zpbao / discovery_obj_move Goto Github PK

Hello Zhipeng,

I would like to split the TRI-PD dataset in the same way as you but cannot find detailed information.

In the paper you mention that 924 videos are used for training and 51 videos for evaluation. From the code (datasetPD.py) it however seems that some scenes are ignored, a single scene is used for evaluation and the rest for training.

Am I missing something? Could you please clarify which videos have been used for training and evaluation?

Thank you,
Matthias

Hi,

I was playing around with the code a little and noticed that the processing of the segmentation mask in the dataloader is fairly slow. (Line 73 - 82 in dataset.py). I quickly threw together a version of this which is much faster. I propose to replace

count = 0
for i in range(mask.shape[0]):
    for j in range(mask.shape[1]):
        if mask[i,j] not in mapping:
            if mask[mask == mask[i,j]].sum()>50:
                mapping[mask[i,j]] = count
                count += 1
            else:
                mapping[mask[i,j]] = 0
        mask[i,j] = mapping[mask[i,j]]

with

values, indices, counts = np.unique(mask, return_inverse=True, return_counts=True)
to_eliminate = counts <= 50
mapping = np.arange(len(values))
mapping[to_eliminate] = 0
_h, _w = mask.shape
mask = mapping[indices].reshape((_h, _w))

On my machine, this sections gets speed up from ~0.3s to 0.01s.

Best,
Felix

When are you planning to release the evaluation code? Hopefully some weeks before CVPR deadline 🤞

Thank you for sharing code of your nice work!
I am trying to train with the TRI-PD dataset, but I cannot find "additional annotations" from the link on the readme.
I understand the annotations are not necessary for the training itself, but can you please check if the annotations are available?
Thank you!

Hello! I am very interested in this job, which is the first to be able to make the slot attention work on video. In our attempt to implement DOM, we face some problems. In the paper, the authors add all the MSE and cross-entropy loss together. However, it seems such a strategy will make the training unstable. Could you please tell us how you calculate the loss? Did you take the average among the time or add them together along the batch or time dimension? And should we take the average on the spatial dimension when calculating MSE and cross-entropy loss？ What's more, it seems the paper did not specify what matrix norm in the slot consistency. I take the sum of absolute number, is it okay？

Thanks for your great work, and I want to when you will release your pretrained model. Thanks in advance!

Dear Zhipeng,

Thank you for your outstanding work and this open-source code. I noticed that only the model and configuration files for TRI-PD are provided in your open-source code. Could you provide the model parameters for the CATER dataset to help us understand the setup better? Thanks in advance!

Best,
Lei

Dear Zhipeng,

Thank you for making the TRI-PD dataset available! I have however problems with using the data: archives 14-19 are truncated (tar: Unexpected EOF in archive). I tried downloading the files again but got the same error.

Can you double check whether the data was uploaded correctly? Maybe you could also publish checksums of your local files so that the download can be verified.

Thank you,
Matthias

In the paper, you used the segmentation masks to supervise, so why it is called unsupervised method??

In addition, are the motion groundtruth in the KITTI dataset manually annotated?

Hi Zhipeng,

thanks again for providing the data and the code!
I just wondered whether you could give me any info on how much GPU RAM you needed for training and how long it takes to train the model from scratch.

Many thanks in advance :D.
Best,
Felix