The machine learning algorithm proposed in this paper is suitable for Big Data multimodal datasets and in particular for integrating image and speech data. Preliminary feature extraction is based on convolutional neur...
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Multimodal human understanding and analysis are emerging research areas that cut through several disciplines like Computer vision (Cv), Natural Language processing (NLP), Speech processing, Human-Computer Interaction ...
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ISBN:
(纸本)9798400706028
Multimodal human understanding and analysis are emerging research areas that cut through several disciplines like Computer vision (Cv), Natural Language processing (NLP), Speech processing, Human-Computer Interaction (HCI), and Multimedia. Several multimodal learning techniques have recently shown the benefit of combining multiple modalities in image-text, audio-visual and video representation learning and various downstream multimodal tasks. At the core, these methods focus on modelling the modalities and their complex interactions by using large amounts of data, different loss functions and deep neural network architectures. However, for many Web and Social media applications, there is the need to model the human, including the understanding of human behaviour and perception. For this, it becomes important to consider interdisciplinary approaches, including social sciences and psychology. The core is understanding various cross-modal relations, quantifying bias such as social biases, and the applicability of models to real-world problems. Interdisciplinary theories such as semiotics or gestalt psychology can provide additional insights on perceptual understanding through signs and symbols across multiple modalities. In general, these theories provide a compelling view of multimodality and perception that can further expand computational research and multimedia applications on the Web and Social media. The theme of the MUWS workshop, multimodal human understanding, includes various interdisciplinary challenges related to social bias analyses, multimodal representation learning, detection of human impressions or sentiment, hate speech, sarcasm in multimodal data, multimodal rhetoric and semantics, and related topics. The MUWS workshop is an interactive event and includes keynotes by relevant experts, a poster session, research presentations and discussion.
The paper discusses the most current advancements in image analysis and computer vision systems and how they are being used to assess the grade of food products. Computer vision is a quick, reliable, and objective exa...
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Medical imageprocessing is one of the significant fields to identify the diseases as earlier to diagnose them appropriately. The brain tumor segmentation process is sub branch of a medical imageprocessing field. The...
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This research paper presents a novel approach for vehicle tracking and counting utilizing the advanced object detection model YOLOv8 in the field of imageprocessing. The accurate monitoring of vehicular traffic is cr...
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Given the rise of multimedia content, human translators increasingly focus on culturally adapting not only words but also other modalities such as images to convey the same meaning. While several applications stand to...
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Glaucoma is a prevalent cause of blindness *** not treated promptly,it can cause vision and quality of life to *** to statistics,glaucoma affects approximately 65 million individuals *** image segmentation depends on ...
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Glaucoma is a prevalent cause of blindness *** not treated promptly,it can cause vision and quality of life to *** to statistics,glaucoma affects approximately 65 million individuals *** image segmentation depends on the optic disc(OD)and optic cup(OC).This paper proposes a computational model to segment and classify retinal fundus images for glaucoma *** data augmentation techniques were applied to prevent overfitting while employing several data pre-processing approaches to improve the image quality and achieve high *** segmentation models are based on an attention U-Net with three separate convolutional neural networks(CNNs)backbones:Inception-v3,visual geometry group 19(vGG19),and residual neural network 50(ResNet50).The classification models also employ a modified version of the above three CNN *** the RIM-ONE dataset,the attention U-Net with the ResNet50 model as the encoder backbone,achieved the best accuracy of 99.58%in segmenting *** Inception-v3 model had the highest accuracy of 98.79%for glaucoma classification among the evaluated segmentation,followed by the modified classification architectures.
This research proposes a distance estimation method using Mono Camera-based object detection and depth estimation to generate Point Cloud data. The study aims to enhance the applicability of Mono Cameras in autonomous...
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The success of deep learning over the traditional machine learning techniques in handling artificial intelligence application tasks such as imageprocessing, computer vision, object detection, speech recognition, medi...
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applications for classifying Synthetic Aperture Radar (SAR) images are critical to environmental monitoring, urban planning, and land resource surveying. Fusion approaches work well for increasing SAR image categoriza...
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