Unified Generative Vision-Language Understanding

This paper introduces an innovative learning framework where linguistic representations are inherently grounded in visual perceptions, circumventing the need for predefined categorical structures. The proposed method, termed Generative Semantic Embedding Model (GSEM), employs a unified generative strategy to construct a shared semantic-visual embedding space. This embedding facilitates robust language grounding across a diverse array of real-world objects. The framework's performance is evaluated by predicting object semantics and benchmarking it against both neural and traditional baselines. Our results demonstrate that GSEM significantly outperforms existing approaches, particularly under low-resource conditions, and is highly adaptable to multilingual datasets with substantial variability. These findings highlight its scalability and generalizability for grounded language learning tasks. The key novelty of GSEM lies in its ability to operate effectively without reliance on pre-trained image models or predefined attribute categories, making it suitable for diverse and dynamic environments. By integrating deep generative techniques with semantic embedding, the model captures complex interrelations between visual features and natural language descriptions, enabling a more nuanced understanding of real-world objects. Furthermore, GSEM demonstrates robustness in scenarios with limited training data, a common challenge in low-resource settings. Extensive experiments validate its effectiveness across different languages, including Spanish and Hindi, showcasing its capability to generalize linguistic grounding in multilingual contexts. Overall, this work presents a significant advancement in grounded language acquisition, offering a scalable, flexible, and efficient solution for connecting visual percepts to linguistic semantics. By addressing key limitations in existing models, GSEM paves the way for future research and applications in areas such as robotics, human-computer interaction, and multilingual artificial intelligence systems.