Systems and methods for image retrieval using super features

1. An image retrieval system, comprising:

a neural network (NN) module configured to generate local features based on an input image;

an iterative attention module configured to, via T iterations, generate an ordered set of super features in the input image based on the local features,

where T is an integer greater than 1; and

a selection module configured to select a second image from a plurality of images in an image database based on the second image having a second ordered set of super features that most closely match the ordered set of super features in the input image,

wherein the super features in the set of super features do not include redundant local features of the input image.

2. The image retrieval system of claim 1 wherein the iterative attention module is configured to:

during a first one of the T iterations, generate a third ordered set of super features in the input image further based on an ordered set of predetermined initialization super features and the local features; and

generate the ordered set of super features based on the third ordered set of super features.

3. The image retrieval system of claim 2 wherein the iterative attention module is configured to:

during a second one of the T iterations that is after the first one of the T iterations, generate a fourth ordered set of super features in the input image further based on the third ordered set of super features and the local features; and

generate the ordered set of super features based on the fourth ordered set of super features.

4. The image retrieval system of claim 1 wherein the iterative attention module is configured to:

during an N-th one of the T iterations, where N is an integer less than or equal to T, generate an N-th ordered set of super features in the input image further based on (a) an N−1th ordered set of super features for an N−1th iteration of the T iterations and (b) the local features; and

generate the ordered set of super features based on the N-th ordered set of super features.

5. The image retrieval system of claim 4 wherein the iterative attention module is configured to, during the N-th one of the T iterations:

determine a first linear projection of the local features;

determine a second linear projection of the local features;

determine a third linear projection of the N−1th ordered set of super features; and

generate the ordered set of super features in the input image based on the first, second, and third linear projections.

6. The image retrieval system of claim 5 wherein the iterative attention module is configured to:

determine a fourth linear projection based on a product of the first and third linear projections;

generate attention maps for the input image based on the fourth linear projection; and

generate the ordered set of super features in the input image based on the attention maps and the second linear projection.

7. The image retrieval system of claim 6 wherein the iterative attention module is configured to generate the attention maps for the input image by:

scaling the fourth linear projection;

applying a softmax function after the scaling; and

normalizing a result of the scaling.

8. The image retrieval system of claim 6 wherein the iterative attention module is configured to:

determine first features based on the attention maps and the second linear projection;

determine second features based on the third linear projection and the first features; and

generate the ordered set of super features in the input image based on the second features.

9. The image retrieval system of claim 8 wherein the iterative attention module includes a multi layer perceptron (MLP) module configured to generate an output based on the second features,

wherein the iterative attention module is configured to generate the ordered set of super features in the input image based on (a) the second features and (b) the output of the MLP module.

10. The image retrieval system of claim 1 wherein the iterative attention module is trained by minimizing a contrastive loss.

11. The image retrieval system of claim 1 wherein the iterative attention module is trained by minimizing a cosine similarity loss.

12. The image retrieval system of claim 1 wherein:

the NN module receives the input image from a computing device via a network; and

the selection module transmits the second image to the computing device via the network.

13. The image retrieval system of claim 12 further comprising the computing device, wherein the computing device is configured to at least one of:

display the second image on a display; and

display information regarding the second image on the display; and

audibly output the information regarding the second image via a speaker.

14. A system, comprising:

the image retrieval system of claim 1 ;

a camera configured to capture the input image; and

a pose and location module configured to determine at least one of:

a present location based on the second image; and

a pose of the camera based on the second image.

15. An image retrieval method, comprising:

by a neural network (NN) module, generating local features based on an input image;

via T iterations, generating an ordered set of super features in the input image based on the local features using an attention module,

where T is an integer greater than 1; and

selecting a second image from a plurality of images in an image database based on the second image having a second ordered set of super features that most closely match the ordered set of super features in the input image,

wherein the super features in the set of super features do not include redundant local features of the input image.

16. The image retrieval method of claim 15 wherein the generating the ordered set of super features includes:

during a first one of the T iterations, generating a third ordered set of super features in the input image further based on an ordered set of predetermined initialization super features and the local features; and

generating the ordered set of super features based on the third ordered set of super features.

17. The image retrieval method of claim 16 wherein the generating the ordered set of super features includes:

during a second one of the T iterations that is after the first one of the T iterations, generating a fourth ordered set of super features in the input image further based on the third ordered set of super features and the local features; and

generating the ordered set of super features based on the fourth ordered set of super features.

18. The image retrieval method of claim 15 wherein the generating the ordered set of super features includes:

during an N-th one of the T iterations, where N is an integer less than or equal to T, generating an N-th ordered set of super features in the input image further based on (a) an N−1th ordered set of super features for an N−1th iteration of the T iterations and (b) the local features; and

generating the ordered set of super features based on the N-th ordered set of super features.

19. The image retrieval method of claim 18 wherein the generating the ordered set of super features includes, during the N-th one of the T iterations:

determining a first linear projection of the local features;

determining a second linear projection of the local features;

determining a third linear projection of the N−1th ordered set of super features; and

generating the ordered set of super features in the input image based on the first, second, and third linear projections.

20. The image retrieval method of claim 19 wherein the generating the ordered set of super features includes:

determining a fourth linear projection based on a product of the first and third linear projections;

generating attention maps for the input image based on the fourth linear projection; and

generating the ordered set of super features in the input image based on the attention maps and the second linear projection.

21. The image retrieval method of claim 20 wherein generating the attention maps includes generating the attention maps for the input image by:

scaling the fourth linear projection;

applying a softmax function after the scaling; and

normalizing a result of the scaling.

22. The image retrieval method of claim 20 wherein the generating the ordered set of super features includes:

determining first features based on the attention maps and the second linear projection;

determining second features based on the third linear projection and the first features; and

generating the ordered set of super features in the input image based on the second features.

23. The image retrieval method of claim 22 further comprising generating an output based on the second features using a multi layer perceptron (MLP) module,

wherein the generating the ordered set of super features includes generating the ordered set of super features in the input image based on (a) the second features and (b) the output of the MLP module.

24. The image retrieval method of claim 15 wherein the attention module is trained by minimizing a contrastive loss.

25. The image retrieval method of claim 15 wherein the attention module is trained by minimizing a cosine similarity loss.

26. The image retrieval method of claim 15 further comprising:

receiving the input image from a computing device via a network; and

transmitting the second image to the computing device via the network.

27. The image retrieval method of claim 26 further comprising, by the computing device, at least one of:

displaying the second image on a display; and

displaying information regarding the second image on the display; and

audibly outputting the information regarding the second image via a speaker.

28. A method, comprising:

the image retrieval method of claim 15 ;

capturing the input image using a camera; and

determining at least one of:

a present location based on the second image; and

a pose of the camera based on the second image.