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About this paper
Zijie Song, Zhenzhen Hu, Yuanen Zhou, Ye Zhao, Richang Hong, Meng Wang
A new Embedded Heterogeneous Attention Transformer (EHAT) architecture enables a single model to generate precise and fluent image captions simultaneously in both English and Chinese. This method establishes local matching between visual regions and linguistic elements using specialized attention, achieving performance comparable to or exceeding advanced monolingual captioning systems on the MSCOCO dataset.
Key insights
- Heterogeneous attention mechanisms can effectively model the distinct properties of visual and diverse linguistic modalities within a single architecture, improving local matching between image regions and words across languages.
- A unified ensemble model, leveraging heterogeneous attention, can generate high-quality captions in structurally different languages (English and Chinese) with performance competitive to or better than advanced monolingual methods.
- Each component of the EHAT model (MHCA, HARN, HCA) plays a critical and distinct role in aligning feature spaces, establishing heterogeneous semantic links, and processing final language outputs, respectively.
Summary by alphaXiv.
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