Image Segmentation via Sequential Token Processing
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Solution Overview
Problem
Existing image segmentation systems rely on manual user interaction or process entire natural language phrases at once, leading to inaccurate segmentation due to neglecting the order of words and difficulty in differentiating between similar objects.
Innovation Solution
The system iteratively segments images based on the ordered sequence of words in natural language phrases using a combination of convolutional neural networks and recurrent neural networks, allowing for accurate identification and isolation of specific image regions by processing each token sequentially.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual bounding box drawing is used for image segmentation, then user control and precision are improved, but user interaction time and operational complexity increase
Solution Approach 1:
The system enables self-service segmentation by automatically processing the image based on the natural language phrase without requiring manual bounding box drawing. The convolutional neural network extracts visual features and the recurrent neural network integrates them with phrase semantics to generate segmentation masks autonomously, eliminating the need for manual user interaction while maintaining segmentation precision.
Solution Approach 2:
The patent replaces the mechanical manual interaction system (drawing bounding boxes) with an intelligent automated system using deep learning networks. The convolutional neural network and recurrent neural network work together to interpret natural language phrases and automatically segment images, substituting manual mechanical operations with computational intelligence.
2Device complexity
If the entire natural language phrase is processed at once, then processing simplicity is improved, but segmentation accuracy deteriorates due to inability to capture word order and spatial relationships
Solution Approach 1:
The patent segments the natural language phrase into individual words or tokens and processes them sequentially through the recurrent neural network. This segmentation of the phrase processing allows the system to capture word order and spatial relationships, improving segmentation accuracy while maintaining manageable processing complexity through the structured sequential approach.
Solution Approach 2:
The recurrent neural network dynamically processes the natural language phrase by maintaining and updating hidden states as it sequences through words. This dynamic processing allows the system to adaptively capture contextual information and spatial relationships, improving segmentation accuracy compared to static whole-phrase processing.
3Productivity
If single interaction of image and phrase latent features is used, then computational efficiency is improved, but segmentation reliability deteriorates due to insufficient feature integration
Solution Approach 1:
The recurrent neural network maintains continuous interaction between image and phrase features through its sequential processing mechanism. The hidden states carry and update feature interactions across multiple time steps, ensuring continuous refinement of the segmentation based on both visual and linguistic information, thereby improving reliability while maintaining computational efficiency.
Solution Approach 2:
The recurrent neural network implements feedback mechanisms where previous processing results influence subsequent processing steps. The hidden states provide feedback about previously processed words and their relationship to image features, allowing the system to refine segmentation progressively and improve reliability through iterative feature integration.
4Extent of automation
If existing phrase-based segmentation systems are used, then automation level is improved, but ability to differentiate similar objects deteriorates due to neglecting word order
Solution Approach 1:
The recurrent neural network dynamically adapts its processing based on the sequence of words in the natural language phrase. This dynamic sequential processing enables the automated system to capture word order information and spatial relationships, improving its ability to differentiate between similar objects while maintaining high automation levels.
Solution Approach 2:
The patent adds the temporal dimension of word sequence processing to the automated segmentation system. By processing words sequentially over time steps rather than simultaneously, the system gains the ability to capture spatial relationships and differentiate between similar objects based on their positional context in the phrase.
Data Source
AI summary
The invention is directed towards segmenting images based on natural language phrases. An image and an n-gram, including a sequence of tokens, are received. An encoding of image features and a sequence of token vectors are generated. A fully convolutional neural network identifies and encodes the image features. A word embedding model generates the token vectors. A recurrent neural network (RNN) iteratively updates a segmentation map based on combinations of the image feature encoding and the token vectors. The segmentation map identifies which pixels are included in an image region referenced by the n-gram. A segmented image is generated based on the segmentation map. The RNN may be a convolutional multimodal RNN. A separate RNN, such as a long short-term memory network, may iteratively update an encoding of semantic features based on the order of tokens. The first RNN may update the segmentation map based on the semantic feature encoding.