Constraint Label Learning for Object Detection
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Solution Overview
Problem
Existing object detection techniques require significant labor for label assignment and struggle with multiple movements or changes in detection targets, making them inefficient for flexible learning.
Innovation Solution
An information processing apparatus and method that uses constraint labels, which define the type of constraint a normal label should follow, allowing for machine learning with reduced label assignment burden and enabling learning of multiple action types in one-time learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object detection techniques are used, then detection accuracy can be achieved, but the burden of label assignment becomes extremely heavy
Solution Approach 1:
The system performs self-labeling by automatically generating constraint labels from raw video data without requiring manual annotation. The detection apparatus itself generates the training labels through automated processes, eliminating the need for external human labelers and significantly reducing the time and labor required for data preparation.
Solution Approach 2:
The system pre-processes video data to automatically generate constraint labels before the actual detection task. By performing preliminary automated labeling and organizing data with pre-defined constraint structures, the system prepares training data in advance, reducing the burden of manual label assignment during model training.
2Measurement precision
If traditional learning methods are used, then single-type detection can be achieved, but multiple movements of detection targets cannot be handled by one-time learning
Solution Approach 1:
The detection apparatus is designed with multi-functional capability to handle various types of detection targets and movements simultaneously. By incorporating diverse constraint labels (spatial, temporal, motion constraints) and using a unified learning model that processes multiple constraint types, the system achieves universal detection across different scenarios without requiring separate specialized models for each movement type.
Solution Approach 2:
The system changes the parameter representation by introducing constraint labels with different types (spatial, temporal, motion) instead of using单一 detection parameters. This allows the learning model to adapt to various detection scenarios by adjusting which constraints are applied, enabling one-time learning to handle multiple movement types through parameter variation rather than model retraining.
3Adaptability or versatility
If more training data is collected to improve learning flexibility, then multiple action types can be learned, but the amount of required data increases
Solution Approach 1:
The system segments training data by constraint types (spatial constraints, temporal constraints, motion constraints) rather than requiring large volumes of diverse raw data. By organizing and processing data in segmented constraint categories, the system achieves flexible learning with smaller, more structured datasets, reducing the total quantity of training data needed while maintaining adaptability.
Data Source
AI summary
There is provided an information processing apparatus to reduce the burden of label assignment and achieve learning that is more flexible, the information processing apparatus including: a learning unit configured to perform machine learning using training data to which a constraint label is assigned. The constraint label is a label in which a type of constraint that a normal label is to follow is defined. In addition, there is provided an information processing method including: performing, by a processor, machine learning using training data to which a constraint label is assigned. The constraint label is a label in which a type of constraint that a normal label is to follow is defined.