Generating object proposals using deep-learning models

One or more computer-readable non-transitory storage media embodying software that is operable when executed to:

• process a plurality of patches of an image, using a first deep-learning model, to detect a plurality of features associated with each patch of the image, wherein each patch comprises one or more pixels of the image;
• generate, using a second deep-learning model, a respective object proposal for each of the plurality of patches of the image, wherein the second deep-learning model takes as input the plurality of detected features associated with the respective patch of the image, and wherein each object proposal comprises a prediction as to a location of an object in the respective patch;
• and
• compute, using a third deep-learning model, a respective score for each object proposal generated using the second deep-learning model, wherein the third deep-learning model takes as input the plurality of detected features associated with the respective patch of the image, and wherein the score comprises a likelihood that the patch contains an entire object.

A system comprising:

• one or more processors;
• and a memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to:
• process a plurality of patches of an image, using a first deep-learning model, to detect a plurality of features associated with each patch of the image, wherein each patch comprises one or more pixels of the image;
• generate, using a second deep-learning model, a respective object proposal for each of the plurality of patches of the image, wherein the second deep-learning model takes as input the plurality of detected features associated with the respective patch of the image, and wherein each object proposal comprises a prediction as to a location of an object in the respective patch;
• and
• compute, using a third deep-learning model, a respective score for each object proposal generated using the second deep-learning model, wherein the third deep-learning model takes as input the plurality of detected features associated with the respective patch of the image, and wherein the score comprises a likelihood that the patch contains an entire object.