Comments (4)
Thank you for your interest.
We used the PyTorch version of Grad-CAM++: link. Please refer to this repository.
We also noticed that there is a well-maintained library for various types of CAMs that you can refer to: link2
Thank you,
Kyle
from gaze-attention.
Thank you.
I was able to generate GradCAM for model_base
using [https://github.com/1Konny/gradcam_plus_plus-pytorch](this link).
For model_gaze
, register_backward_hook
doesn't fire. But register_forward_hook
does.
So, I don't have the gradients['value']
Is there any else I'm missing?
You have three submodel (model_base, model_gaze, model_attn
), when you said you visualize the last convolutional layer
of our model. Which of the three models?
Thank you
from gaze-attention.
You can visualize the output after the last conv layer (Mixed_5c) of model_base
. You do not need to use register_backward_hook
for model_gaze
because you are expected to register the hooks only for model_base
from gaze-attention.
Ok thank. you train three differents network, (I3D, I3D w/ gaze and I3D w/ gaze and attention).
You visualize the output of Mixed_5c
for each network.
from gaze-attention.
Related Issues (10)
- Data preparation HOT 1
- EGTEA Gaze+ Optical Flow dataset HOT 7
- Gaze data preparation HOT 2
- Reproducing results HOT 1
- Whats pmap? HOT 3
- What is pmaps and where do you introduce the gaze data
- EGTEA preparation HOT 4
- Reproduction trouble: how to prepare images_flow HOT 2
- a python script for generating the pmaps
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from gaze-attention.