Object Detection Neural Network Token Sequence Architecture
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Solution Overview
Problem
Existing object detection methods require highly customized and complex architectures, making it difficult to integrate them into larger systems.
Innovation Solution
A neural network-based system that processes input images to generate a sequence of discrete tokens representing object descriptions, allowing for a simple architecture that can be easily integrated into perception systems and extended to different domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing object detection methods are used, then detection performance can be achieved, but the architecture becomes complex and difficult to integrate
Solution Approach 1:
The object detection task is segmented into discrete token generation steps, where each token represents a specific object attribute or category. This segmentation allows the complex detection problem to be broken down into manageable, sequential decisions that can be made by a simpler neural network architecture.
Solution Approach 2:
The patent extracts the essential object detection function into a standalone neural network that generates discrete tokens. This extracted module can be independently integrated into larger perception systems without requiring complex custom architectures, as the token generation mechanism encapsulates the core detection logic.
2Measurement precision
If complex architectures are used for object detection, then detection accuracy improves, but integration into larger systems becomes difficult
Solution Approach 1:
The neural network architecture is designed with universal applicability through its token generation mechanism. The same simple architecture can be adapted to different domains and applications by changing the token vocabulary and training data, without requiring rearchitecture. This multi-functionality enables easy integration into various perception systems.
Solution Approach 2:
The system achieves adaptability by changing parameters such as the token vocabulary size, number of tokens per object, and network depth rather than changing the fundamental architecture. This allows the same base model to be adapted to different object detection tasks and domains while maintaining integration simplicity.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for object detection using neural networks. In one aspect, one of the methods includes obtaining an input image; processing the input image using an object detection neural network to generate an output sequence that comprises respective token at each of a plurality of time steps, wherein each token is selected from a vocabulary of tokens that comprises (i) a first set of tokens that each represent a respective discrete number from a set of discretized numbers and (ii) a second set of tokens that each represent a respective object category from a set of object categories; and generating, from the tokens in the output sequence, an object detection output for the input image.