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.

翻译：图像描述生成在辅助视障人士、改进内容管理系统以及增强人机交互等多个领域至关重要。然而，该领域近期面临的一个挑战是处理低分辨率图像。虽然通过使用如Transformer等大型模型进行编码可以提升性能，但这些模型通常计算量庞大，需要大量计算资源和内存，导致重新训练面临困难。为解决这一问题，本文提出的SOLI方法专为轻量级低分辨率图像描述生成设计。该方法采用Siamese网络架构来优化潜在嵌入，从而提高图像到文本转换过程的效率和准确性。通过聚焦于双路径神经网络结构，SOLI在保持性能的同时最小化计算开销，使其成为资源受限场景下训练的理想选择。