Image Captioning Based on Semantic Scenes.

Area of Science:

• Computer Vision
• Natural Language Processing
• Artificial Intelligence

Background:

• Image captioning generates textual descriptions for images, crucial for applications like image retrieval and autonomous driving.
• Existing region-based methods often focus on local features, neglecting overall scene understanding and leading to inaccurate captions for complex scenes.
• Current methods struggle to extract complete semantic information, resulting in biased or deficient captions.

Purpose of the Study:

• To address the limitations of existing image captioning methods.
• To propose a novel Semantic Scenes Encoder (SSE) for generating comprehensive and accurate image captions.
• To enhance the understanding of both image content and semantic relationships for improved caption generation.

Main Methods:

• The Semantic Scenes Encoder (SSE) extracts a scene graph from images and integrates it into image information encoding.
• A semantic graph is extracted from captions, preserving information via a learnable attention mechanism termed the 'dictionary'.
• The model combines encoded image information and learned semantic information for caption generation.

Main Results:

• The SSE model was evaluated on the MSCOCO dataset.
• Experimental results demonstrated a significant improvement in the overall quality of generated captions.
• The SSE achieved higher scores across multiple evaluation metrics, indicating superior performance in image captioning.

Conclusions:

• The proposed Semantic Scenes Encoder (SSE) effectively enhances image captioning by incorporating scene and semantic graph information.
• The SSE overcomes limitations of previous methods by considering global scene context and complete semantic information.
• The model shows significant advantages in generating accurate and coherent captions, particularly for complex visual scenes.