Weed Classification Using Explainable Multi-Resolution Slot Attention

Weed Classification Using Explainable Multi-Resolution Slot Attention slot attention In the Slot Attention, randomly initialized slots compete with each other to occupy their attention regions in terms of pix- els Eventually, each slot attains slot basketball The Slot Attention architecture works by first taking in the output of a convolutional neural network, which is a representation of the input image or dataset

slot basketball In this paper, we present the Slot Attention module, an architectural component that interfaces with perceptual representations such as the output of a

slot สมัครด้วย wallet Although Slot Attention does not improve the accuracy much, it reinforces the robustness more effectively This indicates that it can better capture target Implementation of Slot Attention from the paper 'Object-Centric Learning with Slot Attention' in Pytorch Here is a video that describes what this network