Using sarcasm on social media platforms to express negative opinions towards a person or object has become increasingly ***,detecting sarcasm in various forms of communication can be difficult due to conflicting *** t...
详细信息
Using sarcasm on social media platforms to express negative opinions towards a person or object has become increasingly ***,detecting sarcasm in various forms of communication can be difficult due to conflicting *** this paper,we introduce a contrasting sentiment-based model for multimodal sarcasm detection(CS4MSD),which identifies inconsistent emotions by leveraging the CLIP knowledge module to produce sentiment features in both text and ***,five external sentiments are introduced to prompt the model learning sentimental preferences among ***,we highlight the importance of verbal descriptions embedded in illustrations and incorporate additional knowledge-sharing modules to fuse such imagelike *** results demonstrate that our model achieves state-of-the-art performance on the public multimodal sarcasm dataset.
Nowadays, high energy amount is being wasted by computing servers and personal electronic devices, which produce a high amount of carbon dioxide. Thus, it is required to decrease energy usage and pollution. Many appli...
详细信息
The present study advances object detection and tracking techniques by proposing a novel model combining Automated Image Annotation with Inception v2-based Faster RCNN (AIA-IFRCNN). The research methodology utilizes t...
详细信息
Eye coloboma is a type of congenital eye abnormality that causes developmental abnormalities of the eye which may culminate in eye disorders that fundamentally derange eyesight. Fractional and timely diagnosis remains...
详细信息
ISBN:
(纸本)9798331508845
Eye coloboma is a type of congenital eye abnormality that causes developmental abnormalities of the eye which may culminate in eye disorders that fundamentally derange eyesight. Fractional and timely diagnosis remains highly important to its proper managing and treating. This work proposes a model with a Transformer-Enhanced U-Net design implemented with Transfer Learning to enhance the ability of the model to detect and segment eye coloboma from medical imaging data. This work builds upon the benefits of U-Net which has shown promising results especially in the biomedical image segmentation problem through addition of transformer modules that introduce long range dependencies and contextual information. This enhancement makes helps the model to pay more attention to such minor aspects as coloboma and general diagnostic accuracy consequently. Thanks to Transfer Learning with a pretrained backbone, for example Efficient Net the system is provided with Feature Representations recovered from large datasets that are already available and training time is drastically cut short while the performance of the system is not compromised at *** approach includes fine-tuning of Transformer-Enhanced U-Net model using a selected set of eye images, labelled for coloboma existence. By including the self-attention mechanism, the model is capable of focusing on the critical regions in the image, increasing its responsiveness to any form of coloboma. Preliminary tests show that this scheme performs better than the standard convolutional networks for segmenting complex regions by minimizing false negatives. Further, the model's flexibility to different imaging conditions demonstrates its suitability in realistic clinical applications. The objective of this research is to come up with a dependable, Auto-Generated diagnostic tool that will help the ophthalmologists in the early diagnosis of the eye related illnesses hence making increased positive outcomes for eye patients. The Transf
Eye health has become a global health concern and attracted broad *** the years,researchers have proposed many state-of-the-art convolutional neural networks(CNNs)to assist ophthalmologists in diagnosing ocular diseas...
详细信息
Eye health has become a global health concern and attracted broad *** the years,researchers have proposed many state-of-the-art convolutional neural networks(CNNs)to assist ophthalmologists in diagnosing ocular diseases efficiently and ***,most existing methods were dedicated to constructing sophisticated CNNs,inevitably ignoring the trade-off between performance and model *** alleviate this paradox,this paper proposes a lightweight yet efficient network architecture,mixeddecomposed convolutional network(MDNet),to recognise ocular *** MDNet,we introduce a novel mixed-decomposed depthwise convolution method,which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low-resolution and high-resolution patterns by using fewer computations and fewer *** conduct extensive experiments on the clinical anterior segment optical coherence tomography(AS-OCT),LAG,University of California San Diego,and CIFAR-100 *** results show our MDNet achieves a better trade-off between the performance and model complexity than efficient CNNs including MobileNets and ***,our MDNet outperforms MobileNets by 2.5%of accuracy by using 22%fewer parameters and 30%fewer computations on the AS-OCT dataset.
The AI-Enhanced Learning Assistant Platform is a revolutionary system designed to enhance learning, with cutting-edge features like question and answer generation, answer evaluation, identification of weak areas, recu...
详细信息
We are currently in a period of upheaval, as many new technologies are emerging that open up new possibilities to shape our everyday lives. Particularly, within the field of Personalized Human-computer Interaction we ...
详细信息
This study introduces a label-free biosensing method for biomolecule detection utilizing an InP/AlGaAs charge plasma dielectric-modulated vertical tunnel field-effect transistor (InP/AlGaAs VTFET) featuring TaN as the...
详细信息
Unusual crowd analysis is an important problem in surveillance video due to their features cannot be extracted efficiently on the crowd scenes. To overcome this challenge, this paper introduced the appearance and moti...
详细信息
This project seeks to identify urban, traffic, and industrial noise hazards. This is accomplished by Arduino, which uses sound sensors to recognize and differentiate between light and loud noise. With the help of the ...
详细信息
暂无评论