Generic Interpretation Approach for Transformer Models Incorporating Heterogenous Attention Structures
Transformer has significantly propelled the development of artificial intelligence, and certainly the development of agents as well. We categorize attention structures of Transformer into two types based on the source of the input information: homogenous and heterogenous attention structures. Heterogenous attention structures, with co-attention as a typical example, process information from different sources. Heterogenous attention structure is the foundation for Transformer models to achieve more complex functions and integrate more modal information. Whether for research purposes or policy requirements, the interpretation of Transformer models with heterogenous attention structures is a...
| Authors | Yongjin Cui, Xiaohui Fan, Huajun Chen |
|---|---|
| Primary category | cs.CV |
| Published | 25 May 2026 |
| Last updated at source | 1 June 2026 |
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