Referring Image Segmentation via Stochastic Attentive Fusion
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Solution Overview
Problem
Conventional referring image segmentation systems often produce inaccurate results that require significant post-processing, and struggle with complex referral expressions and images containing multiple similar objects, neglecting valuable multi-modal interactions.
Innovation Solution
A method employing a stochastic attentive fusion network that combines image and language feature vectors using a multimodal self-attention mechanism, with a balanced feature pyramid structure to refine segmentation masks, and stochastic regularization to prevent overfitting, allowing for accurate segmentation without post-processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional referring image segmentation systems are used, then the system can process images and referral expressions, but the segmentation accuracy is poor and significant post-processing is required
Solution Approach 1:
The system performs preliminary feature extraction and fusion before segmentation, using a feature pyramid network to extract multi-scale features and a fusion module to combine image and language features in advance, which improves segmentation accuracy while reducing the need for post-processing
Solution Approach 2:
The patent introduces an attention mechanism as an intermediary between image and language features, using attention weights to dynamically fuse features from different modalities, which resolves the contradiction by enabling accurate segmentation through sophisticated feature integration without requiring complex post-processing steps
2Adaptability or versatility
If the system processes images with multiple similar objects, then it can handle complex scenarios, but the segmentation accuracy decreases due to neglecting multi-modal interactions
Solution Approach 1:
The attention mechanism serves as an intermediary that captures multi-modal interactions between image and language features, allowing the system to accurately segment objects even in complex scenarios with multiple similar objects by dynamically weighting relevant features
Solution Approach 2:
The patent extends the feature space by incorporating both visual and linguistic dimensions, using a feature pyramid network to extract multi-scale features and fusing them with language features, which provides additional dimensional information to distinguish similar objects in complex scenarios
3Speed
If the system uses simple feature combination, then the processing is fast, but it fails to capture long-range dependencies and feature semantics
Solution Approach 1:
The patent segments the feature extraction process into multiple scales using a feature pyramid network, extracting features at different resolutions and then fusing them, which captures long-range dependencies while maintaining processing efficiency through hierarchical feature representation
Solution Approach 2:
The attention mechanism acts as an intermediary that efficiently captures long-range dependencies by computing attention weights between different feature positions, allowing the system to model global relationships without the computational burden of processing all pixel pairs, thus maintaining speed while preserving information
Data Source
AI summary
A method, apparatus, and non-transitory computer readable medium for referring image segmentation are described. Embodiments of the method, apparatus, and non-transitory computer readable medium may extract an image feature vector from an input image, extract a plurality of language feature vectors for a referral expression, wherein each of the plurality of language feature vectors comprises a different number of dimensions, combine each of the language feature vectors with the image feature vector using a fusion module to produce a plurality of self-attention vectors, combine the plurality of self-attention vectors to produce a multi-modal feature vector, and decode the multi-modal feature vector to produce an image mask indicating a portion of the input image corresponding to the referral expression.