Disentangled Invariant Representations for One Shot Instance Recognition
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Solution Overview
Problem
Conventional object recognition systems struggle with joint pose and identity recognition, as they typically handle these tasks individually, leading to increased complexity, memory load, and overfitting, and are unable to effectively utilize a single image for object recognition, which can result in inaccuracies due to self-occlusions and front-back symmetry issues.
Innovation Solution
A deep neural network is structured to capture pose variation using a two-branch convolutional network architecture that disentangles identity and pose information, allowing for one-shot joint instance and pose recognition by generating an orbit of an object's poses from a single view, enabling the system to recognize both identity and pose from a single image without additional sensory information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional object recognition systems handle pose and identity recognition individually, then each task can be processed separately, but the system complexity, memory load, and overfitting risk increase
Solution Approach 1:
The patent combines pose recognition and identity recognition into a single unified neural network model that processes both tasks simultaneously. The network architecture integrates pose estimation modules and identity classification modules share common feature extraction layers, allowing the system to handle both recognition tasks together rather than separately, thereby reducing overall system complexity while maintaining recognition accuracy
2Measurement precision
If multiple images or additional sensory information are used for object recognition, then recognition accuracy improves, but resource usage and system complexity increase
Solution Approach 1:
The patent segments the recognition task into distinct functional modules within the neural network: feature extraction, pose estimation, and identity classification. This segmentation allows the system to efficiently process a single image by directing different parts of the input through specialized processing pathways, achieving accurate recognition without requiring multiple images or additional sensory resources
Solution Approach 2:
The patent transforms the recognition problem by adding a pose dimension to the traditional identity-only recognition framework. By representing objects in a joint pose-identity space, the system can disentangl epose variations from identity features, enabling accurate recognition from single images despite pose variations, thus avoiding the need for multiple images to cover different poses
3Adaptability or versatility
If the system processes objects in various poses from single images, then practical applicability improves, but self-occlusions and front-back symmetry issues cause inaccuracies
Solution Approach 1:
The patent introduces an intermediary pose estimation module that acts as a mediator between the input image and the identity classification. This intermediate step explicitly estimates pose parameters (rotation and translation) before identity recognition, allowing the system to compensate for self-occlusions and symmetry ambiguities by adjusting the recognition process based on the estimated pose, thereby improving accuracy while maintaining single-image processing capability
Data Source
AI summary
A method of one shot joint instance and pose recognition in an artificial neural network is presented. The method includes receiving a reference instance of a reference object from a reference image. The reference object has a first identity and a first pose in the reference instance. The method also includes generating a first orbit of the reference object comprising multiple additional poses including a second pose for the reference object. The method further includes recognizing a second instance of an example object from an example image. The example object has the first identity and the second pose in the second instance. The method still further includes recognizing the second pose and first identity of the example object based on comparing the first orbit with a second orbit of the example object.