Guiding Code Sequences for Structured Vision Output Tasks
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Solution Overview
Problem
Existing neural networks struggle with high-dimensional structured output tasks, requiring complex interaction modeling and leading to inefficient performance due to the need for task-specific architecture tuning.
Innovation Solution
Utilizing guiding code sequences as an additional input to a base computer vision neural network, which is trained jointly with a restricted oracle network to generate high-quality outputs for tasks like panoptic segmentation, depth estimation, and colorization, by simplifying the architecture and reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a base computer vision neural network processes only the input image without guiding code sequences, then the architecture can be simpler, but the performance on high-dimensional structured output tasks is significantly worse
Solution Approach 1:
The patent introduces guiding code sequences as an intermediary between the input image and the base neural network. These sequences act as a mediator that guides the network's attention and processing, enabling high performance on structured output tasks without requiring the base network to be overly complex. The guiding codes provide structured predictions that help the network focus on relevant features and relationships.
2Reliability
If task-specific architecture hyperparameter tuning is performed to improve performance on specific computer vision tasks, then the performance on those tasks improves, but the computational cost and time required for tuning increases significantly
Solution Approach 1:
The patent creates a universal base computer vision neural network architecture that can be adapted to multiple different computer vision tasks through training with guiding code sequences. This universal architecture eliminates the need for task-specific architecture hyperparameter tuning, as the same base network can perform well across different tasks (e.g., panoptic segmentation, depth estimation, colorization) by learning from diverse training data with appropriate guiding codes.
3Reliability
If a larger base computer vision neural network is used to improve performance on panoptic segmentation, then the performance improves, but the model size and computational requirements increase
Solution Approach 1:
The guiding code sequences serve as an intermediary that enhances the effectiveness of a smaller base network. By providing structured guidance and predictions, these codes enable a compact model to achieve performance levels that would traditionally require much larger models. The guiding codes compensate for the reduced capacity of the base network by providing additional structural information and constraints.
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 sequence transduction 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.