Generating object proposals using deep-learning models
In one embodiment, a plurality of patches of an image are processed using a first-pass of a first deep-learning model to generate object-level information for each of the patches. Each patch includes one or more pixels of the image. Using a second-pass of the first deep-learning model, a respective object proposal is generated for each of the plurality of patches of the image. The second-pass takes as input the first-pass output, and the generated respective object proposals comprise pixel-level information for each of the patches. Using a second deep-learning model, a respective score is computed for each object proposal. The second deep-learning model takes as input the first-pass output, and the object score includes a likelihood that the respective patch of the object proposal contains an entire object.
1. A method comprising, by one or more computing devices:
accessing an image, wherein the image comprises a plurality of patches, wherein each patch comprises one or more pixels;
generating, using a first deep-learning model, a first-pass output comprising object-level information for each patch of the image in a first-pass, wherein the first-pass of the first deep-learning model takes as input the plurality of patches of the image;
generating, using the first deep-learning model, a respective object proposal comprising pixel-level information for each patch of the image in a second-pass, wherein the second-pass of the first deep-learning model takes as input the first-pass output; and
computing, using a second deep-learning model, a respective score for each object proposal generated using the first deep-learning model, wherein the second deep-learning model takes as input the first-pass output, and wherein the score comprises a likelihood that the patch of the respective object proposal contains an entire object.
2. The method of claim 1 , wherein the first-pass comprises a number N of layers.
3. The method of claim 2 , wherein:
a first layer of the first-pass takes as input the plurality of patches; and
each remaining layer of the N layers of the first-pass takes as input a respective output of a previous layer of the N layers of the first-pass.
4. The method of claim 3 , wherein the second-pass comprises the number N of layers.
5. The method of claim 4 , wherein:
a first layer of the second-pass takes as input the first-pass output; and
each remaining layer of the N layers of the second-pass takes as input a respective output of a previous layer of the N layers of the second-pass and output from a corresponding layer of the N layers of the first-pass.
6. The method of claim 1 , wherein the respective object proposal of each patch comprises a prediction of a location of an object in the respective patch.
7. The method of claim 1 , wherein the respective score of each object proposal comprises a likelihood that the respective patch of each object proposal contains an entire object.
8. The method of claim 7 , wherein the respective score of each object proposal further comprises a likelihood that the entire object is centered in the respective patch.
9. The method of claim 1 , wherein the image is associated with a privacy setting indicating a permission for generation of object proposals of the image.
10. The method of claim 1 , wherein a respective object proposal of a patch is represented as a shape overlaying an object in the image.
11. The method of claim 1 , wherein:
the first-pass comprises forward-pass layers; and
the second-pass comprises backward-pass layers.
12. The method of claim 1 , further comprising:
generating a sliding window having a fixed size; and
shifting the sliding window over the image to generate the plurality of patches.
13. The method of claim 12 , wherein the generated plurality of patches comprises a first set of overlapping patches.
14. The method of claim 13 , further comprising:
resizing the image; and
shifting the sliding window over the resized image to create a new set of overlapping patches.
15. The method of claim 14 , further comprising:
iteratively resizing the image and shifting the sliding window over each resized image until at least one patch contains an entire object.
16. The method of claim 1 , further comprising:
generating an identification of an object in the image based on at least one respective object proposal of a patch of the plurality of patches.
17. The method of claim 1 , further comprising:
ranking the plurality of object proposals based on their respective scores; and
determining a subset of object proposals of the plurality of object proposals based on the ranking.
18. One or more computer-readable non-transitory storage media embodying software that is operable when executed to:
access an image, wherein the image comprises a plurality of patches, wherein each patch comprises one or more pixels;
generate, using a first deep-learning model, a first-pass output comprising object-level information for each patch of the image in a first-pass, wherein the first-pass of the first deep-learning model takes as input the plurality of patches of the image;
generate, using the first deep-learning model, a respective object proposal comprising pixel-level information for each patch of the image in a second-pass, wherein the second-pass of the first deep-learning model takes as input the first-pass output; and
compute, using a second deep-learning model, a respective score for each object proposal generated using the first deep-learning model, wherein the second deep-learning model takes as input the first-pass output, and wherein the score comprises a likelihood that the patch of the respective object proposal contains an entire object.
19. 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:
access an image, wherein the image comprises a plurality of patches, wherein each patch comprises one or more pixels;
generate, using a first deep-learning model, a first-pass output comprising object-level information for each patch of the image in a first-pass, wherein the first-pass of the first deep-learning model takes as input the plurality of patches of the image;
generate, using the first deep-learning model, a respective object proposal comprising pixel-level information for each patch of the image in a second-pass, wherein the second-pass of the first deep-learning model takes as input the first-pass output; and
compute, using a second deep-learning model, a respective score for each object proposal generated using the first deep-learning model, wherein the second deep-learning model takes as input the first-pass output, and wherein the score comprises a likelihood that the patch of the respective object proposal contains an entire object.