Machine-learning models for distractor segmentation with reduced user interactions

A system comprising:

• one or more memory devices;
• and
• one or more processors coupled to the one or more memory devices that cause the system to perform operations comprising:
• generating, utilizing a feature extraction model, an image feature representation by encoding feature information from a digital image at a plurality of resolutions to generate a feature representation;
• generating, utilizing a click integration model, a modified feature representation by integrating an encoded spatial location for a selection input, received via a user interface of a client device, corresponding to an object portrayed in the digital image into the image feature representation;
• generating, utilizing an object detection model, a plurality of masking proposals for the object portrayed in the digital image from the modified feature representation;
• and
• generating, utilizing a machine-learning mask model, a mask for the object portrayed in the digital image from the modified feature representation, the digital image, and the plurality of masking proposals.

A non-transitory computer-readable medium storing instructions thereon that, when executed by at least one processor, cause the at least one processor to perform operations comprising:

• generating, utilizing a feature extraction model, an image feature representation by encoding feature information from a digital image at a plurality of resolutions to generate a feature vector representation;
• receiving, via a graphical user interface of a client device, a selection input identifying a position of one or more pixels corresponding to an object portrayed in the digital image;
• generating, utilizing a click integration model, a modified feature representation by integrating an encoded spatial location for the selection input into the image feature representation;
• generating, utilizing an object detection model, a plurality of masking proposals from the digital image based on the modified feature representation;
• and
• generating, utilizing a machine-learning mask model and based on the plurality of masking proposals, a mask for the object portrayed in the digital image from the modified feature representation and the digital image.