Summary of Vibes — Vision Backbone Efficient Selection, by Joris Guerin et al.
VIBES – Vision Backbone Efficient Selection
by Joris Guerin, Shray Bansal, Amirreza Shaban, Paulo Mann, Harshvardhan Gazula
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 proposed Vision Backbone Efficient Selection (VIBES) method efficiently selects high-performance pre-trained vision backbones for specific target tasks by quickly finding well-suited backbones, potentially trading off optimality for efficiency. The approach uses simple yet effective heuristics evaluated across four diverse computer vision datasets. Results show that VIBES can identify backbones outperforming those selected from generic benchmarks, even within a limited search budget of one hour on a single GPU. This marks a paradigm shift from benchmarks to task-specific optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VIBES is a new way to find the best pre-trained vision models for specific tasks. Normally, you would have to test many different models to find the best one, but this takes too long when there are many models and datasets. VIBES tries to quickly find good models by using simple rules. It works well even when you only have an hour on a single computer processor. This is important because it means you can use VIBES in real-world situations. |
Keywords
* Artificial intelligence * Optimization
