Abstract
Visual Grounding (VG) aims to utilize given natural language queries to locate specific target objects within images. While current transformer-based approaches demonstrate strong localization performance in standard scene (i.e, scenarios without any novel objects), they exhibit notable limitations in open-vocabulary scene (i.e, both familiar and novel object categories during testing). These limitations primarily stem from three key factors: (1) imperfect alignment between visual and linguistic modalities, (2) insufficient cross-modal feature fusion, and (3) ineffective utilization of semantic prototype information. To overcome these challenges, we present Prototype-Aware Multimodal Learning (PAML), an innovative framework that systematically addresses these issues through several key components: First, we leverage ALBEF to establish robust cross-modal alignment during initial feature encoding. Subsequently, our Visual Discriminative Feature Encoder selectively enhances salient object