基于Siamese驱动的低分辨率图像潜在嵌入优化在图像描述中的应用
Siamese-Driven Optimization for Low-Resolution Image Latent Embedding in Image Captioning
AI总结：
SOLI通过Siamese网络优化低分辨率图像潜在嵌入，提高图像描述任务的效率和准确性，适用于资源受限场景。
摘要：
图像描述在许多领域中至关重要，包括帮助视障人士、改进内容管理系统以及增强人机交互。然而，该领域最近的挑战之一是处理低分辨率图像（LRI）。虽然通过使用较大的模型如Transformer进行编码可以提高性能，但这些模型通常较为沉重，需要大量的计算资源和内存，导致重新训练困难。为此，所提出的SOLI（基于Siamese驱动的低分辨率图像潜在嵌入优化在图像描述中的应用）方法专门针对轻量级低分辨率图像描述进行了设计。它采用Siamese网络架构来优化潜在嵌入，提高图像到文本的翻译过程的效率和准确性。通过聚焦于双路径神经网络结构，SOLI在不牺牲性能的情况下最小化计算开销，使其成为在资源受限情况下训练的理想选择。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
