Interactive Object Segmentation with Single-Click Mask Refinement
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Solution Overview
Problem
Conventional segmentation systems are inflexible, inefficient, and inaccurate in segmenting objects in digital images, requiring multiple clicks, re-encoding images for each click, and often incorrectly masking or overbroadly segmenting objects.
Innovation Solution
A distractor segmentation system that integrates a single click into feature maps using a machine-learning model to generate a segmentation mask, processes these maps through an object detection model to refine proposals, and filters proposals based on the click position, enhancing the segmentation mask without repeated encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional segmentation systems require multiple positive and negative clicks to identify objects, then segmentation accuracy may improve, but system flexibility and user efficiency deteriorate
Solution Approach 1:
The system performs preliminary encoding of the entire image into feature maps before any user interaction. This pre-processing creates a rich feature representation that enables accurate segmentation with minimal user input, resolving the contradiction by preparing the system in advance to handle diverse segmentation tasks with few clicks
Solution Approach 2:
The system introduces click maps as an intermediary representation between user clicks and segmentation output. By converting clicks into click maps and integrating them with pre-computed feature maps, the system efficiently translates minimal user input into accurate segmentation masks without requiring multiple iterative clicks
2Adaptability or versatility
If conventional systems re-embed an image for each click to identify objects, then segmentation adaptability improves, but computational efficiency deteriorates
Solution Approach 1:
The system performs preliminary encoding of the image into comprehensive feature maps before user interaction. These feature maps capture rich visual information and can be reused across multiple segmentation queries, eliminating the need to re-process the image for each click while maintaining adaptability to different segmentation requests
Solution Approach 2:
The pre-computed feature maps serve multiple functions: they support various types of user inputs (single clicks, multiple clicks, scribbles), enable different segmentation strategies (direct segmentation, proposal-based segmentation), and can be reused across multiple queries. This multi-functionality maintains adaptability while improving computational efficiency
3Area of stationary object
If conventional segmentation systems segment broad portions of the image, then object detection coverage improves, but segmentation precision deteriorates
Solution Approach 1:
The system applies different processing strategies to different regions based on user input. For regions near the click location, it generates detailed segmentation masks with precise boundaries. For distant regions, it uses proposal-based approaches. This local differentiation maintains precision at the segmentation boundary while ensuring comprehensive object coverage
Solution Approach 2:
The system segments the problem into two parts: (1) generating a segmentation mask from the click and feature maps, and (2) generating masking proposals to ensure complete object coverage. By combining these two segmented approaches, the system achieves both precise boundaries and comprehensive coverage
4Reliability
If conventional systems require multiple clicks to accurately identify objects, then segmentation reliability improves, but user interaction complexity increases
Solution Approach 1:
The system performs preliminary encoding and prepares multiple segmentation strategies in advance. When a user provides a single click, the system can reliably segment the object using pre-computed feature maps without requiring additional clarification clicks, thereby maintaining reliability while simplifying interaction
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning to generate a mask for an object portrayed in a digital image. For example, in some embodiments, the disclosed systems utilize a neural network to generate an image feature representation from the digital image. The disclosed systems can receive a selection input identifying one or more pixels corresponding to the object. In addition, in some implementations, the disclosed systems generate a modified feature representation by integrating the selection input into the image feature representation. Moreover, in one or more embodiments, the disclosed systems utilize an additional neural network to generate a plurality of masking proposals for the object from the modified feature representation. Furthermore, in some embodiments, the disclosed systems utilize a further neural network to generate the mask for the object from the modified feature representation and/or the masking proposals.