CLIP4Cir-MoE: A Composed Image Retrieval System Integrating CLIP and Mixture-of-Experts Model

This article introduces the CLIP4Cir-MoE project developed by lanlh1012, which combines the CLIP vision-language model with the Mixture-of-Experts (MoE) mechanism to support precise composed image retrieval using reference images and text descriptions. The project is sourced from GitHub (link: https://github.com/lanlh1012/CLIP4Cir-MoE) and was released on May 24, 2026. This system represents a significant advancement in multimodal retrieval technology, retaining the intuitiveness of visual references while incorporating the precision of text descriptions.

Image retrieval technology has evolved from text label-based to content feature-based approaches. Traditional searches rely on manually annotated keywords, while modern systems use deep learning to understand image content. However, in real-world scenarios, users often need to combine reference images with text adjustments (e.g., "like this dress but in red"), which has spurred the research direction of Composed Image Retrieval (CIR).

Core Components

1. CLIP Model: An OpenAI pre-trained vision-language model that encodes images and text into a unified semantic space, with zero-shot classification and cross-modal alignment capabilities.
2. MoE-Enhanced Combiner Network: Integrates the Mixture-of-Experts mechanism, dynamically fusing visual, text features, and interaction patterns through multiple specialized sub-networks (experts) and gating.

Workflow

The input end receives a reference image (visual context) and modified text (semantic instruction) → CLIP extracts image/text features → MoE Combiner generates target image embeddings → Retrieve and output similar images from the database.

Technical Advantages

• CLIP's pre-trained knowledge reduces reliance on large-scale paired data;
• The MoE mechanism adaptively handles different composed queries, avoiding the limitations of a single fusion strategy;
• The architecture is scalable, supporting the addition of more experts or structural adjustments to adapt to specific domains.

Application Scenarios

• E-commerce: Precise product search using reference images + modified descriptions;
• Creative design: Rapid exploration of visual concept variants;
• Content management systems: Flexible multimodal content retrieval.

Related Research

• CLIP demonstrated the effectiveness of large-scale contrastive learning in vision-language tasks;
• Early CIR works like TIRG and Composed CNN explored feature fusion strategies;
• MoE expands model capacity in Transformers (e.g., Switch Transformer, GLaM).

Implementation Details

The project's code repository has a clear structure and complete README documentation; it is based on mainstream frameworks like PyTorch, with clear explanations of core concepts, making it easy to understand and reproduce.

Limitations

• Whether the CLIP feature space sufficiently captures fine-grained visual attribute changes;
• The robustness of the MoE gating mechanism when dealing with complex compositions;
• Computational efficiency, large-scale index construction, and real-time retrieval performance need optimization.

Future Directions

• Introduce more advanced visual encoders;
• Explore sparse MoE variants to improve efficiency;
• Extend to other modalities like video.

CLIP4Cir-MoE represents an important exploration in composed image retrieval technology, integrating CLIP's cross-modal capabilities with the flexible fusion mechanism of MoE. With the development of multimodal AI, this system is expected to play an important role in fields such as search engines, recommendation systems, and creative design tools, providing valuable reference implementations for researchers and developers.