Comments (1)
"We adopt a very simple design, which passes each feature point through a fully connected layer to predict the category.
When the highest probability of the predicted result after softmax is greater than a certain value, the feature point
is considered as a helpful feature and will be reserved for subsequent fusion. Conversely, the feature point is considered less helpful for fine-grained classification." -- https://arxiv.org/pdf/2202.03822v1.pdf
To know which area is discriminative or not, the PIM module does not need the ground truth.
By the way, we update our code. Hope the new version can provide more clearer explanation. Thanks.
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Related Issues (20)
- Input size in pre-trained and paper HOT 1
- About training configuration for NAbirds HOT 1
- is heat.py correct? HOT 1
- What are your torch version and torchvision version?
- Som questions about the paper. HOT 5
- RuntimeError: mat1 and mat2 shapes cannot be multiplied (6144x1456 and 2720x85) HOT 4
- How can i infer on just image not have label
- How to solve the maximum recursion depth error?
- The url of the pre-trained model is unavailable! HOT 2
- How to predict an image without the label
- How to run HeatMap with your best pretrained NABirds model? HOT 1
- Why the output is a dict and not a tensor? HOT 1
- VIT input size transform 224 to another
- create_feature_extractor problem with VIT
- Some questions about the eval metrics of this paper. HOT 3
- Regarding use of test set for model selection
- some question about model code
- demo
- What are the parameters of eval?
- cal_train_metrics: the cases of 'drop_' and 'select_' are wronly switched.
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