Beyond Self-Attention: Sub-Quadratic Vision Transformers for Fast Image Captioning
For researchers in image captioning and efficient vision transformers, this work offers a sub-quadratic attention mechanism that maintains performance while reducing computational cost.
The paper tackles the high computational cost of quadratic self-attention in Vision Transformers for image captioning by replacing it with a Gaussian Mixture Model-based clustering mechanism, reducing complexity from O(n^2) to O(nK). On the Flickr 30K dataset, the model achieves competitive performance with significant improvement over existing works.
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 self-attention mechanism in Vision Transformers is replaced with a probabilistic transformer approach based on a Gaussian Mixture Model (GMM), a soft-clustering technique. Instead of computing pairwise attention among all image patches, the model groups similar patches into a fixed number of clusters using an Expectation-Maximization (EM) algorithm. This clustering-based mechanism reduces the computational complexity from quadratic O(n^2) to linear O(nK), where K << n. The autoregressive GPT-based decoder is used for caption generation. The model is evaluated on the Flickr 30K dataset, demonstrating competitive and significant improvement over existing works.