Learning to Embed Semantic Similarity for Joint Image-Text Retrieval.

Area of Science:

• Computer Vision
• Machine Learning
• Natural Language Processing

Background:

• Learning joint semantic embeddings for images and captions is crucial for multimodal understanding.
• Existing methods often struggle to accurately capture semantic similarity in a unified embedding space.

Purpose of the Study:

• To develop a deep learning approach for learning joint semantic embeddings of images and captions in Euclidean space.
• To approximate semantic similarity using L2 distances within the learned embedding space.
• To improve the accuracy and efficiency of multimodal semantic retrieval.

Main Methods:

• Utilized a metric learning scheme with multitask learning and center loss for embedding identical semantic concepts.
• Introduced a differentiable quantization scheme into an end-to-end trainable network.
• Proposed a novel metric learning formulation using an adaptive margin hinge loss refined during training.

Main Results:

• Achieved favorable comparisons with state-of-the-art approaches on MS-COCO, Flickr30K, and Flickr8K datasets.
• Demonstrated the effectiveness of the proposed differentiable quantization and adaptive margin hinge loss.
• Successfully derived semantic embeddings of semantically similar concepts in Euclidean space.

Conclusions:

• The proposed deep learning approach effectively learns joint semantic embeddings for images and captions.
• The method accurately approximates semantic similarity using L2 distances in Euclidean space.
• The approach shows significant potential for advancing multimodal information retrieval and understanding.