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@ruotianluo ruotianluo released this 31 Dec 19:15
· 116 commits to master since this release
  1. Add structure loss inspired by Classical Structured Prediction Losses for Sequence to Sequence Learning
  2. Add a function of sample n captions. Support methods described in https://www.dropbox.com/s/tdqr9efrjdkeicz/iccv.pdf?dl=0.
  3. More pytorchy design of dataloader. Also, the dataloader now don't repeat image features according to seq_per_img. The repeating is now moved to the model forward function.
  4. Add multi-sentence sampling evaluation metrics like mBleu, Self-CIDEr etc. (those described in https://www.dropbox.com/s/tdqr9efrjdkeicz/iccv.pdf?dl=0)
  5. Use detectron type of config to setup experiments.
  6. A better self critical objective. (Named as new_self_critical now.)
    Use config ymls that end with nsc to test the performance. A technical report will be out soon.
    Basically, it performs better than original SCST on all metrics (by a small margin), but also faster (by a little bit).