As a new type of deep neural network, the broad learning system has been attracting attention since it was proposed. It shows good performance in image processing, face recognition, etc. To train a broad learning mode...
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Sentiment analysis is a technique that is occasionally used to examine information in textual form and extract thoughts from the text. the goal of sentiment analysis is to determine if users have a good or negative im...
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the application of AI technology in advertisement placement strategy has become increasingly important. thus, advertisement placement strategies were studied using AI hotspot tracking technology through in-depth analy...
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Describing images in natural language is a fundamental step towards the automatic modeling of connections between the visual and textual modalities. In this paper we present CaMEL, a novel Transformer-based architectu...
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ISBN:
(数字)9781665490627
ISBN:
(纸本)9781665490627
Describing images in natural language is a fundamental step towards the automatic modeling of connections between the visual and textual modalities. In this paper we present CaMEL, a novel Transformer-based architecture for image captioning. Our proposed approach leverages the interaction of two interconnected language models that learn from each other during the training phase. the interplay between the two language models follows a mean teacher learning paradigm with knowledge distillation. Experimentally, we assess the effectiveness of the proposed solution on the COCO dataset and in conjunction with different visual feature extractors. When comparing with existing proposals, we demonstrate that our model provides state-of-the-art caption quality with a significantly reduced number of parameters. According to the CIDEr metric, we obtain a new state of the art on COCO when training without using external data. the source code and trained models will be made publicly available at: https://***/aimagelab/camel.
the proceedings contain 32 papers. the topics discussed include: a study on exploit development;mining of microsatellites in genomic data using a hybrid approach with range tree applications;empirical evaluation of in...
ISBN:
(纸本)9798350309904
the proceedings contain 32 papers. the topics discussed include: a study on exploit development;mining of microsatellites in genomic data using a hybrid approach with range tree applications;empirical evaluation of in silico microsatellites mining tools designed using NextGen technology in crops;comparative study of ransomwares;a comparative analysis of stock price prediction techniques;a comparative study of different convolution neural network architectures for hyperspectral image classification;uses of social media and computer technologies for guest satisfaction and recommendation analysis using machinelearning in hotels industries;acceptance of the nanotechnology in the food industry for customer satisfaction;a novel scheme for feature selection using filter approach;machinelearning approaches for crop yield prediction: a review;and significant impact of 5G technology worldwide: with special reference to specific industries and countries.
Multi-label learning has garnered significant attention and application in the domains of machinelearning, datamining, and patternrecognition. However, existing multi-label learning algorithms often fall short in a...
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In global aquaculture, Penaeus vannamei stands out due to its immense economic importance. Water quality, being pivotal for its successful cultivation, demands precise evaluation techniques. this research undertook a ...
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the analysis of video data in machinelearning classification tasks has become a significant topic in both research and application areas. the accurate classification of video data by frame sets, particularly when the...
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ISBN:
(纸本)9789819603534;9789819603541
the analysis of video data in machinelearning classification tasks has become a significant topic in both research and application areas. the accurate classification of video data by frame sets, particularly when the content contains objects that are in a state of dynamic change, represents a significant and complex undertaking. With regard to the temporal phase of video, the thesis proposes an unsupervised classification method based on non-negative tensor factorization (NTF). In order to transform the video data into low-dimensional datathat can be classified by unsupervised clustering, the non-negative tensor factorization method is used to reduce the dimension of the input tensor, resulting in the generation of a non-negative rank-1 tensor combination. Following dimensionality reduction, the extracted features are divided into clusters through Mean Shift, thereby facilitating classification and recognition of video sequences. Experiments on the Coil-100 dataset of item image sequences at different angles and other dynamic pose-changing videos demonstrate the effectiveness of the method.
Withthe rapid development of information technology, the traditional information management system faces the challenge of efficiency and accuracy when dealing with complex data and decision-making problems. In order ...
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Pavement management systems play a vital role in the development of a country as it is a very important part of the economy. Maintaining a good quality of the road is the key duty of the road authorities. they require...
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