Summary of Performance Of Computer Vision Algorithms For Fine-grained Classification Using Crowdsourced Insect Images, by Rita Pucci et al.
Performance of computer vision algorithms for fine-grained classification using crowdsourced insect images
by Rita Pucci, Vincent J. Kalkman, Dan Stowell
First submitted to arxiv on: 4 Apr 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 computer vision approach to improve species recognition in insects, which is crucial for biodiversity monitoring. The authors leverage citizen science campaigns that collect vast amounts of labelled images, enabling experts to create distribution maps. However, the labelling process is time-consuming, so they evaluate nine algorithms from deep convolutional networks (CNN), vision transformers (ViT), and locality-based vision transformers (LBVT) based on classification performance, embedding quality, computational cost, and gradient activity. The results show that ViT excels in inference speed and computational cost, while LBVT outperforms others in performance and embedding quality; CNN offers a trade-off among the metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better recognize insect species by using computer vision. Imagine having a special tool to help scientists identify different types of insects. The authors are trying to find the best way to use this tool for a task called species recognition. They look at many images taken by people and label them, so experts can create maps showing where each type of insect lives. But it takes a lot of time to do this labelling, so they’re exploring different computer vision algorithms to see which one works the best. |
Keywords
» Artificial intelligence » Classification » Cnn » Embedding » Inference » Vit
