The article investigates the trade-offs between fully autoregressive and cross-attention architectures in vision-language models (VLMs). It highlights that while cross-attention architectures have more parameters and higher inference costs, they yield better performance. The study provides insights into how these models can be optimized for specific tasks, revealing a gap in previous comparisons of architecture effectiveness. Findings suggest a strategic shift towards incorporating cross-attention mechanisms to enhance VLM capabilities, which could lead to advancements in natural language processing and visual understanding.
Cross-attention architecture outperforms fully autoregressive models in vision-language tasks, providing superior performance with a higher number of trainable parameters and increased inference cost.
We demonstrate the trade-offs between fully autoregressive and cross-attention architectures by analyzing performance metrics, parameter counts, and inference costs, filling a gap in existing research.
#vision-language-models #architecture-comparison #cross-attention #performance-metrics #machine-learning
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