ViTs are Everywhere: A Comprehensive Study Showcasing Vision Transformers in Different Domain

Transformer design is the de facto standard for natural language processing tasks. The success of the transformer design in natural language processing has lately piqued the interest of researchers in the domain of computer vision. When compared to Convolutional Neural Networks (CNNs), Vision Transformers (ViTs) are becoming more popular and dominant solutions for many vision problems. Transformer-based models outperform other types of networks, such as convolutional and recurrent neural networks, in a range of visual benchmarks. We evaluate various vision transformer models in this work by dividing them into distinct jobs and examining their benefits and drawbacks. ViTs can overcome several possible difficulties with convolutional neural networks (CNNs). The goal of this survey is to show the first use of ViTs in CV. In the first phase, we categorize various CV applications where ViTs are appropriate. Image classification, object identification, image segmentation, video transformer, image denoising, and NAS are all CV applications. Our next step will be to analyze the state-of-the-art in each area and identify the models that are currently available. In addition, we outline numerous open research difficulties as well as prospective research possibilities.Comment: ICCD-2023. arXiv admin note: substantial text overlap with arXiv:2208.04309 by other author

DANet: Enhancing Small Object Detection through an Efficient Deformable Attention Network

Efficient and accurate detection of small objects in manufacturing settings, such as defects and cracks, is crucial for ensuring product quality and safety. To address this issue, we proposed a comprehensive strategy by synergizing Faster R-CNN with cutting-edge methods. By combining Faster R-CNN with Feature Pyramid Network, we enable the model to efficiently handle multi-scale features intrinsic to manufacturing environments. Additionally, Deformable Net is used that contorts and conforms to the geometric variations of defects, bringing precision in detecting even the minuscule and complex features. Then, we incorporated an attention mechanism called Convolutional Block Attention Module in each block of our base ResNet50 network to selectively emphasize informative features and suppress less useful ones. After that we incorporated RoI Align, replacing RoI Pooling for finer region-of-interest alignment and finally the integration of Focal Loss effectively handles class imbalance, crucial for rare defect occurrences. The rigorous evaluation of our model on both the NEU-DET and Pascal VOC datasets underscores its robust performance and generalization capabilities. On the NEU-DET dataset, our model exhibited a profound understanding of steel defects, achieving state-of-the-art accuracy in identifying various defects. Simultaneously, when evaluated on the Pascal VOC dataset, our model showcases its ability to detect objects across a wide spectrum of categories within complex and small scenes.Comment: ICCD-2

Comprehensive assessment of metabolic syndrome among rural Bangladeshi women

Background: Metabolic syndrome (MS), defined as a constellation of cardiovascular disease (CVD) risk factors, is one of the fastest growing public health burdens in the Asia-Pacific region. This trend is despite the fact that people in this region are no more overweight than Europeans and Americans. Unfortunately, in South Asia, MS screening has only been performed in a few countries other than Bangladesh. Therefore the present study is designed to conduct a comprehensive screening of MS in Bangladeshi rural women, which includes estimation of prevalence and assessment of risk factor. Methods: A total of 1535 rural Bangladesh women aged ≥ 15 years were studied using a population based crosssectional survey. The prevalence of MS was estimated using NCEP ATP III, modified NCEP ATP III and IDF criteria. Results: The prevalence rates of MS were 25.60 % (NCEP ATP III), 36.68 % (modified NCEP ATP III), and 19.80 % (IDF), as revealed by the present study. Furthermore, based on the NCEP ATP III criteria, 11.60 % of the subjects were found to have excess waist circumference; 29.12 % had elevated blood pressure, 30.42 % had elevated fasting plasma glucose level, 85.47 % had low HDL values and 26.91 % had increased triglyceride values. Low plasma HDL level was found to be the most common abnormality in the target population and elevated waist circumference was the least frequent component. Conclusions: The present study reveals a high prevalence of MS and its associated risk factors in rural Bangladeshi women. These findings are important in that they provide insights that will be helpful in formulating effective public health policy, notably the development of future health prevention strategies in Bangladesh