Assisted image annotation

1. A method, comprising:

accessing initial object prediction information associated with an image, wherein the initial object prediction information includes a plurality of initial predictions associated with a plurality of objects in the image, including bounding box information associated with the plurality of objects;

presenting the image and at least a portion of the initial object prediction information to be displayed;

receiving adjusted object prediction information pertaining to at least some of the plurality of objects, wherein the adjusted object prediction information is obtained from a user input made via a user interface configured for a user to make annotation adjustments to at least some of the initial object prediction information; wherein the user interface is configured to allow the user to:

select a bounding box associated with an object and directly adjust the bounding box using the user interface; or

change a type associated with the object using the user interface; and

outputting updated object prediction information, wherein the updated object prediction information is based at least in part on the adjusted object prediction information; wherein:

the image and bounding boxes associated with a subset of objects in the image are displayed;

the subset of objects have corresponding confidence levels that at least meet a prespecified confidence level threshold;

a corresponding confidence level of an object in the subset of objects indicates how confident a machine learning model is in making a prediction associated with the object, the prediction including a bounding box of the object;

the image and a hint associated with an object in the image are displayed, the hint being a visual indication of an object that is more compact than a bounding box associated with the object; and

the user interface is further configured to, in response to the hint being selected, display the bounding box associated with the object.

2. The method of claim 1 , wherein the initial object prediction information is generated by the machine learning (ML) model.

3. The method of claim 1 , wherein the initial object prediction information further includes classification information associated with the plurality of objects in the image.

4. The method of claim 1 , wherein the user interface is configured to, in response to a cursor being placed over or near a predicted bounding box or an object, provide an editing interface for the predicted bounding box.

5. The method of claim 1 , wherein the user interface is configured to, in response to a cursor being placed over an object or a bounding box that is hidden, display the bounding box associated with the object.

6. The method of claim 1 , wherein:

the user interface is configured to make bounding boxes of N objects closest to a cursor visible and hide remaining bounding boxes of other objects, wherein N is a prespecified natural number.

7. The method of claim 1 , wherein:

the image and a hint associated with an object in the image are displayed; and

the user interface is further configured to:

in response to the hint being selected, display a bounding box associated with the object and allow the user to make an adjustment to the bounding box; and

in response to receiving the user input, update prediction information associated with the bounding box.

8. The method of claim 1 , wherein:

the image and an initial hint associated with an object in the image are displayed; and

the user interface is further configured to:

in response to the initial hint being selected, display a bounding box associated with the object and allow the user to make an adjustment to the bounding box;

in response to receiving the user input, update prediction information associated with the bounding box; and

replace the bounding box with an updated hint that is distinct from the initial hint.

9. A system, comprising:

one or more processors configured to:

access initial object prediction information associated with an image, wherein the initial object prediction information includes a plurality of initial predictions associated with a plurality of objects in the image, including bounding box information associated with the plurality of objects;

present the image and at least a portion of the initial object prediction information to be displayed;

receive adjusted object prediction information pertaining to at least some of the plurality of objects, wherein the adjusted object prediction information is obtained from a user input made via a user interface configured for a user to make annotation adjustments to at least some of the initial object prediction information; wherein the user interface is configured to allow the user to:

select a bounding box associated with an object and directly adjust the bounding box using the user interface; or

change a type associated with the object using the user interface; and

output updated object prediction information, wherein the updated object prediction information is based at least in part on the adjusted object prediction information; wherein:

the image and bounding boxes associated with a subset of objects in the image are displayed;

the subset of objects have corresponding confidence levels that at least meet a prespecified confidence level threshold;

a corresponding confidence level of an object in the subset of objects indicates how confident a machine learning model is in making a prediction associated with the object, the prediction including a bounding box of the object;

the image and a hint associated with an object in the image are displayed, the hint being a visual indication of an object that is more compact than a bounding box associated with the object; and

the user interface is further configured to, in response to the hint being selected, display the bounding box associated with the object; and

one or more memories coupled to the one or more processors and configured to provide the one or more processors with instructions.

10. The system of claim 9 , wherein the initial object prediction information is generated by the machine learning (ML) model.

11. The system of claim 9 , wherein the initial object prediction information further includes classification information associated with the plurality of objects in the image.

12. The system of claim 9 , wherein the user interface is configured to, in response to a cursor being placed over or near a predicted bounding box or an object, provide an editing interface for the predicted bounding box.

13. The system of claim 9 , wherein the user interface is configured to, in response to a cursor being placed over an object or a bounding box that is hidden, display the bounding box associated with the object.

14. The system of claim 9 , wherein:

the user interface is configured to make bounding boxes of N objects closest to a cursor visible and hide remaining bounding boxes of other objects, wherein N is a prespecified natural number.

15. The system of claim 9 , wherein:

the image and a hint associated with an object in the image are displayed; and

the user interface is further configured to:

in response to the hint being selected, display a bounding box associated with the object and allow the user to make an adjustment to the bounding box; and

in response to receiving the user input, update prediction information associated with the bounding box.

16. The system of claim 9 , wherein:

the image and an initial hint associated with an object in the image are displayed; and

the user interface is further configured to:

in response to the initial hint being selected, display a bounding box associated with the object and allow the user to make an adjustment to the bounding box;

in response to receiving the user input, update prediction information associated with the bounding box; and

replace the bounding box with an updated hint that is distinct from the initial hint.

17. A computer program product for image annotation, the computer program product being embodied in a tangible non-transitory computer readable storage medium and comprising computer instructions for:

accessing initial object prediction information associated with an image, wherein the initial object prediction information includes a plurality of initial predictions associated with a plurality of objects in the image, including bounding box information associated with the plurality of objects;

presenting the image and at least a portion of the initial object prediction information to be displayed;

receiving adjusted object prediction information pertaining to at least some of the plurality of objects, wherein the adjusted object prediction information is obtained from a user input made via a user interface configured for a user to make annotation adjustments to at least some of the initial object prediction information, wherein the user interface is configured to allow the user to:

select a bounding box associated with an object and directly adjust the bounding box using the user interface; or

change a type associated with the object using the user interface; and

outputting updated object prediction information, wherein the updated object prediction information is based at least in part on the adjusted object prediction information; wherein:

the image and bounding boxes associated with a subset of objects in the image are displayed;

the subset of objects have corresponding confidence levels that at least meet a prespecified confidence level threshold;

a corresponding confidence level of an object in the subset of objects indicates how confident a machine learning model is in making a prediction associated with the object, the prediction including a bounding box of the object;

the image and a hint associated with an object in the image are displayed, the hint being a visual indication of an object that is more compact than a bounding box associated with the object; and

the user interface is further configured to, in response to the hint being selected, display the bounding box associated with the object.