Comments (1)
Hi @code10086web, good question. To be honest, I have no idea on theoretical analysis to explain the reason. But intuitively, using LSTM and Transformer can model the sequential representation, and will obtain better performance. In some cases, the results of LSTM and Transformer in Sequential type are comparable. However, the mean pooling is stable. We guess the mean pooling is a Parameter-free type, so the CLIP weights will be changed consistently with gradient propagation. But the LSTM and Transformer bring randomly initialized weights, which makes the model hard to train (e.g., we use different learning rates for these new weights). And the CLIP is learning rate-sensitive when transferring to the retrieval task.
Uncomment Line #543 to save weights.
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Related Issues (20)
- Use my own videos HOT 1
- Question about the calculation method of loss when there are multiple gpus HOT 1
- Results on MSRVTT and MSVD HOT 3
- running the project HOT 1
- Problem about reproducing the model HOT 1
- Reproduction on LSMDC DataSet HOT 6
- What do Pair, L, T stand for in the code? HOT 3
- How to use this repo to retrieve clips among videos by text?
- train dataset is shuffled regardless of seed
- AttributeError: Caught AttributeError in DataLoader worker process 0.
- Video-to-video retrieval?
- How to train and evaluate the model on the Training-7k split? HOT 1
- run simple inference HOT 1
- loss NaN when training on MSRVTT HOT 1
- About mean_pooling on text sequence HOT 2
- Evaluation batch
- Large performance drop if trained with fp32. HOT 1
- [PAD_TOKEN] is not used, but just adding 0
- train files HOT 1
- Directly pass the entire config as a argument to a function
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