lohit, Suhas Cherian, Anoop Jones, Michael J. Scheirer, Walter J. Univ Notre Dame Notre Dame IN 46556 USA Mitsubishi Elect Res Labs Cambridge MA 02139 USA

ISBN: (纸本)9798350318920;9798350318937

Prototypical part neural networks (ProtoPartNNs), namely PROTOPNET and its derivatives, are an intrinsically interpretable approach to machine learning. Their prototype learning scheme enables intuitive explanations of the form, this (prototype) looks like that (testing image patch). But, does this actually look like that? In this work, we delve into why object part localization and associated heat maps in past work are misleading. Rather than localizing to object parts, existing ProtoPartNNs localize to the entire image, contrary to generated explanatory visualizations. We argue that detraction from these underlying issues is due to the alluring nature of visualizations and an over-reliance on intuition. To alleviate these issues, we devise new receptive field-based architectural constraints for meaningful localization and a principled pixel space mapping for ProtoPartNNs. To improve interpretability, we propose additional architectural improvements, including a simplified classification head. We also make additional corrections to PROTOPNET and its derivatives, such as the use of a validation set, rather than a test set, to evaluate generalization during training. Our approach, PixPNET (Pixel-grounded Prototypical part Network), is the only ProtoPartNN that truly learns and localizes to prototypical object parts. We demonstrate that PixPNET achieves quantifiably improved interpretability without sacrificing accuracy(1).

关键词： accountable Algorithms Algorithms Algorithms and algorithms ethical computer vision Explainable fair formulations image recognition and understanding machine learning architectures privacy-preserving

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5th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

5th EAI International Conference on Cognitive Computing and ...

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5th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

ISBN: (纸本)9783031770807

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Deep learning based Automated Food image Classification 2

Deep Learning based Automated Food Image Classification

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2nd International conference on Inventive Computing and Informatics (ICICI)

作者： Sumanth, Mukkamala Reddy, Anumula Hemanth Abhishek, Dandu Balaji, Sanaka Venkata Amarendra, K. Srinivas, P. V. V. S. Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Informat Technol Vaddeswaram Andhra Pradesh India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram Andhra Pradesh India

ISBN: (纸本)9798350373301;9798350373295

Food image classification is essential for numerous applications, like nutritional analysis, food recording, and dietary evaluation. The proliferation of social media and smartphones generated a significant increase in the sharing of food photographs online, necessitating automated systems capable of accurately classifying food items in images. Convolutional neural networks (CNNs), particularly the MobileNetV2 architecture, have emerged as extremely strong machinery for identifying image tasks because of its capacity to extract unique characteristics directly from raw pixel data. This work focuses on utilizing CNNs and the MobileNetV2 architecture for deep learning-based automatic food image classification. MobileNetV2 strikes a balance between classification accuracy and processing efficiency, making it suitable for mobile and embedded vision applications. The study addresses important challenges in food image classification, including intra-class variances, cluttered backgrounds, and disparities in food appearance. The objective aims to set up a reliable and effective system which can determine food products in various visual scenarios by leveraging state-of-the-art deep learning techniques.

关键词： Deep learning image Classification Food Photographs Convolutional neural networks MobileNetV2 Food Classification Nutrition

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Transfer learning for Improved Hand Gesture Recognition with neural networks 8

Transfer Learning for Improved Hand Gesture Recognition with...

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8th IEEE International conference on image and Signal processing and their applications, ISPA 2024

作者： Bekiri, Roumaissa Babahenini, Sarra Babahenini, Mohamed Chaouki Biskra University LESIA Laboratory Computer Science Department Algeria

ISBN: (纸本)9798350309249

Hand recognition is crucial in various applications, such as gesture recognition, augmented and virtual reality. Hand gestures have the potential to facilitate human-computer interaction and enhance its convenience. The characteristics of gestures, such as their orientation and shape, differ among individuals, resulting in non-linearities in the problem. Recent studies have demonstrated the superior performance of Convolutional neural networks (CNNs) in accurately representing and classifying images, further emphasizing their effectiveness in this context. This paper proposes a novel deep learning model that is augmented using a transfer learning technique. Further, we trained our model using hand gesture recognition datasets of near-infrared images captured by the Leap Motion sensor. The proposed model is composed of ten classes that represent personalized gestures. The model with augmented data achieved an accuracy 99.12% which is nearly 2% higher than the model without augmentation (97.87%). The results demonstrate the robustness and efficiency of our model compared with the state-of-the-art models. © 2024 IEEE.

关键词： Palmprint recognition

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5th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

5th EAI International Conference on Cognitive Computing and ...

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5th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

ISBN: (纸本)9783031770746

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Enhancing Emoji Identification in Real-World Emotion Recognition Systems using Facial Masking Techniques 1

Enhancing Emoji Identification in Real-World Emotion Recogni...

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1st International conference on Intelligent Systems and Computational networks, ICISCN 2025

作者： Puthran, Raksha Mugeraya, Supriya Shetty, Anusha Prashanth Department of Master of Computer Applications Bangalore India

ISBN: (纸本)9798331529246

Emojiify is a cutting-edge real-time image processing system powered by Convolutional neural networks (CNNs) for face emotion recognition. This unique technology fosters human-computer interaction by appropriately recognizing emotions in live facial images and instantly overlaying corresponding emojis, satisfying the rising need for engaging and intuitive emoji-based digital communication. The system classifies seven emotions - happiness, winking, pouting, sad, angry, shock, and neutrality - ensuring a smooth and interactive user experience. Trained on a selected dataset of 2,100 photographs, emojiify achieves an incredible 97% accuracy, exhibiting its capacity to capture nuanced emotional expressions. Its good performance across many demographic categories makes it vital for applications in interactive media, targeted content delivery, and user engagement. By integrating state-of-the-art machine learning with real-time picture processing, this research increases digital emotion expression, unlocking potential applications in entertainment, security, and smart systems. This work redefines how technology bridges human emotions and digital interactions. © 2025 IEEE.

关键词： Human computer interaction

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Comparative Study of Plant Disease Detection Techniques: A Performance-Based Review 4

Comparative Study of Plant Disease Detection Techniques: A P...

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4th International conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Singh, Sarita Goel, Noopur Veer Bahadur Singh Purvanchal University India Veer Bahadur Singh Purvanchal University Department of Computer Applications India

ISBN: (纸本)9798350364729

Agriculture is essential for human civilization, contributing to the economy and ensuring food supply. However, plant diseases can hinder growth and reduce crop yield. It is crucial to identify and categorize these diseases as soon as possible and accurately. Manual methods for disease prediction and classification may lead to inaccuracies and tedium. Therefore, implementing computerized image processing methods in agriculture can help to minimize losses and increase productivity. To detect and categorize plant illnesses on the basis of photos of sick leaves or crops, a variety of approaches, including DL and ML techniques like k-means clustering, NB and convolutional neural networks have been investigated. Even though there has been a lot of development in this area, talks and changing technologies call for ongoing improvements. The particular problem, data accessibility, and processing capacity all influence which of the conventional machine learning and deep learning approaches are the best. When a large amount of data and computational resources are available, deep learning, especially with CNNs, is generally the method of choice for picture identification and classification. This paper introduced a brief overview of the latest research on identifying and categorizes crop diseases and illness using image processing in the field of artificial intelligence. It covers the performance, assessment criteria, and findings of different approaches, aiming to keep future researchers informed about the progress in this area. © 2024 IEEE.

关键词： Convolutional neural networks

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