GAN Discriminator for Object Re-Identification Accuracy
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Solution Overview
Problem
Existing datasets for object re-identification are often collected for object detection purposes, making them inadequate for distinguishing between very similar objects, which hinders the reliability of re-identification systems.
Innovation Solution
A method utilizing a Generative Adversarial Network (GAN) with a discriminator neural network for object re-identification, where a generator network generates modified images to train the discriminator, focusing on key features like nose, eyes, or lips, and a second discriminator assesses the realism of generated images to improve re-identification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If datasets collected for object detection are used for re-identification, then the system can process general objects, but it cannot reliably distinguish between very similar objects
Solution Approach 1:
The system performs preliminary action by generating synthetic training data that specifically targets edge cases of similar objects before actual re-identification occurs. The GAN generates challenging training samples in advance, preparing the neural network to handle difficult discrimination scenarios that would otherwise fail with standard detection datasets
Solution Approach 2:
The invention changes parameters by transforming standard object detection datasets into specialized re-identification training data through GAN-generated modifications. The system alters image parameters by generating variations that emphasize subtle differences between similar objects, thereby adapting the training data to suit the specific precision requirements of re-identification tasks
2Measurement precision
If a GAN is used to generate edge-case training samples, then re-identification accuracy improves, but system complexity increases
Solution Approach 1:
The GAN system performs multiple functions: it generates edge-case training samples, validates their realism, and adapts training data to specific re-identification scenarios. This multi-functionality justifies the increased complexity by consolidating several data processing tasks into a single unified system that improves re-identification accuracy across diverse object types
Solution Approach 2:
The GAN acts as an intermediary between standard object detection datasets and the re-identification task. It mediates by transforming general-purpose detection data into specialized training samples that bridge the gap between detection capability and re-identification precision, thereby justifying the added system complexity
3Reliability
If the discriminator neural network is trained with generated images, then it can distinguish similar objects, but training time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-generating comprehensive edge-case training samples using the GAN before the actual re-identification deployment. This upfront generation of challenging training data ensures the discriminator is thoroughly trained in advance, reducing the need for iterative retraining and improving reliability without continuous time loss
Solution Approach 2:
The training process changes parameters by optimizing the balance between generated sample quantity and training duration. The system adjusts training parameters to achieve sufficient discriminator reliability while managing computational resources, allowing flexible adaptation to different deployment scenarios with varying time constraints
Data Source
AI summary
The invention relates to a method comprising receiving, by a neural network, a first image comprising at least one target object; receiving, by the neural network, a second image comprising at least one query object; and determining, by the neural network, whether the query object corresponds to the target object, wherein the neural network comprises a discriminator neural network of a generative adversarial network (GAN). The invention further relates to an apparatus and a computer program product that perform the method.