Interactive Object Segmentation Using Multi-Modal User Inputs
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Solution Overview
Problem
Conventional digital object selection systems are inefficient, inaccurate, and inflexible, often requiring excessive user interaction and struggling to identify diverse objects beyond their training classifications, especially when distinguishing between similar objects within digital visual media.
Innovation Solution
A unified multi-modal interactive deep learning model that aggregates various user inputs, including regional clicks, boundary clicks, natural language expressions, and attention masks, to accurately and efficiently select objects in digital images using a multi-modal object segmentation neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tracing is used to select objects in digital images, then user control and precision are improved, but user interaction time and complexity increase significantly
Solution Approach 1:
The system performs preliminary object detection and candidate generation automatically before presenting selection options to the user. The neural network pre-processes the image to identify potential objects and their boundaries, so when the user provides minimal input (e.g., a click or bounding box), the system has already prepared multiple candidate selections ranked by relevance, dramatically reducing the time needed to achieve precise object selection.
Solution Approach 2:
The patent introduces an intermediary neural network system that acts as a mediator between the user's simple input and the final precise object selection. Instead of requiring direct manual tracing, the user provides rough input (click, box, or minimal trace) and the neural network intermediary refines this into accurate object masks by comparing against pre-detected candidates and image features, achieving both speed and precision.
2Measurement precision
If object selection systems are trained on specific object classes, then identification accuracy for those classes is improved, but versatility across different object types deteriorates
Solution Approach 1:
The patent implements a universal object selection system using a neural network trained on diverse object classes that can handle any object type in digital images. The system uses generic object detection capabilities combined with candidate ranking based on user input relevance, rather than requiring separate specialized models for each object class. This multi-functional approach allows the same system to accurately select people, animals, vehicles, furniture, and other varied objects without retraining.
Solution Approach 2:
The system changes the approach from class-specific detection to parameter-based selection. Instead of training on fixed object classes, the neural network learns to detect objects based on visual parameters and features, then ranks candidates based on how well they match the user's input parameters (click location, bounding box, trace pattern). This parameter-driven approach enables versatility across all object types while maintaining accuracy through adaptive parameter matching.
3Ease of manufacture
If the system uses a fixed type of user input (e.g., tracing), then implementation simplicity is improved, but adaptability to different image scenarios deteriorates
Solution Approach 1:
The patent implements a dynamic input system where the neural network adapts its processing based on the type of user input received. The system can accept tracing inputs, click inputs, bounding box inputs, or other interaction types, and dynamically adjusts its candidate selection and refinement process accordingly. This dynamic adaptability allows the same simple neural network architecture to effectively handle diverse input methods and image scenarios without requiring separate specialized systems for each input type.
Data Source
AI summary
Systems and methods are disclosed for selecting target objects within digital images utilizing a multi-modal object selection neural network trained to accommodate multiple input modalities. In particular, in one or more embodiments, the disclosed systems and methods generate a trained neural network based on training digital images and training indicators corresponding to various input modalities. Moreover, one or more embodiments of the disclosed systems and methods utilize a trained neural network and iterative user inputs corresponding to different input modalities to select target objects in digital images. Specifically, the disclosed systems and methods can transform user inputs into distance maps that can be utilized in conjunction with color channels and a trained neural network to identify pixels that reflect the target object.