Cross-Domain Dictionary Learning for Zero-Shot Recognition
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Solution Overview
Problem
Existing zero-shot learning methods are limited by the lack of image semantic attributes in training datasets, leading to incomplete alignment between visual and semantic spaces, which restricts the accuracy of zero-shot recognition tasks.
Innovation Solution
A dictionary learning method that trains cross-domain dictionaries at both the category and image layers, using semantic attributes to establish alignment between visual and semantic spaces, enabling more precise recognition by incorporating fine-grained image information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only category layer cross domain dictionary is trained, then the model can establish alignment between visual and semantic spaces at category level, but the recognition accuracy is limited due to lack of fine-grained image information
Solution Approach 1:
The patent segments the alignment task into two distinct layers: category layer and image layer. The category layer dictionary learns coarse-grained semantic alignments, while the image layer dictionary captures fine-grained visual-semantic correlations. This segmentation allows the model to progressively refine recognition accuracy without overwhelming complexity at a single level.
Solution Approach 2:
The patent introduces an additional dimension by adding the image layer on top of the existing category layer. This creates a hierarchical structure where semantic attributes are modeled at both category level and image level, enabling the system to capture both global and local visual-semantic relationships for improved zero-shot recognition.
2Measurement precision
If image semantic attributes are not provided in training datasets, then data collection is simpler, but the alignment between visual and semantic spaces is incomplete
Solution Approach 1:
The patent introduces image-level semantic attributes as an intermediary that bridges visual features and category-level semantics. These intermediate representations capture fine-grained relationships that directly connect visual information with semantic concepts, enabling more complete alignment without requiring extensive additional training data.
Solution Approach 2:
The method performs preliminary extraction of image-level semantic attributes from available data before the main training process. By pre-processing and structuring semantic information at the image level, the model can effectively leverage existing data while achieving more complete visual-semantic alignment during training.
Data Source
AI summary
Dictionary learning method and means for zero-shot recognition can establish the alignment between visual space and semantic space at category layer and image level, so as to realize high-precision zero-shot image recognition. The dictionary learning method includes the following steps: (1) training a cross domain dictionary of a category layer based on a cross domain dictionary learning method; (2) generating semantic attributes of an image based on the cross domain dictionary of the category layer learned in step (1); (3) training a cross domain dictionary of the image layer based on the image semantic attributes generated in step (2); (4) completing a recognition task of invisible category images based on the cross domain dictionary of the image layer learned in step (3).