# Enhancing Image Captioning with Neural Models

arXiv: https://arxiv.org/abs/2312.00435 · HTML: https://mlsanity.com/paper/2312.00435/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 15 references that are in the map.
- First posted on arXiv 1 Dec 2023.

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

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [Where to put the Image in an Image Caption Generator](https://mlsanity.com/paper/1703.09137/) (2017)

## Abstract (arXiv)

This research explores the realm of neural image captioning using deep learning models. The study investigates the performance of different neural architecture configurations, focusing on the inject architecture, and proposes a novel quality metric for evaluating caption generation. Through extensive experimentation and analysis, this work sheds light on the challenges and opportunities in image captioning, providing insights into model behavior and overfitting. The results reveal that while the merge models exhibit a larger vocabulary and higher ROUGE scores, the inject architecture generates relevant and concise image captions. The study also highlights the importance of refining training data and optimizing hyperparameters for improved model performance. This research contributes to the growing body of knowledge in neural image captioning and encourages further exploration in the field, emphasizing the democratization of artificial intelligence.