  A vision transformer (ViT) is the dominant model in the computer vision
field. Despite numerous studies that mainly focus on dealing with inductive
bias and complexity, there remains the problem of finding better transformer
networks. For example, conventional transformer-based models usually use a
projection layer for each query (Q), key (K), and value (V) embedding before
multi-head self-attention. Insufficient consideration of semantic $Q, K$, and
$V$ embedding may lead to a performance drop. In this paper, we propose three
types of structures for $Q$, $K$, and $V$ embedding. The first structure
utilizes two layers with ReLU, which is a non-linear embedding for $Q, K$, and
$V$. The second involves sharing one of the non-linear layers to share
knowledge among $Q, K$, and $V$. The third proposed structure shares all
non-linear layers with code parameters. The codes are trainable, and the values
determine the embedding process to be performed among $Q$, $K$, and $V$. Hence,
we demonstrate the superior image classification performance of the proposed
approaches in experiments compared to several state-of-the-art approaches. The
proposed method achieved $71.4\%$ with a few parameters (of $3.1M$) on the
ImageNet-1k dataset compared to that required by the original transformer model
of XCiT-N12 ($69.9\%$). Additionally, the method achieved $93.3\%$ with only
$2.9M$ parameters in transfer learning on average for the CIFAR-10, CIFAR-100,
Stanford Cars datasets, and STL-10 datasets, which is better than the accuracy
of $92.2\%$ obtained via the original XCiT-N12 model.
