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US 10,872,273 B2Granted
System and method for batch-normalized recurrent highway networks
AssigneeKodak Alaris Inc.
InventorsChi Zhang, Raymond Ptucha, Alexander Loui, Carl Salvaggio
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Abstract
Embodiments of the present disclosure relate to a recurrent framework based on Recurrent Highway Networks (RHNs) for sequence modeling using batch normalization. In certain embodiments, constraints within the RHNs are relaxed to reduce or avoid gradient vanishing or exploding by normalizing the current transition units in highway layers.
Claims9
A computer-implemented method for image object recognition and captioning, comprising:
- loading a pre-trained model into a multi-layer Recurrent Highway Network;
- receiving a digital image input on which to perform object recognition, the digital image input containing one or more objects;
- batch normalizing the digital image input by applying one or more non-linear transforms to the digital image input to generate transform outputs from the multi-layer Recurrent Highway Network;
- and
- combining the transform outputs to generate a final output from the multi-layer Recurrent Highway Network, wherein the final output comprises a caption describing the one or more objects recognized in the digital image input.
2 more elements
A computer-implemented method for image object recognition and captioning, comprising:
- receiving a digital image input on which to perform object recognition, the digital image input containing one or more objects;
- batch normalizing the digital image input by applying one or more non-linear transforms to the digital image input to generate transform outputs from a multi-layer Recurrent Highway Network, wherein batch normalizing occurs at one or more highway layers of the multi-layer Recurrent Highway Network, wherein the one or more highway layers comprise a transform gate and a carry gate, and wherein the transform gate is coupled to the carry gate;
- and
- combining the transform outputs to generate a final output, wherein the final output comprises a caption describing the one or more objects in the digital image input.
1 more element
Dependencies are inferred from the claim text.