Summary of Adapting Segment Anything Model (sam) to Experimental Datasets Via Fine-tuning on Gan-based Simulation: a Case Study in Additive Manufacturing, by Anika Tabassum et al.
Adapting Segment Anything Model (SAM) to Experimental Datasets via Fine-Tuning on GAN-based Simulation: A Case Study in Additive Manufacturing
by Anika Tabassum, Amirkoushyar Ziabari
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper explores the application of advanced computer vision models in industrial X-ray computed tomography (XCT) for non-destructive characterization of materials and manufactured components. Specifically, it investigates the use of the Segment Anything Model (SAM), a state-of-the-art model designed for general-purpose image segmentation, in industrial XCT inspection of additive manufacturing components. The study finds that while SAM shows promise, it struggles with out-of-distribution data, multiclass segmentation, and computational efficiency during fine-tuning. To address these issues, the authors propose a fine-tuning strategy using parameter-efficient techniques, such as Conv-LoRa, to adapt SAM for material-specific datasets. Additionally, they leverage generative adversarial network (GAN)-generated data to enhance the training process and improve the model’s segmentation performance on complex X-ray CT data. The experimental results highlight the importance of tailored segmentation models for accurate inspection, showing that fine-tuning SAM on domain-specific scientific imaging data significantly improves performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using a special kind of computer program to help analyze images taken with an X-ray machine. This machine helps us look at things without damaging them, which is important in manufacturing and materials science. The authors are trying to figure out if this program can be used for this purpose, and they’re finding that it’s not perfect yet. They’re working on making the program better by giving it special training data and using other techniques to help it learn. This will help us get more accurate results when we use the X-ray machine. |
Keywords
* Artificial intelligence * Fine tuning * Gan * Generative adversarial network * Image segmentation * Lora * Parameter efficient * Sam
