用EfficientNetB0+Transformer实现高效图像描述生成
Pre-Trained CNN Architecture for Transformer-Based Image Caption Generation Model
- 用EfficientNetB0提取图像特征，结合Transformer自注意力机制
- 在Flickr30k数据集上训练，支持并行化提升效率
- 适合想快速搭建高效图像描述模型的研究者
自动图像描述是连接计算机视觉与自然语言处理的多维度任务，旨在从视觉输入生成描述性文本。尽管卷积神经网络（CNN）和长短期记忆网络（LSTM）已取得显著进展，但存在局限：RNN固有的序列特性导致训练与推理速度慢，LSTM在处理长序列时难以保留早期信息。本项目系统介绍基于Transformer的图像描述模型构建方法。Transformer采用自注意力机制，可有效捕捉数据中的短程与长程依赖关系，支持训练与推理阶段的高效并行计算。我们采用广受认可的Transformer架构，提出一套完整流程：使用Flickr30k数据集进行预处理，构建集成EfficientNetB0作为特征提取器的模型架构，并引入注意力机制进行训练。该方法充分体现了并行化在提升训练与推理效率方面的优势。
原文摘要 · Abstract (English)
Automatic image captioning, a multifaceted task bridging computer vision and natural language processing, aims to generate descriptive textual content from visual input. While Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks have achieved significant advancements, they present limitations. The inherent sequential nature of RNNs leads to sluggish training and inference times. LSTMs further struggle with retaining information from earlier sequence elements when dealing with very long sequences. This project presents a comprehensive guide to constructing and comprehending transformer models for image captioning. Transformers employ self-attention mechanisms, capturing both short- and long-range dependencies within the data. This facilitates efficient parallelization during both training and inference phases. We leverage the well-established Transformer architecture, recognized for its effectiveness in managing sequential data, and present a meticulous methodology. Utilizing the Flickr30k dataset, we conduct data pre-processing, construct a model architecture that integrates an EfficientNetB0 CNN for feature extraction, and train the model with attention mechanisms incorporated. Our approach exemplifies the utilization of parallelization for efficient training and inference. You can find the project on GitHub.
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