Asif Razzaq on LinkedIn: #tech #ai #artificialintelligence

Snap and Northeastern University Researchers Propose EfficientFormer: A Vision Transformer That Runs As Fast As MobileNet While Maintaining High Performance In natural language processing, the Transformer is a unique design that seeks to solve sequence-to-sequence tasks while also resolving long-range dependencies. Vision Transformers (ViT) have demonstrated excellent results on computer vision benchmarks in recent years. On the other hand, they are usually times slower than lightweight convolutional networks because of the large number of parameters and model architecture, such as the attention mechanism. As a result, deploying ViT for real-time applications is difficult, especially on hardware with limited resources, such as mobile devices. Snap Inc. and Northeastern University collaborated on a new study that answers this fundamental question and suggests a new ViT paradigm.