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Harnessing the Power of Deep Learning for Image Segmentation
Revolutionizing numerous industries, deep learning for image segmentation encounters the obstacle of limited training data. Collecting diverse and accurately annotated datasets is time-consuming, expensive, and challenging due to privacy concerns.
Practical Solutions and Value
In 2023, the Segment Anything Model (SAM) from Meta AI and advancements in zero- and few-shot image segmentation have reduced the need for extensive datasets, cutting costs and implementation time.
Fundamental Concepts
Image Segmentation
Partitioning images into segments or objects with applications in medical analysis, autonomous driving, and augmented reality.
Supervised Learning
Training algorithms using input examples paired with expected outputs, such as raw images and corresponding segmentation masks.
Zero-shot Learning
Solving tasks for classes not observed during training by relying on descriptions of these classes or tasks.
Few-shot Learning
Involves training a network with a small set of labeled images known as a support set.
Segment Anything Model (SAM)
Performs image segmentation based on various prompts, bringing computational efficiency through the reuse of embeddings.
Methods
Lang SAM and Grounded Segment Anything
Improving text-based prompts by incorporating automatic image tagging and support for various versions of SAM.
SEEM
Extends SAM by introducing more types of prompts and enables segmentation based on an exemplary image.
SegGPT
Translates successful NLP approaches into computer vision, allowing for few-shot inference and prompt engineering.
PerSAM
Calculates the embedding of the masked region and improves itself through multiple passes.
ClipSeg
Utilizes CLIP for image segmentation, working in both zero- and one-shot scenarios.
Comparison
Quantitative comparison using the umbrella segmentation task to determine the amount of training data required for supervised models to achieve comparable accuracy to zero-shot and few-shot learners.
Conclusions
The Segment Anything Model has revolutionized addressing data scarcity in image segmentation, surpassing the performance of models trained on thousands of examples with no data in certain cases.
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