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A Pytorch implementation of "describing videos by exploiting temporal structure", ICCV 2015

License: MIT License

Python 100.00%

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changzhou94 q-rong ammieqi xiaoyongbest1234 martin-top aihill nasib-ullah

sa-lstm's Issues

some quetsions

About the batch dim and get_last_hidden

Hi hobincar, I see that there's some difference in nn.Linear and nn.LSTM when it comes to the batch dim. nn.Linear is batch_first while nn.LSTM is batch_middle. How did you manage that?
The get_last_hidden function in Decoder class is quite confusing for me. Can you demonstrate those complicated tensor operations for me? What does this function do? Many thanks.

About 3D-ResNext-101 feature of the MSR-VTT dataset.

Hi,

Why didn't you use the 3D-ResNext-101 feature of the MSR-VTT dataset to train the model?

can you provide features for extracting MSVD dataset with R(2+1)D and 3D-ResNet?

Hi, thank you very much for releasing your code.
Can you provide features for extracting MSVD dataset withR(2+1)D and 3D-ResNet?

Can you talk about how to adjust these hyperparameters?

Hi,
I would like to ask you, how can I better adjust these hyperparameters? What's the trick?Or do we have to try them one by one?

Looking forward to your reply, thank you.

Can you help me analyze the problems I have encountered?

Hi,

Thank you again for releasing your code, your work is excellent. I think your research will get better and better.

I made some changes to your code as I thought. But the training results were very bad. I would like to consult you, what may be the reason for this ?

In Epoch 1, I got results similar to yours. However, as the training progresses, the generated results become worse and worse, and the corresponding results are almost zero.

Looking forward to your reply and heartfelt thanks.

Partial results are as follows:

[Epoch #1] loss: 4.623 = (CE: 4.623) + (Ent: 0.0 * 3099.393): 100%|██████████| 244/244 [03:40<00:00,  1.11it/s]
loss: 5.72320610773 (CE 5.72320610773 + E 2923.30143263)
{'reflen': 581, 'guess': [574, 474, 374, 274], 'testlen': 574, 'correct': [425, 162, 78, 9]}
ratio: 0.987951807227
loss: 4.93912514773 (CE 4.93912514773 + E 2970.68725586)
scores: {'CIDEr': 0.12514787945892875, 'Bleu_4': 0.20157463878737503, 'Bleu_3': 0.3705521147726949, 'Bleu_2': 0.4969475811933564, 'Bleu_1': 0.731443463944282, 'ROUGE_L': 0.5897754218096293, 'METEOR': 0.22409070167637316}
Saving checkpoint at epoch=1 to checkpoints/SA-LSTM | MSVD | FEAT MSVD_InceptionV4 mfl-60 fsl-28 mcl-30 | EMB 468 | DEC uni-LSTM-l1-h512 at-256 | DEC uni-LSTM-l1-h512 at-256 | OPTIM AMSGrad lr-5e-05-dc-20-0.5-5-wd-1e-05 rg-0.0 | 191125-10:14:35/1.ckpt

[Epoch #4] loss: 3.143 = (CE: 3.143) + (Ent: 0.0 * 4099.900): 100%|██████████| 244/244 [03:39<00:00,  1.11it/s]
loss: 3.27911160422 (CE 3.27911160422 + E 4025.05741507)
{'reflen': 1117, 'guess': [1197, 1097, 997, 897], 'testlen': 1197, 'correct': [598, 191, 61, 15]}
ratio: 1.07162041182
loss: 5.4078495936 (CE 5.4078495936 + E 2899.17534846)
scores: {'CIDEr': 0.1675833601295786, 'Bleu_4': 0.09712741190089311, 'Bleu_3': 0.1745913876077926, 'Bleu_2': 0.2949285982058635, 'Bleu_1': 0.4995822890555559, 'ROUGE_L': 0.47106992298136474, 'METEOR': 0.1974787218445869}
Saving checkpoint at epoch=4 to checkpoints/SA-LSTM | MSVD | FEAT MSVD_InceptionV4 mfl-60 fsl-28 mcl-30 | EMB 468 | DEC uni-LSTM-l1-h512 at-256 | DEC uni-LSTM-l1-h512 at-256 | OPTIM AMSGrad lr-5e-05-dc-20-0.5-5-wd-1e-05 rg-0.0 | 191125-10:14:35/4.ckpt

[Epoch #8] loss: 2.302 = (CE: 2.302) + (Ent: 0.0 * 4122.210): 100%|██████████| 244/244 [03:39<00:00,  1.11it/s]
loss: 2.32029580581 (CE 2.32029580581 + E 3954.91530021)
{'reflen': 1399, 'guess': [1804, 1704, 1604, 1504], 'testlen': 1804, 'correct': [695, 216, 57, 11]}
ratio: 1.28949249464
loss: 6.12755023349 (CE 6.12755023349 + E 2833.49368564)
scores: {'CIDEr': 0.06094166329578324, 'Bleu_4': 0.059687988482948866, 'Bleu_3': 0.12017135956381349, 'Bleu_2': 0.2209867404159963, 'Bleu_1': 0.38525498891331195, 'ROUGE_L': 0.3835879206901543, 'METEOR': 0.1720359332410031}
Saving checkpoint at epoch=8 to checkpoints/SA-LSTM | MSVD | FEAT MSVD_InceptionV4 mfl-60 fsl-28 mcl-30 | EMB 468 | DEC uni-LSTM-l1-h512 at-256 | DEC uni-LSTM-l1-h512 at-256 | OPTIM AMSGrad lr-5e-05-dc-20-0.5-5-wd-1e-05 rg-0.0 | 191125-10:14:35/8.ckpt

[Epoch #10] loss: 2.018 = (CE: 2.018) + (Ent: 0.0 * 3962.546): 100%|██████████| 244/244 [03:39<00:00,  1.11it/s]
loss: 2.03280989068 (CE 2.03280989068 + E 3847.87243252)
{'reflen': 1527, 'guess': [1883, 1783, 1683, 1583], 'testlen': 1883, 'correct': [739, 215, 59, 11]}
ratio: 1.23313686968
loss: 6.26145935059 (CE 6.26145935059 + E 2839.57718173)
scores: {'CIDEr': 0.035406386106884076, 'Bleu_4': 0.05826935694302678, 'Bleu_3': 0.1183812833024839, 'Bleu_2': 0.21754074803295365, 'Bleu_1': 0.39245884227276023, 'ROUGE_L': 0.3603913688475546, 'METEOR': 0.17098638202970123}
Saving checkpoint at epoch=10 to checkpoints/SA-LSTM | MSVD | FEAT MSVD_InceptionV4 mfl-60 fsl-28 mcl-30 | EMB 468 | DEC uni-LSTM-l1-h512 at-256 | DEC uni-LSTM-l1-h512 at-256 | OPTIM AMSGrad lr-5e-05-dc-20-0.5-5-wd-1e-05 rg-0.0 | 191125-10:14:35/10.ckpt

About the results of your model.

Hi,
Why is your SA-LSTM(Inception-v4) model so different from SA-LSTM[3](Inception-v4) model in terms of evaluation indicators?

Such as:

SA-LSTM[3](Inception-v4) BLEU4(45.3) CIDEr(76.2) METEOR(31.9) ROUGE_L(64.2)
SA-LSTM[Yours](Inception-v4) BLEU4(50.2) CIDEr(79.0) METEOR(33.3) ROUGE_L(69.7)

about reforcement learning

Hi, your reproduced work and coding ability are both very good. So, would you consider using reforcement learning as objective function instead of cross entropy like many image captioning methods? I think this would further enhance the performance.

about generated sentences

Thank you for your awesome code! When I run the train.py, I can only obtain the scores of four metrics. Thus, I have the question that how can the model generate sentences and store them?

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