Mask-Aware Neural Classification for Accurate Image Segment Labels
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Solution Overview
Problem
Conventional digital image processing systems fail to provide accurate classifications for image segments partitioned via dense segmentation, often ignoring contextual information and using limited training data, leading to inaccurate labeling and poor performance in downstream processes.
Innovation Solution
A mask-aware classification neural network is employed to generate labels for image segments by applying pre-computed masks to image embeddings, incorporating contextual features and using a combination of strong-alignment and weak-alignment data for training, including masks, bounding boxes, and point locations to enhance model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image segmentation models are used to perform dense segmentation, then image segments can be partitioned, but accurate classifications cannot be provided for those segments
Solution Approach 1:
The patent introduces mask embeddings as an intermediary representation between the segmentation masks and the classification process. These mask embeddings capture contextual information about each segment and serve as a bridge to improve classification accuracy, allowing the system to provide reliable labels for densely segmented regions.
Solution Approach 2:
The patent transforms the segmentation problem from a 2D spatial partitioning task into a 3D feature space by incorporating mask embeddings that encode contextual information. This dimensional transformation enables the classification model to access richer features beyond pixel intensities, resolving the contradiction between segmentation capability and classification accuracy.
2Adaptability or versatility
If class-agnostic masking models are used to generate masks, then a wide range of image segments can be segmented, but accurate labels cannot be generated without additional training data
Solution Approach 1:
The patent performs preliminary extraction of mask embeddings from the segmented regions before the classification stage. By pre-computing these contextual representations and storing them for later use, the system prepares the necessary information in advance, enabling accurate labeling without requiring additional training data collection and processing steps.
Solution Approach 2:
The patent creates a copy of the essential contextual information from the original images in the form of mask embeddings. These embeddings serve as compressed representations that capture the necessary features for classification, allowing the system to generate accurate labels for diverse segments without needing to process the full original images repeatedly.
3Reliability
If contextual information is incorporated into the classification process, then labeling accuracy improves, but computational complexity increases
Solution Approach 1:
The patent extracts only the essential contextual information needed for classification in the form of mask embeddings, rather than incorporating all possible contextual data. This selective extraction approach maintains labeling reliability by capturing the most relevant features while avoiding the computational burden of processing complete contextual information.
Solution Approach 2:
The patent changes the parameter representation from raw pixel data to compressed mask embeddings. This parameter transformation reduces the dimensionality and complexity of the input data while preserving the essential contextual information needed for accurate classification, thereby improving reliability without proportionally increasing model complexity.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media that generate segment labels for image segments of a digital image that have been determined via deep segmentation. For instance, in some embodiments, the disclosed systems generate, using a segment classification neural network, an image embedding for a digital image portraying a plurality of image segments. Additionally, the disclosed systems determine, using the segment classification neural network, masked segment embeddings for the plurality of image segments of the digital image based on the image embedding and a plurality of masks corresponding to the plurality of image segments. Based on the masked segment embeddings, the disclosed systems use the segment classification neural network to determine segment labels for the plurality of image segments.