Context-Adaptive Multi-Prompt Embedding with Large Language Models for Vision-Language Alignment

Dahun Kim, Anelia Angelova

Keywords: CLIP, contrastive learning, LLM embedding

TL;DR: We propose a method that uses multiple context-adaptive prompts with a pretrained LLM to generate diverse text embeddings for contrastive vision-language learning.

Abstract: We propose Context-Adaptive Multi-Prompt Embedding, a novel approach to enrich semantic representations in vision-language contrastive learning. Unlike standard CLIP-style models that rely on a single text embedding, our method introduces multiple structured prompts, each containing a distinct adaptive token that captures diverse semantic aspects of the input text. We leverage a pretrained LLM as the text encoder within the CLIP framework, processing all prompts jointly in a single forward pass. The resulting prompt embeddings are combined into a unified text representation, enabling semantically richer alignment with visual features. To further promote semantic diversity and representation quality, we incorporate a diversity regularization loss and a negation-aware loss, encouraging specialization across prompts and improving contrastive discrimination. Our method achieves consistent improvements on both image-text and video-text retrieval benchmarks.

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Submission Number: 328