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View Code? Open in Web Editor NEWDemo for "Fast User-Guided Video Object Segmentation by Interaction-and-Propagation Networks", CVPR 2019.
Demo for "Fast User-Guided Video Object Segmentation by Interaction-and-Propagation Networks", CVPR 2019.
Hi:
Thanks for sharing this code. Based on this code I'm trying to reproduce the results reported in table.2, which the model is trained only on DAVIS 17. The reported AUC=0.555 and J@60=0.589. However in my reproduction, only AUC=0.496 and J@60=0.521 achieved:
time: [0.00, 4.64, 9.63, 14.19, 18.32, 22.38, 26.16, 29.85, 33.67, 102.02]
J: [0.00, 39.86, 45.37, 48.06, 49.39, 50.31, 50.93, 51.56, 52.08, 52.08]
Since I don't know the difference between my implementation and yours, I present my training details for review:
Are these training details correct?
The volatile flag is deprecated. In the latest stable release (0.4.0) you should change the "model.py" by adding the following to lines 98-99:
with torch.no_grad():
self.Prop_forward(target, right_end)
self.Prop_backward(target, left_end)
Link to commit:
hosseinjavidnia@2bbf2d9
Dear Sir:
In "3.3. Testing Scheme",There is a saying that "we propagate the object mask until we reach a frame in which user annotations were given in any previous rounds."
I don't understand why the weight should get inverse when we reach the frame where our annotions were given before, just like the graph in Fig.4.
Are there some rofound meaning behind it?Or maybe I have a misunderstanding?
Looking forward to your reply!Thank you!
Hi, in the paper, you say that: "The two object masks are represented with probabilities and the neutral mask is used if the mask is not available."(Section 3.1 Propagation Network).
However, in the code you write:
def Prop_forward(self, target, end):
for n in range(target+1, end+1): #[1,2,...,N-1]
print('[MODEL: propagation network] >>>>>>>>> {} to {}'.format(n-1, n))
self.all_E[:,n], _, self.next_a_ref = self.model_P(self.ref, self.a_ref, self.all_F[:,:,n], self.prev_E[:,n], torch.round(self.all_E[:,n-1]), self.dummy_M, [1,0,0,0,0])
It seems that you take the probability map(self.prev_E[:,n]) obtained at the previous round and the binary mask(torch.round(self.all_E[:,n-1])) obtained at the previous frame as input to the propagation network. I tried to reproduce the results presented in the paper using your code and the pre-trained model. I would like to know what input should I use.
Hello, I'm interested in your work. And I have some questions about this network.
(ivs) administrator@ubuntu:~/ivs-demo-master$ python gui.py -seq camel
Interaction Network: initialized
Propagation Network: initialized
qt.qpa.plugin: Could not load the Qt platform plugin "xcb" in "" even though it was found.
This application failed to start because no Qt platform plugin could be initialized. Reinstalling the application may fix this problem.
Available platform plugins are: eglfs, linuxfb, minimal, minimalegl, offscreen, vnc, wayland-egl, wayland, wayland-xcomposite-egl, wayland-xcomposite-glx, webgl, xcb.
已放弃 (核心已转储)
please help me how to deal with this error.thanks.
The indexing in propagation function, Line 102 is wrong:
self.all_E[:,:,f] = weight[f] * self.all_E[:,:,f] + (1-weight[f]) * self.prev_E[:,:,f]
Has to be replaced with:
self.all_E[:, f, :, :] = weight[f] * self.all_E[:, f, :, :] + (1 - weight[f]) * self.prev_E[:, f, :, :]
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