TextMage: The Automated Bangla Caption Generator Based On Deep Learning

BanglaMM-Disaster: 一种基于Transformer的多模态深度学习框架，用于孟加拉语多类灾害分类

BanglaMM-Disaster: A Multimodal Transformer-Based Deep Learning Framework for Multiclass Disaster Classification in Bangla

BanglaMM-Disaster通过结合文本和视觉数据，提出了一种多模态深度学习框架，用于孟加拉语多类灾害分类，提升了灾害响应效率。

Chittron: An Automatic Bangla Image Captioning System

Automatic image caption generation aims to produce an accurate description of an image in natural language automatically. However, Bangla, the fifth most widely spoken language in the world, is lagging considerably in the research and development of such domain. Besides, while there are many established data sets to related to image annotation in English, no such resource exists for Bangla yet. Hence, this paper outlines the development of "Chittron", an automatic image captioning system in Bangla. Moreover, to address the data set availability issue, a collection of 16,000 Bangladeshi context…

Bangla Image Caption Generation through CNN-Transformer based Encoder-Decoder Network

Automatic Image Captioning is the never-ending effort of creating syntactically and validating the accuracy of textual descriptions of an image in natural language with context. The encoder-decoder structure used throughout existing Bengali Image Captioning (BIC) research utilized abstract image feature vectors as the encoder's input. We propose a novel transformer-based architecture with an attention mechanism with a pre-trained ResNet-101 model image encoder for feature extraction from images. Experiments demonstrate that the language decoder in our technique captures fine-grained informatio…

多种方言，多种语言，一种文化视角：评估多语言视觉语言模型对孟加拉文化的理解，涵盖历史关联语言和地区方言

Many Dialects, Many Languages, One Cultural Lens: Evaluating Multilingual VLMs for Bengali Culture Understanding Across Historically Linked Languages and Regional Dialects

提出 BanglaVerse 基准，通过手工标注图像和扩展至多种语言及方言，评估多语言视觉语言模型在孟加拉文化理解中的表现，发现标准孟加拉语评估高估模型能力，方言变化导致性能下降，文化知识缺失是主要瓶颈。

Improved Bengali Image Captioning via deep convolutional neural network based encoder-decoder model

Image Captioning is an arduous task of producing syntactically and semantically correct textual descriptions of an image in natural language with context related to the image. Existing notable pieces of research in Bengali Image Captioning (BIC) are based on encoder-decoder architecture. This paper presents an end-to-end image captioning system utilizing a multimodal architecture by combining a one-dimensional convolutional neural network (CNN) to encode sequence information with a pre-trained ResNet-50 model image encoder for extracting region-based visual features. We investigate our approac…