A Computationally Efficient Multidimensional Vision Transformer
This addresses efficiency issues for practitioners deploying Vision Transformers in computer vision, though it appears incremental as it builds on existing transformer architectures.
The paper tackles the high computational and memory costs limiting the deployment of Vision Transformers by introducing a tensor-based framework using the Tensor Cosine Product, achieving a uniform 1/C parameter reduction while maintaining competitive accuracy on classification and segmentation benchmarks.
Vision Transformers have achieved state-of-the-art performance in a wide range of computer vision tasks, but their practical deployment is limited by high computational and memory costs. In this paper, we introduce a novel tensor-based framework for Vision Transformers built upon the Tensor Cosine Product (Cproduct). By exploiting multilinear structures inherent in image data and the orthogonality of cosine transforms, the proposed approach enables efficient attention mechanisms and structured feature representations. We develop the theoretical foundations of the tensor cosine product, analyze its algebraic properties, and integrate it into a new Cproduct-based Vision Transformer architecture (TCP-ViT). Numerical experiments on standard classification and segmentation benchmarks demonstrate that the proposed method achieves a uniform 1/C parameter reduction (where C is the number of channels) while maintaining competitive accuracy.