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The table prvoides the models and results of various models on CIFAR100. Learning rate =0.1 and will be divided by 10 every 70 epochs. Total 300 epochs. Using SGD optimizer, momentum=0.9, weight_decay=5e-4. Loss is CrossEntropyLoss. Batch-size=512.
Code for the paper MSAF: Multimodal Split Attention Fusion. This is our implementation of the MSAF module and the three MSAF-powered multimodal networks.
A repository for the code used to create and train the model defined in 'Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification' from AAAI 2021 (See the Paper section).
Figure 1. Illustration of our DCANet. We visualize intermediate feature activation using Grad-CAM. Vanilla SE-ResNet50 varies its focus dramatically at different stages. In contrast, our DCA enhanced SE-ResNet50 progressively and recursively adjusts focus, and closely pays attention to the…
This repository is a PyTorch implementation of our coordinate attention (will appear in CVPR2021).
The main features of andrew-qibin/coordattention are: Attention Mechanisms.
Projects with overlapping indexed features include: 13952522076/spanet — The table prvoides the models and results of various models on CIFAR100. Learning rate =0.1 and will be divided by 10… anita-hu/msaf — Code for the paper MSAF: Multimodal Split Attention Fusion. This is our implementation of the MSAF module and the… ardhendubehera/cap — A repository for the code used to create and train the model defined in 'Context-aware Attentional Pooling (CAP) for… bangguwu/ecanet — ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. blue-blue272/imgreid-ianet. 13952522076/dcanet — Figure 1. Illustration of our DCANet. We visualize intermediate feature activation using Grad-CAM. Vanilla SE-ResNet50…