Beyond Self-Attention: Sub-Quadratic Vision Transformers for Fast Image Captioning
Image captioning is a challenging and significant task that aims to generate coherent and semantically meaningful textual descriptions for given images. To accomplish this task, it requires a deep understanding of visual content along with the ability to express that understanding in natural language. Despite remarkable progress with transformer-based architectures, existing approaches often suffer from limitations, such as a lack of rich local feature representations and the high computational cost of quadratic self-attention. The proposed model focuses on improving computational efficiency by restructuring the vision transformer architecture. In designing this approach, the standard sel...
| Authors | Chiradeep Ghosh, Dakshina Ranjan Kisku |
|---|---|
| Primary category | cs.CV |
| Published | 7 June 2026 |
| Last updated at source | 7 June 2026 |
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