# Siamese-Driven Optimization for Low-Resolution Image Latent Embedding in Image Captioning

arXiv: https://arxiv.org/abs/2512.08873 · HTML: https://mlsanity.com/paper/2512.08873/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 9 Dec 2025.

## Builds on (most cited first, 8 of 8)

- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://mlsanity.com/paper/1412.2306/) (2014)
- [Monkey: Image Resolution and Text Label Are Important Things for Large Multi-modal Models](https://mlsanity.com/paper/2311.06607/) (2023)
- [From Show to Tell: A Survey on Deep Learning-based Image Captioning](https://mlsanity.com/paper/2107.06912/) (2021)
- [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)
- [Low-Cost Implementation of Bilinear and Bicubic Image Interpolation for Real-Time Image Super-Resolution](https://mlsanity.com/paper/2009.09622/) (2020)
- [Quality-agnostic Image Captioning to Safely Assist People with Vision Impairment](https://mlsanity.com/paper/2304.14623/) (2023)

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

- [SiMiC: Context-Aware Silicon Microstructure Characterization Using Attention-Based Convolutional Neural Networks for Field-Emission Tip Analysis](https://mlsanity.com/paper/2601.17048/) (2026)

## Abstract (arXiv)

Image captioning is essential in many fields including assisting visually impaired individuals, improving content management systems, and enhancing human-computer interaction. However, a recent challenge in this domain is dealing with low-resolution image (LRI). While performance can be improved by using larger models like transformers for encoding, these models are typically heavyweight, demanding significant computational resources and memory, leading to challenges in retraining. To address this, the proposed SOLI (Siamese-Driven Optimization for Low-Resolution Image Latent Embedding in Image Captioning) approach presents a solution specifically designed for lightweight, low-resolution images captioning. It employs a Siamese network architecture to optimize latent embeddings, enhancing the efficiency and accuracy of the image-to-text translation process. By focusing on a dual-pathway neural network structure, SOLI minimizes computational overhead without sacrificing performance, making it an ideal choice for training on resource-constrained scenarios.