论文标题
高斯光滑的语义特征（GSSF） - 使用MSCOCO框架探索印度语言视觉字幕（孟加拉语）的语言方面
Gaussian Smoothen Semantic Features (GSSF) -- Exploring the Linguistic Aspects of Visual Captioning in Indian Languages (Bengali) Using MSCOCO Framework
论文作者
论文摘要
在这项工作中，我们介绍了高斯平滑的语义特征（GSSF），以便为印度区域语言的图像字幕进行更好的语义选择，并介绍了一个程序，我们使用了现有的翻译和英语众包句子进行培训。我们已经证明，这种体系结构是一个有前途的替代来源，其中资源有些紧缩。这项工作的主要贡献是，孟加拉语的深度学习体系结构（是世界上第五种语言中的第五种语言），具有完全不同的语法和语言属性。我们已经证明，这些功能在复杂的应用程序中效果很好，例如从图像上下文中的语言生成，并且可以通过引入约束，更广泛的功能和独特的特征空间来多样化表示形式。我们还确定，当我们使用传统的LSTM和特征分解网络使用平滑的语义张量时，我们可以实现绝对的精度和多样性。通过更好的学习体系结构，我们成功地建立了一种自动化算法和评估程序，可以帮助评估有效的应用程序，而无需进行专业知识和人类干预。
In this work, we have introduced Gaussian Smoothen Semantic Features (GSSF) for Better Semantic Selection for Indian regional language-based image captioning and introduced a procedure where we used the existing translation and English crowd-sourced sentences for training. We have shown that this architecture is a promising alternative source, where there is a crunch in resources. Our main contribution of this work is the development of deep learning architectures for the Bengali language (is the fifth widely spoken language in the world) with a completely different grammar and language attributes. We have shown that these are working well for complex applications like language generation from image contexts and can diversify the representation through introducing constraints, more extensive features, and unique feature spaces. We also established that we could achieve absolute precision and diversity when we use smoothened semantic tensor with the traditional LSTM and feature decomposition networks. With better learning architecture, we succeeded in establishing an automated algorithm and assessment procedure that can help in the evaluation of competent applications without the requirement for expertise and human intervention.
