Local Implicit Image Functions for Resolution-Scalable Segmentation
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Solution Overview
Problem
Conventional image segmentation systems are inefficient and inflexible in accurately detecting semantic regions within digital content, requiring large numbers of parameters and being computationally expensive, while also struggling to scale resolution of segmentation masks.
Innovation Solution
The use of a local implicit image function (LIIF) neural network for image segmentation, which generates a continuous class label probability distribution to create semantic segmentation masks at different resolutions without changing the input resolution, utilizing a low-parameter image encoder and decoder.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional semantic segmentation systems are used, then accurate detection of semantic regions can be achieved, but the systems require large numbers of parameters and are computationally expensive
Solution Approach 1:
The patent transforms the segmentation approach from discrete class predictions to continuous implicit function representations. By parameterizing the segmentation problem as a continuous field rather than discrete pixel classifications, the system achieves accurate semantic region detection with significantly fewer parameters. The implicit neural representation allows the model to generalize across different resolutions and scales without increasing parameter count.
Solution Approach 2:
The patent introduces a continuous spatial dimension to the segmentation problem by using coordinate-based implicit representations. Instead of processing images in the traditional pixel grid, the system evaluates segmentation functions at continuous coordinates, enabling resolution-independent segmentation with a fixed parameter set. This dimensional transformation from discrete pixel space to continuous coordinate space reduces model complexity while maintaining accuracy.
2Adaptability or versatility
If conventional semantic segmentation systems are used, then segmentation can be performed, but the systems are inflexible and struggle to scale resolution of segmentation masks
Solution Approach 1:
The patent implements dynamic resolution scaling by evaluating the implicit segmentation function at different coordinate scales. The system can generate segmentation masks at any resolution by simply sampling the continuous implicit representation at the desired output dimensions, without requiring architectural changes or additional training. This dynamic adaptability allows the same model to produce segmentation masks ranging from low-resolution overviews to high-resolution detailed segmentations.
Solution Approach 2:
The implicit neural representation serves multiple functions simultaneously: it performs semantic segmentation, enables resolution scaling, supports multiple output resolutions from a single model, and provides continuous spatial reasoning. This universal approach replaces multiple specialized models with a single flexible system that adapts to different segmentation requirements through coordinate sampling rather than model architecture changes.
3Productivity
If conventional semantic segmentation systems are used, then face parsing can be performed, but the systems are computationally expensive and slow
Solution Approach 1:
The patent extracts the essential segmentation information into a compact implicit representation that can be evaluated efficiently at any resolution. By separating the feature extraction stage from the segmentation evaluation stage, the system performs computationally intensive feature learning once and then generates segmentation masks through lightweight function evaluation. This extraction approach eliminates redundant computations that occur in conventional systems when generating segmentation at different scales.
Solution Approach 2:
The implicit neural representation acts as a compressed copy of the segmentation information that can be replicated and evaluated at any output resolution without requiring additional processing power. Instead of running separate heavy models for different resolutions, the system creates a single implicit representation that can be sampled indefinitely, effectively copying the segmentation knowledge into a space-efficient format that enables fast inference.
Data Source
AI summary
This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that utilize a local implicit image function neural network to perform image segmentation with a continuous class label probability distribution. For example, the disclosed systems utilize a local-implicit-image-function (LIIF) network to learn a mapping from an image to its semantic label space. In some instances, the disclosed systems utilize an image encoder to generate an image vector representation from an image. Subsequently, in one or more implementations, the disclosed systems utilize the image vector representation with a LIIF network decoder that generates a continuous probability distribution in a label space for the image to create a semantic segmentation mask for the image. Moreover, in some embodiments, the disclosed systems utilize the LIIF-based segmentation network to generate segmentation masks at different resolutions without changes in an input resolution of the segmentation network.