From Pixels to Words: A Deep Learning Approach to Image Captioning

Image captioning, a crucial task in computer vision and natural language processing (NLP), aims to generate meaningful textual descriptions for images. Traditional models use an encoder-decoder framework, where convolutional neural networks (CNNs) extract image features, and sequence models generate captions. However, conventional CNN-based approaches often lack efficiency in feature extraction. To address this, we propose a novel image captioning model integrating EfficientNetB0 as the feature extractor with a Transformer-based encoder-decoder architecture. The Transformer-Encoder, equipped with Multi-Head Attention, refines image feature representations by capturing both global and local dependencies. The Transformer-Decoder consists of two self-attention layers: Self-Attention_1 focuses on previously generated words, ensuring linguistic coherence, while Self-Attention_2 dynamically attends to the refined image features, enabling the model to emphasize relevant visual details at each decoding step. Additionally, an adaptive attention mechanism further optimizes image feature utilization for caption generation. We evaluate our model on the Flickr 8k dataset, demonstrating superior performance. Our results highlight the effectiveness of combining EfficientNetB0 with a Transformer-based encoder-decoder model, achieving improved caption accuracy while maintaining computational efficiency.