model specifications not coherent with the MLB paper
- Dominant language
- Python
- Stars
- 733
- Forks
- 176
- PR merge metrics
- No merged PRs in 30d
Description
The model configuration is not the same as described in the paper. There is a softmax layer missing at the end of the model. The paper concatenates the attention * vision features for all the glimpses and then pass it through a single linear layer. You use non-linearity both times before and after fusion.
Contributor guide
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Research direction
No file or test is named; start by locating the model implementation and comparing its configuration and forward path with the MLB paper. Done means the model includes the paper's final softmax, concatenates attention-weighted vision features across glimpses before one linear layer, and matches the paper's nonlinearity placement.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100