Image-Text Surgery: Efficient Concept Learning in Image Captioning by Generating Pseudopairs.

Area of Science:

• Computer Science
• Artificial Intelligence
• Natural Language Processing

Background:

• Neural networks achieve promising results in image captioning but struggle to describe novel concepts not present in training data.
• Manually labeling image-sentence pairs for new concepts is labor-intensive and costly.
• Efficiently learning novel concepts is crucial for advancing image captioning capabilities.

Purpose of the Study:

• To propose a novel method, Image-Text Surgery, for synthesizing pseudo-image-sentence pairs.
• To enable image captioning models to learn novel concepts without human-labeled data.
• To improve the performance and sentence quality of image captioning systems.

Main Methods:

• Image-Text Surgery synthesizes pseudo-image-sentence pairs using a knowledge base, syntax from a seed dataset (MSCOCO), and visual information from a large-scale image base (ImageNet).
• The captioning model learns novel concepts from these synthesized pseudo-data pairs.
• Adaptive visual replacement, utilizing an attention mechanism, filters unnecessary visual features in the pseudo-data.

Main Results:

• The proposed approach significantly improves performance over state-of-the-art methods on a held-out MSCOCO subset.
• Experimental results demonstrate notable enhancements in F1 score and sentence quality.
• An ablation study and qualitative results confirm the effectiveness of the Image-Text Surgery method.

Conclusions:

• Image-Text Surgery effectively generates pseudo-image-sentence pairs to teach image captioning models novel concepts.
• The method alleviates the need for extensive human labeling, offering a more efficient learning paradigm.
• The approach demonstrates significant improvements in captioning performance, particularly for unseen concepts.