Summary of Annolid: Annotate, Segment, and Track Anything You Need, by Chen Yang et al.
Annolid: Annotate, Segment, and Track Anything You Need
by Chen Yang, Thomas A. Cleland
First submitted to arxiv on: 27 Mar 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 A deep learning-based software package called Annolid is designed for segmenting, labeling, and tracking research targets within video files. It focuses on animal behavior analysis and uses state-of-the-art instance segmentation methods, including the Cutie model. This enables markerless tracking of multiple animals from single annotated frames, even when they’re partially concealed by environmental features or each other. Annolid also integrates strategies like Segment Anything and Grounding-DINO to automatically mask and segment recognizable animals and objects using text commands, eliminating manual annotation needs. The software’s comprehensive approach accommodates a broad range of behavior analysis applications, allowing classification of diverse behavioral states such as freezing, digging, pup huddling, and social interactions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Annolid is a special computer program that helps scientists study animal behavior by looking at videos of animals. It uses advanced technology to identify and track individual animals in the videos, even when they’re hidden or moving around. This makes it easier for scientists to understand what the animals are doing and how they interact with each other. |
Keywords
» Artificial intelligence » Classification » Deep learning » Grounding » Instance segmentation » Mask » Tracking
