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The problem of the precomputed optical flow #3
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Yes. You're right. Actually, for YouTube-VOS, we don't use the precomputed optical flow and replace it with the real-time computed optical flow by RAFT. |
Thanks for your reply. It seems that i need to update the code in train.py if i want to train from scratch. |
Why not use FlowNet-CSS2 on YouTube-VOS, but choose RAFT? @hzxie |
@MaxChanger |
Can I understand it as FlowNet-CSS2 performs better than RAFT on DAVIS?But why? |
Yes, you are right. I am also confused about it. |
Well, it seems to be consistent with what I understand, and there is no theoretical explanation, only that the experimental effect is better. Very extensive experimental comparison. 👍👍 |
Do you plan to release the model train on YouTube vos dataset?@hzxie |
@cctgem |
感谢开源。有一个问题想请教。
光流如果是precompute的话,应该是frame by frame的,即frame_step == 1。
当训练到后期的时候会动态增加frame_step,即可能输入第10,20,30帧数据送网络,frame_step==10,对应的光流输入的是10->20,20->30,但是此时实际输入的光流是19->20,29->30。
不确定是否您做了对应的处理我没看到相应的代码,还是我理解错了离线做光流数据的方式。
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