Distill CLIP (DCLIP): Enhancing Image-Text Retrieval via Cross-Modal Transformer Distillation

• URL: http://arxiv.org/abs/2505.21549v4
• Date: Mon, 16 Jun 2025 01:45:22 GMT
• Title: Distill CLIP (DCLIP): Enhancing Image-Text Retrieval via Cross-Modal Transformer Distillation
• Authors: Daniel Csizmadia, Andrei Codreanu, Victor Sim, Vighnesh Prabhu, Michael Lu, Kevin Zhu, Sean O'Brien, Vasu Sharma,
• Abstract summary: Distill CLIP (DCLIP) is a fine-tuned variant of the CLIP model.<n>It enhances multimodal image-text retrieval while preserving the original model's strong zero-shot classification capabilities.
• Score: 4.063715077687089
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Distill CLIP (DCLIP), a fine-tuned variant of the CLIP model that enhances multimodal image-text retrieval while preserving the original model's strong zero-shot classification capabilities. CLIP models are typically constrained by fixed image resolutions and limited context, which can hinder their effectiveness in retrieval tasks that require fine-grained cross-modal understanding. DCLIP addresses these challenges through a meta teacher-student distillation framework, where a cross-modal transformer teacher is fine-tuned to produce enriched embeddings via bidirectional cross-attention between YOLO-extracted image regions and corresponding textual spans. These semantically and spatially aligned global representations guide the training of a lightweight student model using a hybrid loss that combines contrastive learning and cosine similarity objectives. Despite being trained on only ~67,500 samples curated from MSCOCO, Flickr30k, and Conceptual Captions-just a fraction of CLIP's original dataset-DCLIP significantly improves image-text retrieval metrics (Recall@K, MAP), while retaining approximately 94% of CLIP's zero-shot classification performance. These results demonstrate that DCLIP effectively mitigates the trade-off between task specialization and generalization, offering a resource-efficient, domain-adaptive, and detail-sensitive solution for advanced vision-language tasks. Code available at https://anonymous.4open.science/r/DCLIP-B772/README.md.

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