Summary of Classification Of Freshwater Snails Of the Genus Radomaniola with Multimodal Triplet Networks, by Dennis Vetter and Muhammad Ahsan and Diana Delicado and Thomas A. Neubauer and Thomas Wilke and Gemma Roig
Classification of freshwater snails of the genus Radomaniola with multimodal triplet networks
by Dennis Vetter, Muhammad Ahsan, Diana Delicado, Thomas A. Neubauer, Thomas Wilke, Gemma Roig
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 This paper proposes a machine learning system for classifying freshwater snails of the genus Radomaniola. The system uses triplet networks and combines multiple input modalities like images, measurements, and genetic information to overcome challenges in the small, imbalanced dataset with high visual similarity between classes. The proposed approach reaches performance comparable to that of a trained domain expert. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand freshwater snails by creating a machine learning system to classify them. It’s hard because we only have a little data and many types of snails look similar. To solve this, the scientists use special networks and combine different kinds of information like pictures, measurements, and genetic details. The result is as good as what an expert can do. |
Keywords
» Artificial intelligence » Machine learning
