Summary of Cksp: Cross-species Knowledge Sharing and Preserving For Universal Animal Activity Recognition, by Axiu Mao and Meilu Zhu and Zhaojin Guo and Zheng He and Tomas Norton and Kai Liu
CKSP: Cross-species Knowledge Sharing and Preserving for Universal Animal Activity Recognition
by Axiu Mao, Meilu Zhu, Zhaojin Guo, Zheng He, Tomas Norton, Kai Liu
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 presents a novel approach called Cross-species Knowledge Sharing and Preserving (CKSP) for automated animal activity recognition (AAR) tasks with wearable sensors. Traditional deep learning-based AAR models are limited by their reliance on large-scale labelled data from individual species, which restricts their applicability in practice. The proposed CKSP framework addresses this limitation by sharing knowledge across multiple species, leveraging generic and species-specific features. The approach employs a Shared-Preserved Convolution (SPConv) module that extracts species-specific features and learns generic features, as well as a Species-specific Batch Normalization (SBN) module to handle discrepancies in data distributions among species. Experimental results on three public datasets demonstrate the effectiveness of CKSP, with significant improvements in classification performance compared to traditional one-for-one frameworks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using machine learning to recognize animal behavior from wearable sensors. Current methods are good at recognizing specific types of animals, but they struggle when there’s not enough data or when they need to recognize different types of animals. The researchers propose a new approach that can share knowledge across many different animal species, which makes it more effective and flexible. They use this approach on three datasets of horse, sheep, and cattle activity recognition, and the results show significant improvements over traditional methods. |
Keywords
» Artificial intelligence » Activity recognition » Batch normalization » Classification » Deep learning » Machine learning
