Comments (2)
Hi,
Thanks for the positive words and feedback.
Regarding your questions:
- The training is not "end-to-end"
- We train only the captioning module (yes, on GT proposals)
- We use the predictions (
./data/bafcg_val_100_proposal_result.csv
– we contacted the authors for it) from the model proposed in Wang et. al 2018 which wasn't finetuned anyhow
Therefore, the sequence of training:
- Training the captioning module on GT proposals (it will evaluate the model on GT automatically)
- Evaluate the captioning module with the proposals from Wang et. al 2018
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Thank you - you've been very helpful!
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Related Issues (20)
- All the best for your CVPR 2020 workshop, Vladimir! :) HOT 1
- Dense Video Captioning on raw input videos HOT 23
- Requesting tensorboard log file for best model HOT 7
- videoCategoriesMetaUS.json HOT 4
- ASR HOT 1
- ASR HOT 3
- 503 service unavailable: cannot open a3s.fi links HOT 5
- ResolvePackageNotFound Issue HOT 9
- difference between val_1 and val_2 HOT 1
- The utilization rate of GPU is low HOT 4
- Alignment key for the A/V features in the .npy/.hdf5 files HOT 3
- ResolvePackageNotFound: HOT 4
- I have two Issues - 1. I want to run this without GPU (i don't have GPU in Ubuntu) HOT 3
- RuntimeError: CUDA out of memory. HOT 2
- 作者您好,请问提取的多模态特征的时候需不需要区分是训练集的特征还是测试集的特征。
- Hello, author. May I ask whether it is necessary to distinguish the features of training set or test set when extracting multi-modal features? HOT 1
- About text HOT 1
- How to extract vggish features having overlapping? HOT 2
- Code to run model on own videos HOT 1
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