Bridging Detection Architectures With Foundation Models: A Unified Framework for Human–Object Interaction Detection

Human–Object Interaction Detection (HOID) has benefited greatly from advances in modern detection architectures and vision-language foundation models. In this paper, we present two progressively improved HOID frameworks—SOV-STG-VLA and Hybrid-SOV—that jointly push the frontier of accurate and efficient interaction understanding. SOV-STG-VLA reformulates HOI prediction as a subject–object–verb (SOV) decoding problem and introduces a Split Target Guided (STG) denoising strategy that accelerates convergence while enhancing structural consistency. Furthermore, a Vision–Language Advisor (VLA) integrates priors from large multimodal models to enrich verb semantics and improve HOI classification. Building upon this, Hybrid-SOV aligns HOID with the latest object-detection paradigms by incorporating an efficient hybrid encoder and a query-selection mechanism that directly constructs HOI queries from visual features, eliminating predefined embeddings and enabling more interpretable decoding. When coupled with DINO-v3 foundation features, Hybrid-SOV achieves state-of-the-art accuracy with superior inference efficiency. Extensive experiments on HICO-DET and V-COCO demonstrate that our frameworks not only advance HOID performance but also establish an effective path toward bridging detection architectures with vision-language foundation models.