Few-Shot Image and Sentence Matching via Aligned Cross-Modal Memory

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Image and sentence matching is a significant research area with many existing methods.
• Current state-of-the-art models struggle with few-shot content in images and sentences, hindering real-world applications.
• Few-shot matching is an understudied problem and a bottleneck for performance improvement.

Purpose of the Study:

• To address the challenge of few-shot image and sentence matching.
• To propose a novel model capable of handling limited information in cross-modal tasks.
• To improve the accuracy and robustness of image-sentence matching systems.

Main Methods:

• Formulated the problem as few-shot image and sentence matching.
• Proposed the Aligned Cross-Modal Memory (ACMM) model.
• ACMM employs weakly-supervised alignment for few-shot regions and words.
• ACMM stores and updates cross-modal prototypical representations without groundtruth correspondence.
• Adaptive balancing of few-shot and common content for similarity measurement.

Main Results:

• Achieved state-of-the-art results on two public benchmark datasets.
• Demonstrated effectiveness in both few-shot and conventional image and sentence matching tasks.
• The proposed ACMM model shows significant improvements over existing methods.

Conclusions:

• The Aligned Cross-Modal Memory (ACMM) model effectively addresses the few-shot image and sentence matching problem.
• The model's ability to align and reference few-shot content without groundtruth data is a key innovation.
• This work advances the field of cross-modal matching and has implications for real-world applications.