# Pre-Trained CNN Architecture for Transformer-Based Image Caption Generation Model

arXiv: https://arxiv.org/abs/2509.17365 · HTML: https://mlsanity.com/paper/2509.17365/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 22 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [A Comprehensive Survey of Deep Learning for Image Captioning](https://mlsanity.com/paper/1810.04020/) (2018)
- [STAIR Captions: Constructing a Large-Scale Japanese Image Caption Dataset](https://mlsanity.com/paper/1705.00823/) (2017)
- [Deep Learning Approaches on Image Captioning: A Review](https://mlsanity.com/paper/2201.12944/) (2022)
- [Fluency-Guided Cross-Lingual Image Captioning](https://mlsanity.com/paper/1708.04390/) (2017)

## Abstract (arXiv)

Automatic image captioning, a multifaceted task bridging computer vision and natural language processing, aims to generate descriptive textual content from visual input. While Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks have achieved significant advancements, they present limitations. The inherent sequential nature of RNNs leads to sluggish training and inference times. LSTMs further struggle with retaining information from earlier sequence elements when dealing with very long sequences. This project presents a comprehensive guide to constructing and comprehending transformer models for image captioning. Transformers employ self-attention mechanisms, capturing both short- and long-range dependencies within the data. This facilitates efficient parallelization during both training and inference phases. We leverage the well-established Transformer architecture, recognized for its effectiveness in managing sequential data, and present a meticulous methodology. Utilizing the Flickr30k dataset, we conduct data pre-processing, construct a model architecture that integrates an EfficientNetB0 CNN for feature extraction, and train the model with attention mechanisms incorporated. Our approach exemplifies the utilization of parallelization for efficient training and inference. You can find the project on GitHub.