To reduce the quadratic computation complexity caused by the global self-attention, various methods constrain the range of attention within a local region to improve its efficiency.
To be specific, the universal features denote the general characteristic of facial emotions within a period and the unique features denote the specific characteristic at this moment.
Facial Expression Recognition Facial Expression Recognition (FER)
Transformers have shown impressive performance in various natural language processing and computer vision tasks, due to the capability of modeling long-range dependencies.
This structure leverages the advantages of MobileNet at local processing and transformer at global interaction.
This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision.
Ranked #2 on Image Classification on OmniBenchmark
Finally, we present a simple adaptation of the BoTNet design for image classification, resulting in models that achieve a strong performance of 84. 7% top-1 accuracy on the ImageNet benchmark while being up to 1. 64x faster in compute time than the popular EfficientNet models on TPU-v3 hardware.
Ranked #52 on Instance Segmentation on COCO minival
However, the routine of the current ViT model is to maintain a full-length patch sequence during inference, which is redundant and lacks hierarchical representation.
Ranked #781 on Image Classification on ImageNet
We present in this paper a new architecture, named Convolutional vision Transformer (CvT), that improves Vision Transformer (ViT) in performance and efficiency by introducing convolutions into ViT to yield the best of both designs.
Ranked #3 on Image Classification on Flowers-102 (using extra training data)
We build a family of models which surpass existing MLPs and even state-of-the-art Transformer-based models, e. g., Swin Transformer, while using fewer parameters and FLOPs.
Ranked #15 on Semantic Segmentation on DensePASS