Comments (3)
Sorry for the late reply.
The value is a linear transformation that transforms feature before skip connection. Please refer to g
in fig 2 of NonLocal, V
in fig 2 left of Attention is all your need, or W_V
in fig 2 right of Relation Network for more details.
Btw, it's good to hear that GCNet works. Thanks for your interest.
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Sorry for the late reply.
The value is a linear transformation that transforms feature before skip connection. Please refer to
g
in fig 2 of NonLocal,V
in fig 2 left of Attention is all your need, orW_V
in fig 2 right of Relation Network for more details.Btw, it's good to hear that GCNet works. Thanks for your interest.
Thank you very much for your answer!According to my understanding,, we can think of its value as the importance of the channel, can we?By the way,I think GCNet is a kind of channel attentional mechanism,right?
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By value of transform module, are you referring to the output of Global Context Block before residue connection?
In this case, we are not explicitly using channel-wise attention. The output doesn't necessarily imply the importance.
Btw, he SENet could be one example of channel attentional mechanism.
Moreover, the main idea of GCNet is to explicitly capture the global context, as the name indicates.
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Related Issues (20)
- Did anyone use GCNet on Optical Flow features?
- How can I use gc block in resnet18? HOT 3
- Simplified NL HOT 1
- gcnet performs not good on segmentation tasks. HOT 1
- Questions about training
- AP, AR=-1 while evaluation at the end of each epoch HOT 8
- Attention maps in Different query position HOT 1
- GCNet with pretrianed model on COCO detection?
- Could it be used in 3D data? HOT 1
- possible replacements for layernorm HOT 1
- 找个GC Block这么难? HOT 3
- Mask for training
- Visualization code wanted HOT 1
- change mask-rcnn to faster-rcnn?
- Does GCNet have 1d? HOT 1
- some of the problems HOT 3
- Add location based on yolov7
- Welcome update to OpenMMLab 2.0
- Weight download problem
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