Comments (1)
I believe that we did the second thing: concatenate the one-hot representations of the objects, then put through some fully connected layers with biases (code: https://github.com/alsuhr-c/nlvr-baselines/blob/master/img_ft_rnn.py#L150). Also, note that we padded the boxes with zero-vectors for objects the box did not have (up to 8 objects). It would be a good idea to embed each item individually, which we did not report results on.
We did try a model where we embedded objects independently with the same embedding matrix, and averaged them, then averaged the embeddings of each box (I'm not sure on the details of this model). This was done to model the order independence of the objects and of the boxes. The performance for this model was roughly the same as the image features + RNN model that we reported, but I don't have exact numbers at the moment.
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Related Issues (9)
- Broken json files HOT 1
- File missing : image Split folder when using CLIP4CIR HOT 1
- The images have 4 channels HOT 4
- Typos in the dataset HOT 1
- License? HOT 1
- Vocabulary words HOT 1
- Label distributions HOT 2
- NLVR dataset uploaded & visualized at tagtog HOT 4
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