# Compressed Image Captioning using CNN-based Encoder-Decoder Framework

arXiv: https://arxiv.org/abs/2404.18062 · HTML: https://mlsanity.com/paper/2404.18062/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 28 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [DepGraph: Towards Any Structural Pruning](https://mlsanity.com/paper/2301.12900/) (2023)
- [Convolutional Image Captioning](https://mlsanity.com/paper/1711.09151/) (2017)
- [Deep Learning Approaches on Image Captioning: A Review](https://mlsanity.com/paper/2201.12944/) (2022)
- [Describing Multimedia Content using Attention-based Encoder--Decoder Networks](https://mlsanity.com/paper/1507.01053/) (2015)
- [Language Models for Image Captioning: The Quirks and What Works](https://mlsanity.com/paper/1505.01809/) (2015)
- [Image Captioning as an Assistive Technology: Lessons Learned from VizWiz 2020 Challenge](https://mlsanity.com/paper/2012.11696/) (2020)
- [Phrase-based Image Captioning](https://mlsanity.com/paper/1502.03671/) (2015)
- [Frequency Regularization: Restricting Information Redundancy of Convolutional Neural Networks](https://mlsanity.com/paper/2304.07973/) (2023)

## Abstract (arXiv)

In today's world, image processing plays a crucial role across various fields, from scientific research to industrial applications. But one particularly exciting application is image captioning. The potential impact of effective image captioning is vast. It can significantly boost the accuracy of search engines, making it easier to find relevant information. Moreover, it can greatly enhance accessibility for visually impaired individuals, providing them with a more immersive experience of digital content. However, despite its promise, image captioning presents several challenges. One major hurdle is extracting meaningful visual information from images and transforming it into coherent language. This requires bridging the gap between the visual and linguistic domains, a task that demands sophisticated algorithms and models. Our project is focused on addressing these challenges by developing an automatic image captioning architecture that combines the strengths of convolutional neural networks (CNNs) and encoder-decoder models. The CNN model is used to extract the visual features from images, and later, with the help of the encoder-decoder framework, captions are generated. We also did a performance comparison where we delved into the realm of pre-trained CNN models, experimenting with multiple architectures to understand their performance variations. In our quest for optimization, we also explored the integration of frequency regularization techniques to compress the "AlexNet" and "EfficientNetB0" model. We aimed to see if this compressed model could maintain its effectiveness in generating image captions while being more resource-efficient.