Visual Question Answering (VQA) is a research area at the intersection of computer vision and
natural language processing. It involves developing algorithms and models that can understand and
answer questions about visual content, such as images or videos.
Visual Saliency Model Using Sift and Comparison of Learning Approachescsandit
This document discusses a study that aims to develop a visual saliency model to predict where humans look in images. It uses the SIFT feature in addition to low, mid, and high-level image features to train machine learning models on an eye-tracking dataset. Support vector machines (SVM) achieved the best performance, accurately predicting fixations 88% of the time. Including the SIFT feature further improved SVM performance to 91% accuracy. The study evaluates different machine learning methods and determines SVM to be best suited for this binary classification task using high-dimensional image data.
IRJET - Visual Question Answering – Implementation using KerasIRJET Journal
This document summarizes a research paper that implements visual question answering using Keras deep learning frameworks. The researchers combine image features extracted from VGG-16 with question vectors from spaCy word embeddings to produce answers to questions about images. A multi-layer perceptron is used to merge the CNN and RNN models and produce categorical classes as answers. The implementation uses Keras with TensorFlow backend to extract image features from VGG-16 and question vectors from spaCy, which are then combined in the multi-layer perceptron to answer questions about the images.
This document discusses face detection techniques. It begins with an introduction that defines face detection and discusses why it is important and challenging. It then covers topics like image segmentation, face detection approaches, morphological image processing, and skin color-based face detection. The document analyzes literature on face detection methods and provides descriptions of techniques like thresholding, edge detection, region-based segmentation, and template matching. It also includes a case study on specific face detection software applications and concludes by summarizing the discussed techniques.
IMAGE CAPTIONING USING TRANSFORMER: VISIONAIDIRJET Journal
The document proposes a new image captioning model called VisionAid that aims to address several issues with existing approaches. It conducts a literature review of transformer-based image captioning methods to identify solutions. VisionAid incorporates grid-level feature extraction, augmented training data diversity using BERT embeddings, and a combination of normalized self-attention and geometric self-attention to better model object relationships while avoiding internal covariate shift issues. The model aims to generate more accurate and diverse captions by leveraging techniques from various transformer models discussed in the literature review.
IRJET - Facial In-Painting using Deep Learning in Machine LearningIRJET Journal
This document describes a deep learning approach for facial in-painting to generate plausible facial structures for missing pixels in face images. The approach uses the observable region of a face as well as inferred attributes like gender, ethnicity, and expression. It selects a guidance face matching the attributes to in-paint missing areas like the mouth or eyes. In-painting is done on intrinsic image layers instead of RGB to handle illumination differences. The proposed method uses face detection, alignment, representation, and matching with real-time datasets to fill missing or masked regions with realistic synthesized content.
IRJET- Visual Question Answering using Combination of LSTM and CNN: A SurveyIRJET Journal
This document discusses using a combination of long short-term memory (LSTM) and convolutional neural networks (CNN) for visual question answering (VQA). It proposes extracting image features from CNNs and encoding question semantics with LSTMs. A multilayer perceptron would then combine the image and question representations to predict answers. The methodology aims to reduce statistical biases in VQA datasets by focusing attention on relevant image regions. It was implemented in Keras with TensorFlow using pre-trained CNNs for images and word embeddings for questions. The proposed approach analyzes local image features and question semantics to improve VQA classification accuracy over methods relying solely on language.
The document summarizes contributions from a student research project on developing an online learning system called AvaScholar Student.
The key contributions include:
1) Developing a real-time 3D face tracking system from 2D video to model facial geometry and motions.
2) Using the face tracking to power a performance-driven avatar and detect student attention.
3) Designing a mobile-cloudlet framework to enable computationally heavy tasks like face tracking on mobile devices.
4) Proposing a novel supervised learning method called Supervised SVQ to improve non-frontal expression recognition rates.
Concept mapping is a strategy for organizing and representing knowledge through diagrams that show relationships between concepts. It has benefits for both teachers and students by simplifying complex information in a visual way that improves understanding and long-term retention. The document discusses how concept mapping can be integrated into classroom lessons by providing concept map handouts and having students create their own maps for assignments and assessments. The future of concept mapping is promising as software provides tools to apply the strategy and it may become a primary way information is presented in education and other contexts.
Visual Saliency Model Using Sift and Comparison of Learning Approachescsandit
This document discusses a study that aims to develop a visual saliency model to predict where humans look in images. It uses the SIFT feature in addition to low, mid, and high-level image features to train machine learning models on an eye-tracking dataset. Support vector machines (SVM) achieved the best performance, accurately predicting fixations 88% of the time. Including the SIFT feature further improved SVM performance to 91% accuracy. The study evaluates different machine learning methods and determines SVM to be best suited for this binary classification task using high-dimensional image data.
IRJET - Visual Question Answering – Implementation using KerasIRJET Journal
This document summarizes a research paper that implements visual question answering using Keras deep learning frameworks. The researchers combine image features extracted from VGG-16 with question vectors from spaCy word embeddings to produce answers to questions about images. A multi-layer perceptron is used to merge the CNN and RNN models and produce categorical classes as answers. The implementation uses Keras with TensorFlow backend to extract image features from VGG-16 and question vectors from spaCy, which are then combined in the multi-layer perceptron to answer questions about the images.
This document discusses face detection techniques. It begins with an introduction that defines face detection and discusses why it is important and challenging. It then covers topics like image segmentation, face detection approaches, morphological image processing, and skin color-based face detection. The document analyzes literature on face detection methods and provides descriptions of techniques like thresholding, edge detection, region-based segmentation, and template matching. It also includes a case study on specific face detection software applications and concludes by summarizing the discussed techniques.
IMAGE CAPTIONING USING TRANSFORMER: VISIONAIDIRJET Journal
The document proposes a new image captioning model called VisionAid that aims to address several issues with existing approaches. It conducts a literature review of transformer-based image captioning methods to identify solutions. VisionAid incorporates grid-level feature extraction, augmented training data diversity using BERT embeddings, and a combination of normalized self-attention and geometric self-attention to better model object relationships while avoiding internal covariate shift issues. The model aims to generate more accurate and diverse captions by leveraging techniques from various transformer models discussed in the literature review.
IRJET - Facial In-Painting using Deep Learning in Machine LearningIRJET Journal
This document describes a deep learning approach for facial in-painting to generate plausible facial structures for missing pixels in face images. The approach uses the observable region of a face as well as inferred attributes like gender, ethnicity, and expression. It selects a guidance face matching the attributes to in-paint missing areas like the mouth or eyes. In-painting is done on intrinsic image layers instead of RGB to handle illumination differences. The proposed method uses face detection, alignment, representation, and matching with real-time datasets to fill missing or masked regions with realistic synthesized content.
IRJET- Visual Question Answering using Combination of LSTM and CNN: A SurveyIRJET Journal
This document discusses using a combination of long short-term memory (LSTM) and convolutional neural networks (CNN) for visual question answering (VQA). It proposes extracting image features from CNNs and encoding question semantics with LSTMs. A multilayer perceptron would then combine the image and question representations to predict answers. The methodology aims to reduce statistical biases in VQA datasets by focusing attention on relevant image regions. It was implemented in Keras with TensorFlow using pre-trained CNNs for images and word embeddings for questions. The proposed approach analyzes local image features and question semantics to improve VQA classification accuracy over methods relying solely on language.
The document summarizes contributions from a student research project on developing an online learning system called AvaScholar Student.
The key contributions include:
1) Developing a real-time 3D face tracking system from 2D video to model facial geometry and motions.
2) Using the face tracking to power a performance-driven avatar and detect student attention.
3) Designing a mobile-cloudlet framework to enable computationally heavy tasks like face tracking on mobile devices.
4) Proposing a novel supervised learning method called Supervised SVQ to improve non-frontal expression recognition rates.
Concept mapping is a strategy for organizing and representing knowledge through diagrams that show relationships between concepts. It has benefits for both teachers and students by simplifying complex information in a visual way that improves understanding and long-term retention. The document discusses how concept mapping can be integrated into classroom lessons by providing concept map handouts and having students create their own maps for assignments and assessments. The future of concept mapping is promising as software provides tools to apply the strategy and it may become a primary way information is presented in education and other contexts.
TOP 5 Most View Article From Academia in 2019sipij
TOP 5 Most View Article From Academia in 2019
Signal & Image Processing : An International Journal (SIPIJ)
ISSN : 0976 - 710X (Online) ; 2229 - 3922 (print)
http://www.airccse.org/journal/sipij/index.html
Real-time eyeglass detection using transfer learning for non-standard facial...IJECEIAES
The aim of this paper is to build a real-time eyeglass detection framework based on deep features present in facial or ocular images, which serve as a prime factor in forensics analysis, authentication systems and many more. Generally, eyeglass detection methods were executed using cleaned and fine-tuned facial datasets; it resulted in a well-developed model, but the slightest deviation could affect the performance of the model giving poor results on real-time non-standard facial images. Therefore, a robust model is introduced which is trained on custom non-standard facial data. An Inception V3 architecture based pre-trained convolutional neural network (CNN) is used and fine-tuned using model hyper-parameters to achieve a high accuracy and good precision on non-standard facial images in real-time. This resulted in an accuracy score of about 99.2% and 99.9% for training and testing datasets respectively in less amount of time thereby showing the robustness of the model in all conditions.
Development of video-based emotion recognition using deep learning with Googl...TELKOMNIKA JOURNAL
Emotion recognition using images, videos, or speech as input is considered as a hot topic in the field of research over some years. With the introduction of deep learning techniques, e.g., convolutional neural networks (CNN), applied in emotion recognition, has produced promising results. Human facial expressions are considered as critical components in understanding one's emotions. This paper sheds light on recognizing the emotions using deep learning techniques from the videos. The methodology of the recognition process, along with its description, is provided in this paper. Some of the video-based datasets used in many scholarly works are also examined. Results obtained from different emotion recognition models are presented along with their performance parameters. An experiment was carried out on the fer2013 dataset in Google Colab for depression detection, which came out to be 97% accurate on the training set and 57.4% accurate on the testing set.
ATTENTION BASED IMAGE CAPTIONING USING DEEP LEARNINGNathan Mathis
The document describes a study on attention-based image captioning using deep learning. The study aims to generate image captions using an encoder-decoder model with an attention mechanism. The encoder is Google InceptionV3 which extracts image features, and the decoder is a GRU that generates captions. The model is trained on the MS COCO dataset and evaluated using BLEU score. Results show the attention mechanism helps focus on relevant image areas to produce descriptive captions.
A Literature Survey on Image Linguistic Visual Question AnsweringIRJET Journal
This document discusses a literature survey on image and linguistic visual question answering. It aims to develop a model that achieves higher performance than state-of-the-art solutions by exploring different existing models and developing a custom model. The paper reviews several existing models for visual question answering and image classification using convolutional neural networks. It also discusses developing a new dataset for visual question answering using automated question generation from image descriptions.
IRJET- Saliency based Image Co-SegmentationIRJET Journal
The document describes a saliency-based image co-segmentation method. It proposes to first extract inter-image information through co-saliency maps generated from multiple saliency extraction methods. Then, it performs single image segmentation on each individual image. A key step is fusing the diverse saliency maps through a saliency co-fusion process at the superpixel level. This exploits inter-image information to boost common foreground regions and suppress background regions. Experiments show the co-fusion based approach achieves competitive performance compared to state-of-the-art methods without parameter tuning.
Modelling Framework of a Neural Object RecognitionIJERA Editor
In many industrial, medical and scientific image processing applications, various feature and pattern recognition
techniques are used to match specific features in an image with a known template. Despite the capabilities of
these techniques, some applications require simultaneous analysis of multiple, complex, and irregular features
within an image as in semiconductor wafer inspection. In wafer inspection discovered defects are often complex
and irregular and demand more human-like inspection techniques to recognize irregularities. By incorporating
neural network techniques such image processing systems with much number of images can be trained until the
system eventually learns to recognize irregularities. The aim of this project is to develop a framework of a
machine-learning system that can classify objects of different category. The framework utilizes the toolboxes in
the Matlab such as Computer Vision Toolbox, Neural Network Toolbox etc.
A Study Of Visual Design In PowerPoint Presentation Slide And Its Relationshi...Lisa Muthukumar
This study examined the relationship between visual design of PowerPoint presentation slides and learner engagement and satisfaction among 21 postgraduate students. Students viewed a lecture with visually non-designed slides and a lecture with visually designed slides. Questionnaires and observations found no significant relationship between visual design and engagement/satisfaction, but mean engagement and satisfaction scores were higher for the designed slides session. While visual design did not significantly impact engagement and satisfaction, comments indicated students preferred the designed slides and found them more interesting. The study suggests instructor teaching style, including sharing real-life examples, may influence learner engagement and satisfaction more than visual design alone. Further research is needed to better understand these relationships.
IRJET - Deep Learning Approach to Inpainting and Outpainting SystemIRJET Journal
This document discusses a deep learning approach for image inpainting and outpainting. It proposes a new generative model-based approach using a fully convolutional neural network that can process images with multiple holes at variable locations and sizes. The model aims to not only synthesize novel image structures, but also explicitly utilize surrounding image features as references during training to generate better predictions. Experiments on faces, textures and natural images demonstrate the proposed approach generates higher quality inpainting results than existing methods. It aims to address limitations of CNNs in borrowing information from distant areas by leveraging texture and patch synthesis approaches.
Visualization tools allow learners to visualize ideas in ways that help interpret and communicate concepts. There are two main uses - interpretive tools help extract meaning from information, while expressive tools help convey meaning. Examples of interpretive tools include programs that enable visualizing science concepts like molecular structures. Expressive tools include drawing programs. Visualizing mathematics with technologies also helps comprehension through manipulatives and graphing calculators. Digital cameras, videos, and phones further aid visualization.
Analysis of student sentiment during video class with multi-layer deep learni...IJECEIAES
The modern education system is an essential part of the rise of technology. The E-learning education system is not just an experimental system; it is a vital learning system for the whole world over the last few months. In our research, we have developed our learning method in a more effective and modern way for students and teachers. For significant implementation, we are implementing convolutions neural networks and advanced data classifiers. The expression and mood analysis of a student during the online class is the main focus of our study. For output measure, we divide the final output result as attentive, inattentive, understand, and neutral. Showing the output in real-time online class and for sensory analysis, we have used support vector machine (SVM) and OpenCV. The level of 5*4 neural network is created for this work. An advanced learning medium is proposed through our study. Teachers can monitor the live class and different feelings of a student during the class period through this system.
WEAKLY SUPERVISED FINE-GRAINED CATEGORIZATION WITH PART-BASED IMAGE REPRESENT...Nexgen Technology
TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS
MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM,WWW.FINALYEAR-IEEEPROJECTS.COM, EMAIL:Praveen@nexgenproject.com
NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
This document presents a method for real-time facial expression analysis using principal component analysis (PCA). The method involves detecting faces, extracting expression features from the eye and mouth regions, applying PCA to extract texture features, and using a support vector machine classifier to classify expressions. The proposed approach was tested on a database of facial images with expressions categorized as happy, angry, disgust, sad, or neutral. PCA was used to select the most relevant eigenfaces and reduce the dimensionality of the feature space for more efficient classification of expressions in real-time.
This document discusses using visuals and images in education. It explains that visuals can help address different learner preferences and styles, enhance learning and retention, and bring deeper understanding to complex subjects. It then discusses visual literacy, defining it as the ability to interpret and derive meaning from visual content. The document outlines some key visual elements or building blocks of images like framing, angle, lighting, and scale, and how understanding these elements can help students analyze and interpret visual information.
This document summarizes various clustering techniques used for image segmentation. It discusses clustering approaches like k-means, hierarchical clustering, spectral clustering (Ncut), and relevance feedback. The document provides examples and explanations of how these techniques work. For instance, it explains that k-means clustering aims to group images into predefined clusters by minimizing intra-cluster distances. It also gives an example of how hierarchical clustering builds clusters in a tree structure. In summary, the document surveys popular clustering methods and how they can segment images in an effective manner by grouping similar pixels or images.
A study on attention-based deep learning architecture model for image captioningIAESIJAI
Image captioning has been widely studied due to its ability in a visual scene understanding. Automatic visual scene understanding is useful for remote monitoring system and visually impaired people. Attention-based models, including transformer, are the current state-of-the-art architectures used in developing image captioning model. This study examines the works in the development of image captioning model, especially models that are developed based on attention mechanism. The architecture, the dataset, and the evaluation metrics analysis are done to the collected works. A general flow of image captioning model development is also presented. The literature search process carried out on Google Scholar. There are 36 literatures used in this study, including a specific image captioning development in Indonesian. It is done to take one point of view of image captioning development in a low resource language. Studies using transformer model generally achieves higher evaluation metric scores. In our finding, the highest evaluation scores on the consensus-based image description evaluation (CIDEr) c5 and c40 metrics are 138.5 and 140.5 respectively. This study gives a baseline on future development of image captioning model and brings the general concept of the image captioning development process including a picture of the development in low resource language.
IMAGE CONTENT DESCRIPTION USING LSTM APPROACHcsandit
In this digital world, artificial intelligence has provided solutions to many problems, likewise to
encounter problems related to digital images and operations related to the extensive set of
images. We should learn how to analyze an image, and for that, we need feature extraction of
the content of that image. Image description methods involve natural language processing and
concepts of computer vision. The purpose of this work is to provide an efficient and accurate
image description of an unknown image by using deep learning methods. We propose a novel
generative robust model that trains a Deep Neural Network to learn about image features after
extracting information about the content of images, for that we used the novel combination of
CNN and LSTM. We trained our model on MSCOCO dataset, which provides set of annotations for a particular image, and after the model is fully automated, we tested it by providing raw images. And also several experiments are performed to check efficiency and robustness of the system, for that we have calculated BLUE Score.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
FACE PHOTO-SKETCH RECOGNITION USING DEEP LEARNING TECHNIQUES - A REVIEWIRJET Journal
This document reviews various deep learning techniques for face photo-sketch recognition. It summarizes several studies that used deep learning and other machine learning methods to match faces across photo and sketch domains. Feature injection and convolutional neural networks, knowledge distillation frameworks, relational graph modules, domain alignment embedding networks, and using 3D morphable models and CNNs for forensic sketch recognition are some of the techniques discussed. The studies aim to improve cross-domain face recognition accuracy, which remains challenging due to differences in photo and sketch representations. Accuracies ranging from 73-95% are reported for the various methods.
Stellmach.2011.designing gaze supported multimodal interactions for the explo...mrgazer
This document describes research into designing gaze-supported multimodal interactions for exploring large image collections. Specifically, it investigates combining eye gaze with a touch-and-tilt mobile device or keyboard to control an adaptive fisheye lens visualization. User interviews were conducted to understand how people would want to use such input combinations for browsing images. Based on this feedback, a prototype system was developed using a SpringLens technique with gaze and a touch device. A user study provided insights into how well the gaze-supported interaction techniques were experienced.
TOP 5 Most View Article From Academia in 2019sipij
TOP 5 Most View Article From Academia in 2019
Signal & Image Processing : An International Journal (SIPIJ)
ISSN : 0976 - 710X (Online) ; 2229 - 3922 (print)
http://www.airccse.org/journal/sipij/index.html
Real-time eyeglass detection using transfer learning for non-standard facial...IJECEIAES
The aim of this paper is to build a real-time eyeglass detection framework based on deep features present in facial or ocular images, which serve as a prime factor in forensics analysis, authentication systems and many more. Generally, eyeglass detection methods were executed using cleaned and fine-tuned facial datasets; it resulted in a well-developed model, but the slightest deviation could affect the performance of the model giving poor results on real-time non-standard facial images. Therefore, a robust model is introduced which is trained on custom non-standard facial data. An Inception V3 architecture based pre-trained convolutional neural network (CNN) is used and fine-tuned using model hyper-parameters to achieve a high accuracy and good precision on non-standard facial images in real-time. This resulted in an accuracy score of about 99.2% and 99.9% for training and testing datasets respectively in less amount of time thereby showing the robustness of the model in all conditions.
Development of video-based emotion recognition using deep learning with Googl...TELKOMNIKA JOURNAL
Emotion recognition using images, videos, or speech as input is considered as a hot topic in the field of research over some years. With the introduction of deep learning techniques, e.g., convolutional neural networks (CNN), applied in emotion recognition, has produced promising results. Human facial expressions are considered as critical components in understanding one's emotions. This paper sheds light on recognizing the emotions using deep learning techniques from the videos. The methodology of the recognition process, along with its description, is provided in this paper. Some of the video-based datasets used in many scholarly works are also examined. Results obtained from different emotion recognition models are presented along with their performance parameters. An experiment was carried out on the fer2013 dataset in Google Colab for depression detection, which came out to be 97% accurate on the training set and 57.4% accurate on the testing set.
ATTENTION BASED IMAGE CAPTIONING USING DEEP LEARNINGNathan Mathis
The document describes a study on attention-based image captioning using deep learning. The study aims to generate image captions using an encoder-decoder model with an attention mechanism. The encoder is Google InceptionV3 which extracts image features, and the decoder is a GRU that generates captions. The model is trained on the MS COCO dataset and evaluated using BLEU score. Results show the attention mechanism helps focus on relevant image areas to produce descriptive captions.
A Literature Survey on Image Linguistic Visual Question AnsweringIRJET Journal
This document discusses a literature survey on image and linguistic visual question answering. It aims to develop a model that achieves higher performance than state-of-the-art solutions by exploring different existing models and developing a custom model. The paper reviews several existing models for visual question answering and image classification using convolutional neural networks. It also discusses developing a new dataset for visual question answering using automated question generation from image descriptions.
IRJET- Saliency based Image Co-SegmentationIRJET Journal
The document describes a saliency-based image co-segmentation method. It proposes to first extract inter-image information through co-saliency maps generated from multiple saliency extraction methods. Then, it performs single image segmentation on each individual image. A key step is fusing the diverse saliency maps through a saliency co-fusion process at the superpixel level. This exploits inter-image information to boost common foreground regions and suppress background regions. Experiments show the co-fusion based approach achieves competitive performance compared to state-of-the-art methods without parameter tuning.
Modelling Framework of a Neural Object RecognitionIJERA Editor
In many industrial, medical and scientific image processing applications, various feature and pattern recognition
techniques are used to match specific features in an image with a known template. Despite the capabilities of
these techniques, some applications require simultaneous analysis of multiple, complex, and irregular features
within an image as in semiconductor wafer inspection. In wafer inspection discovered defects are often complex
and irregular and demand more human-like inspection techniques to recognize irregularities. By incorporating
neural network techniques such image processing systems with much number of images can be trained until the
system eventually learns to recognize irregularities. The aim of this project is to develop a framework of a
machine-learning system that can classify objects of different category. The framework utilizes the toolboxes in
the Matlab such as Computer Vision Toolbox, Neural Network Toolbox etc.
A Study Of Visual Design In PowerPoint Presentation Slide And Its Relationshi...Lisa Muthukumar
This study examined the relationship between visual design of PowerPoint presentation slides and learner engagement and satisfaction among 21 postgraduate students. Students viewed a lecture with visually non-designed slides and a lecture with visually designed slides. Questionnaires and observations found no significant relationship between visual design and engagement/satisfaction, but mean engagement and satisfaction scores were higher for the designed slides session. While visual design did not significantly impact engagement and satisfaction, comments indicated students preferred the designed slides and found them more interesting. The study suggests instructor teaching style, including sharing real-life examples, may influence learner engagement and satisfaction more than visual design alone. Further research is needed to better understand these relationships.
IRJET - Deep Learning Approach to Inpainting and Outpainting SystemIRJET Journal
This document discusses a deep learning approach for image inpainting and outpainting. It proposes a new generative model-based approach using a fully convolutional neural network that can process images with multiple holes at variable locations and sizes. The model aims to not only synthesize novel image structures, but also explicitly utilize surrounding image features as references during training to generate better predictions. Experiments on faces, textures and natural images demonstrate the proposed approach generates higher quality inpainting results than existing methods. It aims to address limitations of CNNs in borrowing information from distant areas by leveraging texture and patch synthesis approaches.
Visualization tools allow learners to visualize ideas in ways that help interpret and communicate concepts. There are two main uses - interpretive tools help extract meaning from information, while expressive tools help convey meaning. Examples of interpretive tools include programs that enable visualizing science concepts like molecular structures. Expressive tools include drawing programs. Visualizing mathematics with technologies also helps comprehension through manipulatives and graphing calculators. Digital cameras, videos, and phones further aid visualization.
Analysis of student sentiment during video class with multi-layer deep learni...IJECEIAES
The modern education system is an essential part of the rise of technology. The E-learning education system is not just an experimental system; it is a vital learning system for the whole world over the last few months. In our research, we have developed our learning method in a more effective and modern way for students and teachers. For significant implementation, we are implementing convolutions neural networks and advanced data classifiers. The expression and mood analysis of a student during the online class is the main focus of our study. For output measure, we divide the final output result as attentive, inattentive, understand, and neutral. Showing the output in real-time online class and for sensory analysis, we have used support vector machine (SVM) and OpenCV. The level of 5*4 neural network is created for this work. An advanced learning medium is proposed through our study. Teachers can monitor the live class and different feelings of a student during the class period through this system.
WEAKLY SUPERVISED FINE-GRAINED CATEGORIZATION WITH PART-BASED IMAGE REPRESENT...Nexgen Technology
TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS
MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM,WWW.FINALYEAR-IEEEPROJECTS.COM, EMAIL:Praveen@nexgenproject.com
NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
This document presents a method for real-time facial expression analysis using principal component analysis (PCA). The method involves detecting faces, extracting expression features from the eye and mouth regions, applying PCA to extract texture features, and using a support vector machine classifier to classify expressions. The proposed approach was tested on a database of facial images with expressions categorized as happy, angry, disgust, sad, or neutral. PCA was used to select the most relevant eigenfaces and reduce the dimensionality of the feature space for more efficient classification of expressions in real-time.
This document discusses using visuals and images in education. It explains that visuals can help address different learner preferences and styles, enhance learning and retention, and bring deeper understanding to complex subjects. It then discusses visual literacy, defining it as the ability to interpret and derive meaning from visual content. The document outlines some key visual elements or building blocks of images like framing, angle, lighting, and scale, and how understanding these elements can help students analyze and interpret visual information.
This document summarizes various clustering techniques used for image segmentation. It discusses clustering approaches like k-means, hierarchical clustering, spectral clustering (Ncut), and relevance feedback. The document provides examples and explanations of how these techniques work. For instance, it explains that k-means clustering aims to group images into predefined clusters by minimizing intra-cluster distances. It also gives an example of how hierarchical clustering builds clusters in a tree structure. In summary, the document surveys popular clustering methods and how they can segment images in an effective manner by grouping similar pixels or images.
A study on attention-based deep learning architecture model for image captioningIAESIJAI
Image captioning has been widely studied due to its ability in a visual scene understanding. Automatic visual scene understanding is useful for remote monitoring system and visually impaired people. Attention-based models, including transformer, are the current state-of-the-art architectures used in developing image captioning model. This study examines the works in the development of image captioning model, especially models that are developed based on attention mechanism. The architecture, the dataset, and the evaluation metrics analysis are done to the collected works. A general flow of image captioning model development is also presented. The literature search process carried out on Google Scholar. There are 36 literatures used in this study, including a specific image captioning development in Indonesian. It is done to take one point of view of image captioning development in a low resource language. Studies using transformer model generally achieves higher evaluation metric scores. In our finding, the highest evaluation scores on the consensus-based image description evaluation (CIDEr) c5 and c40 metrics are 138.5 and 140.5 respectively. This study gives a baseline on future development of image captioning model and brings the general concept of the image captioning development process including a picture of the development in low resource language.
IMAGE CONTENT DESCRIPTION USING LSTM APPROACHcsandit
In this digital world, artificial intelligence has provided solutions to many problems, likewise to
encounter problems related to digital images and operations related to the extensive set of
images. We should learn how to analyze an image, and for that, we need feature extraction of
the content of that image. Image description methods involve natural language processing and
concepts of computer vision. The purpose of this work is to provide an efficient and accurate
image description of an unknown image by using deep learning methods. We propose a novel
generative robust model that trains a Deep Neural Network to learn about image features after
extracting information about the content of images, for that we used the novel combination of
CNN and LSTM. We trained our model on MSCOCO dataset, which provides set of annotations for a particular image, and after the model is fully automated, we tested it by providing raw images. And also several experiments are performed to check efficiency and robustness of the system, for that we have calculated BLUE Score.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
FACE PHOTO-SKETCH RECOGNITION USING DEEP LEARNING TECHNIQUES - A REVIEWIRJET Journal
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3. Table of
Contents
● What is VQA?
● Requirements
● Explicit Visual Attention
● Related Research Works
● Architecture of Model
● Training of Model
● Advantages and Limitations
● Application and Scope
4. Visual Question Answering (VQA) is a research area at the intersection of computer vision
and natural language processing. It involves developing algorithms and models that can
understand and answer questions about visual content, such as images or videos. (Vinyals
et al., 2015).
What is Visual Question Answering?
5. [1] V. Lioutas, N. Passalis, and A. Tefas, "Visual Question Answering using Explicit
Visual Attention," Dept. of Informatics, Aristotle University of Thessaloniki,
Thessaloniki, 54124, Greece.
7. REQUIREMENTS
Implementing a Visual Question Answering (VQA) project requires a combination
of technical skills, tools, and resources. Here's a list of requirements you'll need to
consider:
1. Programming Languages and Libraries:
Python: The primary language for implementing machine learning and deep
learning models.
Deep Learning Frameworks: TensorFlow, PyTorch, or Keras for building and
training neural networks.
Libraries for Image Processing: OpenCV, PIL (Python Imaging Library) for image
preprocessing and manipulation.
8. 2. Hardware and Software:
A computer with sufficient computational power, to speed up training.
Development environment with Python and necessary libraries installed.
3. Dataset:
Gather or obtain a VQA dataset that includes images, questions, and
corresponding answers. We have used the COCO-QA(Ren et al. 2015a, b,c)
dataset.
4. Pretrained Models and Embeddings:
Pretrained image classification models (e.g., ResNet, VGG, etc.) for feature
extraction. Pre-trained word embeddings (e.g., GloVe, Word2Vec) for representing
words in questions.
10. Explicit Visual Attention
Explicit Visual Attention in the context of Visual Question Answering (VQA) is a
concept that enhances the accuracy and interpretability of VQA models by
explicitly guiding the model's focus to specific regions of an image that are
relevant to the question being asked.
[1] S. Manmadhan and B. C. Kovoor,
"Visual question answering: a state-of-the-
art review," IEEE, 8 April 2020.
11. Explicit Visual Attention
Working
[1] V. Lioutas, N. Passalis, and A. Tefas, "Visual Question Answering using Explicit Visual
Attention," Dept. of Informatics, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece.
12. How Explicit Visual Attention works?
● Identifying relevant parts of an image for a question.
● Generating an attention map highlighting those regions.
● Combining the map with the question's context.
● Enhancing the model's focus on crucial visual details.
● Leading to accurate and contextually aware answers in Visual
Question Answering.
[1] V. Lioutas, N. Passalis, and A. Tefas, "Visual Question Answering using Explicit Visual
Attention," Dept. of Informatics, Aristotle University of Thessaloniki, Thessaloniki, 54124,
Greece.
13. Significance of Using Explicit Visual Attention in VQA
Beyond Soft Attention:
Explicit Visual Attention takes attention mechanisms a step further. Unlike soft
attention that softly distributes focus, explicit attention directly guides the model's
gaze to specific image regions. This precise focus enhances the model's
understanding of image-question relationships, leading to more accurate and
contextually relevant answers.
Interpretable Focus:
Explicit Visual Attention's localized focus provides transparency into how the
model processes images and questions.
[1] P. Wang, Q. Wu, C. Shen, A. van den Hengel, and A. Dick, "Explicit Knowledge-based
Reasoning for Visual Question Answering," School of Computer Science, The University of
Adelaide, November 13, 2015.
14. Addressing Ambiguity with Clarity
VQA questions can be ambiguous. Explicit Visual Attention helps the model
address ambiguity by attending to the most relevant part of the image, clarifying
the context and intent of the question. This ensures more accurate answers, even
when the question phrasing might lead to multiple interpretations.
Capturing Fine Details
Images often contain intricate details critical for answering specific questions.
Explicit Visual Attention ensures the model doesn't miss these details, leading to
more precise answers.
[1] P. Wang, Q. Wu, C. Shen, A. van den Hengel, and A. Dick, "Explicit Knowledge-
based Reasoning for Visual Question Answering," School of Computer Science, The
University of Adelaide, November 13, 2015.
15. Existing Projects and Research Works
An explicitly trained attention model that is inspired by the theory of
pictorial superiority effect. In this model, it use attention-oriented
word embeddings that increase the efficiency of learning common
representation spaces.
[1] V. Lioutas, N. Passalis, and A. Tefas, "Visual Question
Answering using Explicit Visual Attention," IEEE, 2018.
16. A novel model called Multi-modal Explicit Sparse Attention
Networks (MESAN), which concentrates the model’s attention by
explicitly selecting the parts of the input features that are the most
relevant to answering the input question.
[1] Z. Guo and D. Han, "Multi-Modal Explicit Sparse Attention
Networks for Visual Question Answering," College of
Information Engineering, Shanghai, 2020.
17. [1] Y. Liu, Y. Guo, J. Yin, X. Song, S. University, China, W. Liu, China University of Petroleum
(East China), China, and L. Nie, Shandong University, China, "Answer Questions with Right
Image Regions: A Visual Attention Regularization Approach," IEEE.
18. How explicit visual attention is different from others?
Explicit Visual Attention is a specific approach within the broader field of attention
mechanisms used in tasks like Visual Question Answering (VQA). It differentiates
itself from other attention mechanisms through its focus on providing a more
controlled and interpretable way of directing a model's attention to specific regions
of an image.
Soft Attention: It distributes attention across the entire image, allowing the model
to consider all regions simultaneously. The model assigns varying degrees of
attention to different parts of the image, but there's no strict focus on specific
regions.
[1] Z. Guo and D. Han, "Multi-Modal Explicit Sparse Attention Networks for Visual Question
Answering," College of Information Engineering, Shanghai, 2020.
19. Hard Attention:
It forces the model to focus on a single region of the image. Only one region is
chosen for processing, leading to limitations in handling complex scenes or
capturing multiple relevant details. It can be computationally expensive and may
not always be practical for complex tasks.
Implicit Visual Attention:
It refers to attention mechanisms where the model's focus on image regions is
learned implicitly as part of the model's internal processes. While it may capture
relevant regions, the exact regions attended to might not be transparent or easily
interpretable.
[1] V. Lioutas, N. Passalis, and A. Tefas, "Visual Question Answering using Explicit Visual
Attention," Dept. of Informatics, Aristotle University of Thessaloniki, Thessaloniki, 54124,
Greece.
20. Architecture of this model:
[1] Z. Lei, G. Zhang, L. Wu, K. Zhang, and R. Liang, "A Multi-level Mesh Mutual Attention Model
for Visual Question Answering," IEEE, 2022.
21. Input Layer: The input consists of an image and a question. The image is fed into
a CNN, and the question is tokenized and processed by an RNN.
CNN (Convolutional Neural Network): The CNN processes the image and
extracts high-level visual features. These features capture the image's content,
including objects, patterns, and structures.
RNN (Recurrent Neural Network): The RNN processes the question tokens
sequentially and generates a question representation.The question representation
captures the semantic context of the question.
Attention Mechanism: The attention mechanism combines the image features
from the CNN and the question features from the RNN. It computes attention
scores that determine the importance of different image regions for answering the
question.
22. Attention Map Generation:
The attention scores are used to generate an attention map that highlights
relevant regions of the image. The attention map guides the model's focus on
specific image regions.
Enhanced Image Features:
The attention map is combined with the original image features, creating
enhanced image features. These enhanced features emphasize the regions
of the image that are important for the given question.
Joint Representation:
The enhanced image features and the question representation from the RNN
are combined. This creates a joint representation that incorporates both visual
and textual information, with explicit attention.
23. Answer Prediction:
The joint representation is used to predict the answer to the question. The
model leverages the enhanced features and contextually focused attention for
more accurate responses.
Attention Visualization:
Optionally, the attention map can be visualized to show which regions of the
image were attended to during answer prediction. This helps users
understand the model's reasoning process.
Training and Optimization:
The model is trained using labeled VQA data, which includes questions,
images, and answers. During training, the model learns to generate accurate
attention maps and make correct predictions.
24. [1] Y. Liu, Y. Guo, J. Yin, X. Song, S. University, China, W. Liu, China University of
Petroleum (East China), China, and L. Nie, Shandong University, China, "Answer
Questions with Right Image Regions: A Visual Attention Regularization Approach,"
IEEE.
25. Training of this model:
Data Preparation:
Gather a dataset of images, corresponding questions, and ground truth answers.
Preprocess the images by resizing, normalizing pixel values, and extracting features using a
pre-trained CNN. Tokenize and encode the questions using word embeddings and an RNN.
Attention Mechanism Setup:
Choose an attention mechanism architecture that combines image and question features to
generate attention scores. Design the attention mechanism to generate attention maps that
highlight important image regions.
26. Loss Function:
The loss function measures the difference between predicted answers and ground truth
answers. We will choose a loss function that suits the problem. Common loss functions for
VQA include.
Cross-Entropy Loss: Suitable for single-label classification tasks where each question has a
single correct answer.
Ranking Loss: Used when multiple correct answers are available for a question. The model
is trained to rank the correct answer higher than incorrect ones.
Attention Mechanism Loss: If using attention, consider incorporating attention-related loss
terms that encourage the model to focus on the right image regions.
27. Fine-Tuning and Transfer Learning:
Leverage pre-trained models to bootstrap our VQA model's performance. Transfer Learning:
Start with pre-trained models for both vision (CNNs) and language (BERT, GPT) to provide a
strong initial representation for your model.
Fine-Tuning: Fine-tune the pre-trained models on our VQA dataset to adapt them to the
specific task.
Validation and Evaluation:
Regularly evaluate the model's performance on a validation set to monitor its progress.
Validation Metrics: Use accuracy, top-k accuracy, or other relevant metrics to track
performance. Hyperparameter Tuning: Adjust hyperparameters based on validation
performance.
28. Comparison of results
[1] S. Manmadhan and B. C. Kovoor, "Visual
question answering: a state-of-the-art
review," IEEE, 2020.
[1] V. Lioutas, N. Passalis, and A. Tefas, "Visual
Question Answering using Explicit Visual
Attention," Dept. of Informatics, Aristotle
University of Thessaloniki, Thessaloniki, 54124,
Greece.
29. Graphical Interpretation
[1] V. Lioutas, N. Passalis, and A. Tefas, "Explicit ensemble attention learning for improving visual
question answering," Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki
54124, Greece.
30. Advantages of this model:
Accurate Answers: By combining visual information from CNNs with semantic context from
RNNs, the architecture enables the model to generate more accurate answers. The explicit
attention mechanism further enhances accuracy by focusing on relevant image
regions.(Lopes et al., 2017).
Contextual Understanding: The joint representation created by merging image features
and question features captures the contextual relationship between visual and textual
elements, facilitating a deeper understanding of the question.(Vinyals et al., 2015).
Handling Complex Scenes: CNNs excel at capturing visual details in images, enabling the
model to handle complex scenes with multiple objects, textures, and patterns, enhancing the
model's ability to provide meaningful answers.(Antol et al., 2015).
31. Limitations of this model:
Computational Complexity: Combining CNNs and RNNs can lead to higher computational
demands during both training and inference. This might result in longer training times and
slower predictions, especially for real-time applications(Szegedy et al., 2015).
Data Requirements: Training an architecture with both CNNs and RNNs requires labeled
data that includes images, questions, and answers. Gathering such data can be time-
consuming and expensive(Antol et al., 2015).
Hyperparameter Tuning: The architecture involves multiple components with their own set
of hyperparameters (e.g., learning rates, sequence lengths). Fine-tuning these parameters
for optimal performance can be challenging and time-intensive(Xu et al., 2015).
Overfitting: The combination of complex components might increase the risk of overfitting,
where the model performs well on the training data but struggles to generalize to new,
unseen examples(Malinowski et al., 2015).
32. APPLICATIONS
Visual Question Answering (VQA) has several potential applications across
various domains. Here are some ways in which VQA can be used:
1)Image Captioning and Enhancement: VQA can be used to automatically
generate descriptive captions for images or videos, enhancing their accessibility
and understandability. This is particularly useful for visually impaired individuals
who can benefit from textual descriptions of visual content(Vinyals et al., 2015).
2)Interactive AI Systems: VQA can be integrated into interactive AI systems,
such as virtual assistants or chatbots, to allow users to ask questions about
images or scenes. For example, a user could ask a virtual shopping assistant
about the details of a product shown in an image(Antol et al., 2015).
33. 3) Visual Content Retrieval: VQA can assist in retrieving specific visual content
from large databases based on textual queries. This is helpful for searching
through image or video collections, enabling more intuitive and specific
searches(Gordo et al., 2017).
4)Education and Learning: VQA can be used in educational settings to create
interactive learning materials. Students can ask questions about images or
diagrams to deepen their understanding of the subject matter(Malinowski et al.,
2015).
5)Medical Imaging: In the medical field, VQA can aid doctors in understanding
medical images like X-rays. By asking questions about specific aspects of the
images, clinicians can receive insights to support diagnosis decisions(Lopes et
al., 2017).
6)Safety and Surveillance: VQA can be used in surveillance systems to
34. SCOPE
1. Improved Performance: Explicit visual attention has been shown to improve
the performance of models in tasks that involve understanding images in the
context of natural language questions(Anderson et al., 2018).
2. Interpretability: Attention maps generated by explicit visual attention provide
insights into the reasoning process of the model. Users and researchers can
visualize these maps to understand which image regions contribute most to the
answer, making the decision-making process more transparent and
interpretable(Xu et al., 2015).
3. Fine-Grained Understanding: Explicit attention mechanisms enable models to
pay selective attention to specific objects, regions, or details within an image,
leading to a deeper understanding of the visual content. (Xu et al., 2015).
35. 4. Robustness to Clutter: In images with multiple objects or complex scenes,
explicit visual attention can help the model focus on the relevant objects or regions
while ignoring distractions or irrelevant parts of the image(Ba et al., 2014).
5. Adaptability to Different Questions: Attention mechanisms can adapt to the
nature of the question being asked. For example, for questions like "What color is
the apple?" the attention can be directed to the region containing the apple(Luong
et al., 2015).
6. Localization in Images: Explicit visual attention can be extended to tasks like
image localization, where the model is required to identify and highlight specific
objects or regions within an image(Xu et al., 2015).
7. Personalized User Interfaces: In applications involving human-computer
interaction, attention maps can be used to create user interfaces that highlight the
areas of an image the model is focusing on.(Chen et al., 2018).
36. CURRENT STATE OF ART
There are several interesting future work directions. Thus, more deliberate
techniques could be employed for combining multiple attention models, e.g., the
AdaBoost technique. Furthermore, in the proposed method the attention model
was not trained when a question does not contain ground truth bounding boxes.
Exploiting the information contained in these image-question pairs, in a way
similar to the implicit attention, can lead to a hybrid implicit explicit attention model
that can further improve the visual question answering accuracy.
[1] V. Lioutas, N. Passalis, and A. Tefas, "Visual Question Answering using Explicit Visual
Attention," Dept. of Informatics, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece.
37. Advanced pooling techniques, such as BoF pooling, can be also used to improve
the scale invariance of the attention model and provide more reliable attention
information. The proposed methodology can also be applied to other tasks that
require high level visual understanding, such as image caption generation, and
video caption generation. Finally, the proposed approach could also be used to
improve the precision of multi-modal information retrieval, where providing
accurate visual attention information given a textual query from the user is of
critical significance.
These models integrate attention mechanisms and have led to competitive results.
Pretrained language models like BERT have been fine-tuned for VQA. By
leveraging large amounts of textual data, these models can capture contextual
relationships between question and answer elements.
[1] Y. Liu, Y. Guo, J. Yin, X. Song, S. University, China, W. Liu, China University of Petroleum (East
China), China, and L. Nie, Shandong University, China, "Answer Questions with Right Image
Regions: A Visual Attention Regularization Approach," IEEE.
38. CONCLUSION
Visual Question Answering is an interesting combination of Computer Vision
and Natural Language Processing which is growing by utilizing the power of
deep learning methods. It is very challenging task where requires solving many
subtasks like object detection, activity recognition, spatial relationships
between objects, commonsense reasoning etc
In conclusion, our implementation of explicit visual attention in our VQA model,
which employs CNNs for computer vision and RNNs for text processing, has
yielded substantial improvements in terms of both accuracy and loss functions.
The main obstacle in the journey of VQA models towards AI dream is that it is
not clear what the source of improvement is and to which extent the model
understood the visual-language concepts.
[1] S. Manmadhan and B. C. Kovoor, "Visual question answering: a state-of-the-art
review," IEEE, 8 April 2020.
39. REFERENCES
Authors: Vasileios Lioutas, Nikolaos Passalis, Anastasios Tefas
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8351158
Authors: YIBING LIU, YANGYANG GUO, JIANHUA YIN, and XUEMENG SONG
https://arxiv.org/pdf/2102.01916.pdf
Authors: Zhi Lei, Guixian Zhang, Lijuan Wu, Kui Zhang, Rongjiao Liang
https://link.springer.com/article/10.1007/s41019-022-00200-9
Authors: Zihan Guo and Dezhi Han
https://www.mdpi.com/1424-8220/20/23/6758