# A Comparative Study of Pre-trained CNNs and GRU-Based Attention for Image Caption Generation

arXiv: https://arxiv.org/abs/2310.07252 · HTML: https://mlsanity.com/paper/2310.07252/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 41 references that are in the map.
- First posted on arXiv 11 Oct 2023.

## Builds on (most cited first, 19 of 19)

- [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)
- [Automated Testing of Image Captioning Systems](https://mlsanity.com/paper/2206.06550/) (2022)
- [Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images](https://mlsanity.com/paper/1504.06692/) (2015)
- [Multi-modal gated recurrent units for image description](https://mlsanity.com/paper/1904.09421/) (2019)
- [aiTPR: Attribute Interaction-Tensor Product Representation for Image Caption](https://mlsanity.com/paper/2001.09545/) (2020)

## Abstract (arXiv)

Image captioning is a challenging task involving generating a textual description for an image using computer vision and natural language processing techniques. This paper proposes a deep neural framework for image caption generation using a GRU-based attention mechanism. Our approach employs multiple pre-trained convolutional neural networks as the encoder to extract features from the image and a GRU-based language model as the decoder to generate descriptive sentences. To improve performance, we integrate the Bahdanau attention model with the GRU decoder to enable learning to focus on specific image parts. We evaluate our approach using the MSCOCO and Flickr30k datasets and show that it achieves competitive scores compared to state-of-the-art methods. Our proposed framework can bridge the gap between computer vision and natural language and can be extended to specific domains.