Video Representation Learning by Recognizing Temporal Transformations. In ECCV, 2020.

Dear friend, thank you very much for your work, I really learned a lot from it. It is impressive that after training only on speed prediction and 50 epoch, it got 49.3% acc on UCF-101 with linear evaluation. Nowadays, I have been trying to reproduce this performance on Pytorch following your code. But I just got 15% acc on UCF-101 with linear evaluation. Could you please give me some advice on how to achieve the performance? I have checked a lot of times that I followed your code and I may neglect some important things. Thank you very much.

Hi, When SSLtraining In your paper, you claim to use two softmax for the pseudo-task. However, in this code, it seems you use tf.split to split the prediction（eg. 8 class for all, and 4 for skip ,the other 4 for transforms）, which lead to only one softmax. what is the difference about this, or just my misunderstanding. Looking forward to your reply.

Hi Jenni, I'm the author of VTDL. I'm preparing my manuscript and aims to compare with your work. It seems your R(2+1)D model can achieve 46.4 with 112 resolution input which seems very surprising. Could you provide train logs or pretrain weight for your experiments?

Hello Thank you friend for sharing your work and knowledge. I am sorry for asking these question but I am not familiar with tensor flow at all.

Please could you clarify the following questions:

1- During the down stream task (action recognition) training, did you sample one clip from each training video using random starting index ? If Yes, then at each epoch the total number of training videos would be equal to the size of the training split.

Or
Did you use temporal jittering during training? If Yes how many clips did you sample from each training video ? 
What is the size of one epoch then ?

2- During down stream task evaluation, you mentioned in the paper that you used all the sub sequences of each testing video in the test split to get the video level prediction. What if the testing video length is not divisible by the clip length, then there would be extra frames that are not enough to sample one clip ? What is your approach to over come this issue ?

For example: When the testing video has a 173 frames and the clip length is 16 frames then 10 non overlapping clips 
can be sampled and  13 extra frames that are not enough to sample one clip are left over.

Thanks for your help

Thanks for releasing the temporal-ssl codebase. I run the temporal-ssl training with command python train_test_C3D.py --tag='test' --num_gpus=2 and find that the training is extremely slow on a 8-TitanXp server: it takes several minutes for one iteration:

How long does it take to train temporal-ssl on your platform? Would you please share the training log?

We noticed in your paper that you got an amazing result with R(2+1)D backbone. Could you public your implementation of R(2+1)D model as well as the best checkpoint? Thanks!