VTCR: Multimodal Visual–Text Cross-Attention Reranking for Streaming Media and Creative Content Discovery

Streaming media platforms face the critical challenge of accurately matching user queries to multimodal content (images, videos, audio) in real-time. Traditional text-based ranking models fail to capture the rich visual and semantic signals present in media content, leading to suboptimal user experiences. We introduce VTCR (Visual-Text Cross-Attention Ranker), a parameter-efficient reranking architecture that seamlessly integrates pre-computed multimodal embeddings with transformer-based text encoders through lightweight cross-attention modules. Unlike existing approaches that concatenate features or rely on expensive multi-tower architectures, VTCR employs query-aware cross-attention to dynamically extract relevant visual and semantic features from candidate items. Trained on 3 million query-content pairs from Adobe's large-scale creative content platform, VTCR achieves NDCG@10 of 0.900 and MRR of 0.905, representing substantial improvements over text-only baselines while maintaining production-grade latency. With only 4.2M trainable parameters (2.2% of the total model), VTCR demonstrates exceptional training efficiency, converging in 3 epochs. Our architecture is platform-agnostic and can readily be applied to music, video, podcast, and visual content streaming services.