Relationship Recognition Models for Long-Tail Image Analysis
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Solution Overview
Problem
Existing scene graph generation models suffer from a long tail effect in relationship class distribution, leading to biased predictions that favor head classes and neglect valuable tail classes, which affects the performance of object relationship recognition.
Innovation Solution
A group-based co-training method is employed, dividing the training data set into balanced subsets and using multiple classifiers to learn these subsets collaboratively, with knowledge accumulation and transfer between them, and incorporating hybrid attention layers to fuse visual and text features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single classifier is used for relationship recognition, then the model structure is simple, but the prediction is biased toward head classes due to long tail effect in class distribution
Solution Approach 1:
The patent divides the relationship recognition task into multiple classifiers, each responsible for specific relationship classes. The training data is segmented into balanced subsets assigned to different classifiers, preventing any single classifier from being biased by the long tail distribution of all classes combined. This segmentation resolves the contradiction by maintaining relatively simple individual classifier structures while achieving unbiased predictions through the collective ensemble.
Solution Approach 2:
The patent introduces a new dimension of classification by organizing classifiers in a hierarchical structure where the kth classifier's classification space includes the previous classifier's space plus new relationship classes. This dimensional expansion allows the system to handle the full range of relationship classes without any single classifier bearing the burden of all classes, thus maintaining simplicity while improving prediction accuracy.
2Measurement precision
If multiple classifiers are used to balance class distribution, then the prediction accuracy improves, but the model complexity increases
Solution Approach 1:
The patent segments the relationship classes into multiple balanced subsets, each handled by a dedicated classifier. This segmentation balances the computational load and data distribution across classifiers, improving prediction accuracy for tail classes while keeping each classifier's structure manageable and relatively simple.
Solution Approach 2:
The patent designs classifiers with overlapping classification spaces where the kth classifier handles both new relationship classes and previously handled classes. This multi-functionality allows classifiers to share knowledge and improve each other's performance, achieving high prediction accuracy without requiring each classifier to be overly complex or specialized.
3Reliability
If balanced subsets are created for multiple classifiers, then the long tail effect is reduced, but the data processing complexity increases
Solution Approach 1:
The patent segments the training data into balanced subsets for different classifiers, ensuring each classifier receives a manageable and balanced portion of the data. This segmentation reduces the long tail effect by preventing any single classifier from being overwhelmed by rare classes, thereby improving prediction reliability while keeping data processing complexity distributed and manageable.
Solution Approach 2:
The patent performs preliminary data processing to create balanced subsets before training begins. By pre-balancing the data distribution across subsets and assigning them to classifiers in advance, the system eliminates the need for complex real-time adjustments during training, thus improving prediction reliability without excessive processing complexity.
4Measurement precision
If hybrid attention layers are added to fuse visual and text features, then the feature representation is enhanced, but the computational complexity increases
Solution Approach 1:
The patent merges visual features and text features through hybrid attention layers that process both modalities simultaneously. This merging enhances feature representation by capturing interactions between visual and textual information, improving prediction accuracy. The attention mechanism efficiently combines features without requiring separate processing pipelines, thus enhancing representation while controlling computational complexity.
Solution Approach 2:
The hybrid attention layers act as intermediaries that fuse visual and text features before passing them to the classification network. This intermediary processing enhances feature representation by systematically integrating multi-modal information, while the attention mechanism provides an efficient computational pathway that avoids the complexity of more elaborate fusion architectures.
Data Source
AI summary
Implementations of the present specification provide a method for training a relationship recognition model and a method and an apparatus for analyzing an image by using the model. The training method includes: dividing a total relationship class set into K groups, and correspondingly setting K classifiers, where the kth classification space corresponding to any kth classifier includes classification space corresponding to a previous classifier and relationship classes in a kth group; sampling a full set of image samples for the K classifiers to obtain K sample subsets respectively, and inputting the K sample subsets to corresponding classifiers to obtain relationship class prediction results between objects in images; and determining a first loss based on classification losses of the K classifiers, and determining a second loss based on a similarity between relationship class prediction results of different classifiers in the K classifiers for a same pair of objects, so that a relationship recognition model is updated based on the first loss and the second loss.