Summary of Geobiked: a Dataset with Geometric Features and Automated Labeling Techniques to Enable Deep Generative Models in Engineering Design, by Phillip Mueller et al.
GeoBiked: A Dataset with Geometric Features and Automated Labeling Techniques to Enable Deep Generative Models in Engineering Design
by Phillip Mueller, Sebastian Mueller, Lars Mikelsons
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| 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 paper proposes a dataset called GeoBiked for enabling Deep Generative Models (DGMs) in engineering design. The dataset contains 4,355 bicycle images annotated with structural and technical features. To automate data labeling, the authors utilize large-scale foundation models to investigate two automated labeling techniques: utilizing consolidated latent features (Hyperfeatures) from image-generation models to detect geometric correspondences in structural images and generating diverse text descriptions for structural images using vision-language-models (VLMs). The authors demonstrate that representing technical images as Diffusion-Hyperfeatures enables the detection of geometric points in unseen samples with improved accuracy. They also show that VLMs can generate accurate descriptions of technical images, but require careful prompt-engineering and input selection to balance creativity and accuracy. The paper suggests that applying foundation models in engineering design is largely unexplored and aims to bridge this gap by proposing a dataset for training, fine-tuning, and conditioning DGMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a big picture by providing a new dataset (GeoBiked) to help machines learn from technical images. They use special computer models called foundation models to automate the process of labeling these images with important information. The authors test two ways to do this: using a combination of image and text features to detect specific parts in an image, and generating multiple descriptions for each image using another type of model. They show that their approach can be useful for tasks like detecting points in an image or generating descriptions for technical images. Overall, the paper takes a step towards using these computer models in new areas like engineering design. |
Keywords
» Artificial intelligence » Data labeling » Diffusion » Fine tuning » Image generation » Prompt
