Efficient Discriminative Joint Encoders for Large Scale Vision-Language Reranking

Mitchell Keren Taraday; Shahaf Wagner; Chaim Baskin

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arXiv · 2510.06820

Efficient Discriminative Joint Encoders for Large Scale Vision-Language Reranking

Mitchell Keren Taraday·Shahaf Wagner·Chaim Baskin

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Abstract

Multimodal retrieval still leans on embedding-based models like CLIP for fast vector search over pre-computed image embeddings. Yet, unlike text retrieval, where joint-encoder rerankers are standard, comparable vision-language rerankers are largely absent. We find that seminal joint encoders such as BLIP are severely bottlenecked by an expensive visual feature-extraction stage, preventing practical deployment at scale. Motivated by this bottleneck, we introduce EDJE, an Efficient Discriminative Joint Encoder that precomputes vision tokens offline and compresses them via a lightweight attention-based adapter, so online inference runs only a compact joint encoder over a small set of visual tokens plus the text. EDJE preserves strong retrieval performance while drastically reducing storage and online compute, enabling high-throughput inference. Specifically, EDJE processes 50k image--text pairs/second while requiring 49kB of disk storage per image, matching prior art on Flickr (zero-shot) and COCO (fine-tuned) retrieval.

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Mitchell Keren Taraday, Shahaf Wagner, Chaim Baskin. 2025-10-08. Efficient Discriminative Joint Encoders for Large Scale Vision-Language Reranking. https://arxiv.org/abs/2510.06820

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