One-Shot Instance Segmenter Training via Synthesized Image Augmentation
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Solution Overview
Problem
One-shot instance segmenters face significant performance degradation when the background changes dramatically, as they are typically trained on a single frame and struggle to predict new content not present in the annotated image, leading to poor segmentation results.
Innovation Solution
The system synthesizes training images by augmenting foregrounds and backgrounds using a content-correlated image synthesizer, which includes a foreground augmenter and a background augmenter, to generate diverse training sets that can handle scene changes, allowing the model to be trained without annotated external images and improving precision and recall.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If one-shot instance segmenters are trained on a single annotated frame, then training efficiency is improved and annotation cost is reduced, but segmentation performance degrades when background changes dramatically
Solution Approach 1:
The system performs preliminary action by synthesizing diverse training images beforehand using a background augmenter that combines the annotated foreground with multiple external background images. This pre-synthesis of varied backgrounds allows the model to be exposed to different scene configurations during training, improving its ability to handle background changes without requiring extensive manual annotation of multiple frames.
Solution Approach 2:
The system uses copying by replicating the annotated foreground object across multiple synthesized images with different backgrounds. The foreground mask and object features are copied and preserved while only the background varies, allowing the model to learn robust object representation that is invariant to background changes. This copying approach maintains training efficiency while improving generalization.
2Quantity of substance
If one-shot instance segmenters are trained on a single annotated frame, then annotation cost is reduced, but the model fails to predict new content not present in the annotated image
Solution Approach 1:
The system performs preliminary action by pre-synthesizing training images with diverse backgrounds using the background augmenter. This allows the model to be exposed to a variety of scene configurations beforehand, improving its ability to adapt to new content and backgrounds during inference without requiring additional annotated examples. The preliminary synthesis of varied contexts enables better generalization to unseen scenarios.
Solution Approach 2:
The system applies parameter changes by systematically varying the background parameters in synthesized training images while keeping the foreground object consistent. The background augmenter changes background characteristics such as scene type, lighting conditions, and environmental context, allowing the model to learn invariant object features that generalize across different parameter settings without requiring re-annotation.
3Loss of time
If traditional training methods are used with limited annotated data, then training time is reduced, but segmentation precision and recall deteriorate
Solution Approach 1:
The system uses copying by replicating the single annotated foreground object across multiple synthesized training images with different backgrounds. This creates an expanded training dataset without requiring additional annotation time, allowing the model to learn from more diverse examples while maintaining the original annotation effort. The copied foreground instances provide sufficient training signal to improve precision and recall.
Solution Approach 2:
The system applies parameter changes by varying background parameters in synthesized images while preserving the foreground object. This generates training diversity through parameter variation rather than through additional annotation, enabling the model to achieve better segmentation precision and recall by learning from multiple background configurations without increasing training time or annotation effort.
Data Source
AI summary
An example apparatus for generating synthesized images includes a receiver to receive a frame, a mask and external images. The apparatus also includes a foreground augmenter to generate augmented foregrounds in the frame based on the mask. The apparatus includes a background augmenter to generate augmented backgrounds based on the frame, the mask, and the external images. The apparatus also further includes an image synthesizer to generate a synthesized image based on the generated augmented foregrounds and the augmented backgrounds.