# A Deep Neural Framework for Image Caption Generation Using GRU-Based Attention Mechanism

arXiv: https://arxiv.org/abs/2203.01594 · HTML: https://mlsanity.com/paper/2203.01594/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 35 references that are in the map.
- First posted on arXiv 3 Mar 2022.

## Builds on (most cited first, 17 of 17)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [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)
- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://mlsanity.com/paper/1412.2306/) (2014)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [Self-critical Sequence Training for Image Captioning](https://mlsanity.com/paper/1612.00563/) (2016)
- [Image Captioning with Semantic Attention](https://mlsanity.com/paper/1603.03925/) (2016)
- [Exploring Visual Relationship for Image Captioning](https://mlsanity.com/paper/1809.07041/) (2018)
- [Neural Baby Talk](https://mlsanity.com/paper/1803.09845/) (2018)
- [Deep Reinforcement Learning-based Image Captioning with Embedding Reward](https://mlsanity.com/paper/1704.03899/) (2017)
- [Attention Correctness in Neural Image Captioning](https://mlsanity.com/paper/1605.09553/) (2016)
- [Attend to You: Personalized Image Captioning with Context Sequence Memory Networks](https://mlsanity.com/paper/1704.06485/) (2017)
- [Deep Learning for Video Classification and Captioning](https://mlsanity.com/paper/1609.06782/) (2016)
- [Areas of Attention for Image Captioning](https://mlsanity.com/paper/1612.01033/) (2016)
- [Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images](https://mlsanity.com/paper/1504.06692/) (2015)
- [aiTPR: Attribute Interaction-Tensor Product Representation for Image Caption](https://mlsanity.com/paper/2001.09545/) (2020)

## Built on by (most cited first, 1 of 1)

- [MsEdF: A Multi-stream Encoder-decoder Framework for Remote Sensing Image Captioning](https://mlsanity.com/paper/2502.09282/) (2025)

## Abstract (arXiv)

Image captioning is a fast-growing research field of computer vision and natural language processing that involves creating text explanations for images. This study aims to develop a system that uses a pre-trained convolutional neural network (CNN) to extract features from an image, integrates the features with an attention mechanism, and creates captions using a recurrent neural network (RNN). To encode an image into a feature vector as graphical attributes, we employed multiple pre-trained convolutional neural networks. Following that, a language model known as GRU is chosen as the decoder to construct the descriptive sentence. In order to increase performance, we merge the Bahdanau attention model with GRU to allow learning to be focused on a specific portion of the image. On the MSCOCO dataset, the experimental results achieve competitive performance against state-of-the-art approaches.