Single Neural Network Ensemble Integration for Object Detection
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Solution Overview
Problem
Existing object detection methods using multiple neural networks increase computational complexity, making them difficult to apply in real-time tasks and perform poorly when only one object candidate is detected.
Innovation Solution
An information processing apparatus and method that infers an object's attribute and weight in an image using a single neural network, with an integration unit performing ensemble integration based on inference maps to stabilize detection results independently of the number of detected candidates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple neural networks are used to obtain an ensemble for object detection, then detection performance is improved, but computational complexity increases making real-time processing difficult
Solution Approach 1:
The patent merges multiple object detection candidates from a single neural network into one ensemble result. The integration unit combines detection candidates using attention mechanisms to weight and fuse their outputs, achieving ensemble performance while using only one neural network, thus reducing computational complexity while maintaining improved detection accuracy
Solution Approach 2:
The patent introduces an integration unit as an intermediary component that receives multiple detection candidates from a single neural network and combines them using attention mechanisms. This intermediary process enables ensemble-like performance without requiring multiple neural networks, resolving the contradiction between detection performance and computational complexity
2Measurement precision
If multiple neural networks are used to obtain an ensemble, then object detection performance is improved, but the system becomes difficult to apply in real-time tasks
Solution Approach 1:
The patent merges multiple object detection candidates from a single neural network into one ensemble result. The integration unit combines detection candidates using attention mechanisms to weight and fuse their outputs, achieving ensemble performance while using only one neural network, thus reducing computational complexity while maintaining improved detection accuracy
Solution Approach 2:
The patent performs preliminary action by generating multiple detection candidates within a single neural network pass before the integration unit processes them. This allows the system to prepare ensemble inputs efficiently without requiring multiple separate neural network executions, thereby maintaining real-time processing speed while achieving improved detection performance
3Measurement precision
If attention-based weighting is used to obtain an ensemble from multiple candidates, then detection performance is improved, but the system performs poorly when only one candidate is detected
Solution Approach 1:
The patent applies beforehand cushioning by designing the integration unit to handle edge cases where only one detection candidate is generated. The attention mechanism is designed to gracefully degrade to single-candidate processing, and the system includes fallback logic to ensure stable detection results even when the number of candidates varies, thus preventing performance degradation in low-candidate scenarios
Solution Approach 2:
The patent applies dynamics by making the integration unit adaptive to varying numbers of detection candidates. The attention mechanism dynamically adjusts its operation based on the number of candidates received, allowing the system to maintain optimal performance whether one or multiple candidates are detected, thus improving reliability across different detection scenarios
Data Source
AI summary
An information processing apparatus comprises a first inference unit configured to infer an attribute of an object in an input image, and a weight relating to the attribute of each region in the input image, and a second inference unit configured to infer an attribute of the object, based on an attribute and a weight that are inferred by the first inference unit.