Latent Space Semantic Supervision Based on Knowledge Distillation for Cross-Modal Retrieval.

Area of Science:

• Information Retrieval
• Computer Vision
• Natural Language Processing

Background:

• Fine-grained cross-modal retrieval is crucial for understanding image-text relationships.
• Existing methods struggle with accurate alignment between image and text latent spaces.
• This can lead to incorrect intra-modal relationship inference and cross-modal alignment.

Purpose of the Study:

• To propose a novel model, latent space semantic supervision with knowledge distillation (L3S-KD), for improved fine-grained cross-modal retrieval.
• To address the limitations of existing methods in capturing fine-grained correspondences within latent spaces.
• To enhance the accuracy of semantic similarity learning for image-text pairs.

Main Methods:

• Developed a latent space semantic supervision model based on knowledge distillation (L3S-KD).
• Utilized object detection to obtain fine-grained correspondences between image region features and semantic features.
• Employed knowledge distillation for image latent space fine-grained alignment and object/attribute labels for text latent space fine-grained alignment.

Main Results:

• L3S-KD learns more accurate semantic similarities for local fragments in image-text pairs compared to existing methods.
• The model demonstrates consistent outperformance over state-of-the-art methods on MS-COCO and Flickr30K datasets.
• Achieved significant improvements in fine-grained cross-modal retrieval and image-text matching tasks.

Conclusions:

• The proposed L3S-KD model effectively enhances fine-grained cross-modal retrieval by improving latent space alignment.
• Leveraging object detection and knowledge distillation provides a robust approach for learning semantic similarities.
• L3S-KD represents a significant advancement in accurate image-text matching.