Tokenized Vision Network for Multi-Task Inference with Lower Memory Use
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing computer vision tasks require distinct architectures and loss functions due to differing input and output spaces, leading to inefficiencies in memory usage and performance across multiple tasks.
Innovation Solution
A shared computer vision neural network that processes images using a unified interface represented as tokenized sequences, allowing a single model to perform multiple tasks with a single architecture and loss function, reducing memory consumption and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate neural networks are used for each computer vision task, then task-specific performance is optimized, but memory consumption increases significantly
Solution Approach 1:
The patent implements a universal neural network architecture that can perform multiple computer vision tasks (image classification, object detection, segmentation) through a single model. The network uses a shared backbone for feature extraction and task-specific heads for different outputs, allowing one network to replace multiple task-specific networks, thereby reducing memory consumption while maintaining competitive performance across tasks.
2Reliability
If distinct architectures and loss functions are used for different vision tasks, then each task is optimized independently, but system complexity increases
Solution Approach 1:
The patent segments the neural network into modular components: a shared backbone network for common feature extraction and task-specific head networks for individual task processing. This segmentation allows each task to have its optimized loss function and head architecture while sharing the computational backbone, reducing overall system complexity compared to completely separate networks while maintaining task-specific optimization.
Solution Approach 2:
The shared backbone network serves multiple functions by extracting features that are useful across different computer vision tasks. This universal feature extraction mechanism reduces the need for separate architectures, simplifying the overall system while still allowing task-specific customization through different head networks and loss functions.
3Adaptability or versatility
If multiple task-specific neural networks are deployed, then comprehensive task coverage is achieved, but deployment efficiency decreases
Solution Approach 1:
The patent creates a multi-functional neural network that can perform multiple computer vision tasks within a single deployment. By sharing the backbone network and using task-specific heads, the system achieves comprehensive task coverage (classification, detection, segmentation) while improving deployment efficiency through a single model file and unified inference pipeline, eliminating the need to deploy and manage multiple separate networks.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing multiple computer vision tasks using a shared computer vision neural network. In one aspect, one of the methods includes obtaining an input image; processing the input image and a prompt sequence using a shared computer vision 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 shared vocabulary of tokens that is shared between the plurality of computer vision tasks, wherein the shared vocabulary 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 natural language text token.