  Transformers exhibit great advantages in handling computer vision tasks. They
model image classification tasks by utilizing a multi-head attention mechanism
to process a series of patches consisting of split images. However, for complex
tasks, Transformer in computer vision not only requires inheriting a bit of
dynamic attention and global context, but also needs to introduce features
concerning noise reduction, shifting, and scaling invariance of objects.
Therefore, here we take a step forward to study the structural characteristics
of Transformer and convolution and propose an architecture search method-Vision
Transformer with Convolutions Architecture Search (VTCAS). The high-performance
backbone network searched by VTCAS introduces the desirable features of
convolutional neural networks into the Transformer architecture while
maintaining the benefits of the multi-head attention mechanism. The searched
block-based backbone network can extract feature maps at different scales.
These features are compatible with a wider range of visual tasks, such as image
classification (32 M parameters, 82.0% Top-1 accuracy on ImageNet-1K) and
object detection (50.4% mAP on COCO2017). The proposed topology based on the
multi-head attention mechanism and CNN adaptively associates relational
features of pixels with multi-scale features of objects. It enhances the
robustness of the neural network for object recognition, especially in the low
illumination indoor scene.
