yukun su

your trained model is deal(404) could you please check it?

> Thanks for your reply! But you have 4 attention layers, which attention layer are you visualizing? Do you visualizing another attention layer? In my exp, the 1st and 3rd...

> ` import matplotlib matplotlib.use('TkAgg') import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from pylab import * > > def Visualize_Attention_Map(u, xyz, attention, axis): coordi = xyz.cpu().permute(1, 0) fig =...

We just upload it and you can refer to https://drive.google.com/file/d/1Lo1Ow0P3wt4dUfNC47m1IVpuw9Qri30o/view?usp=sharing

Sorry for the late reply. They are the entire datasets for testing. You can download them from their publicly released paper. Thanks

Thanks for paying attention to this issue. Actually, we did not take these noisy labels into consideration. And this is the main difference between co-segmentation (segment out all the co-objects)...

Thanks for your help. I am glad to see it on Huggingface Spaces.

there are two options: 1. convert your custom training data into COCO format and use the provided “data_processed.py” 2. rewrite the “data_processed.py” as you want and load your data

> "img" "label" "flow_img" 是指文件夹 我把图片 标签 光流图分别放到了这三个文件夹下面 fw_指的是forward flow 就是t->t+1 bw就是 t ->t-1 您好，这个fw_flow和bw_flow是同一个东西来的吗？比如跑flownet2或者RAFT得到一些光流，怎么把这些flow分成fw和bw呢？

> fw bw 表示forward 和backward的光流 对于第i张图 forward光流是第i-1 到i backward是i+1到i suyukun666 ***@***.***> 于2022年2月25日周五 18:30写道： > […](#) > "img" "label" "flow_img" 是指文件夹 我把图片 标签 光流图分别放到了这三个文件夹下面 fw_指的是forward flow 就是t->t+1 bw就是 t ->t-1 您好，这个fw_flow和bw_flow是同一个东西来的吗？比如跑flownet2或者RAFT得到一些光流，怎么把这些flow分成fw和bw呢？...