Tile Compression and Embeddings for Multi-Label Classification in GeoLifeCLEF 2024
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910518626746368 |
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| author | Miyaguchi, Anthony Aphiwetsa, Patcharapong McDuffie, Mark |
| author_facet | Miyaguchi, Anthony Aphiwetsa, Patcharapong McDuffie, Mark |
| contents | We explore methods to solve the multi-label classification task posed by the GeoLifeCLEF 2024 competition with the DS@GT team, which aims to predict the presence and absence of plant species at specific locations using spatial and temporal remote sensing data. Our approach uses frequency-domain coefficients via the Discrete Cosine Transform (DCT) to compress and pre-compute the raw input data for convolutional neural networks. We also investigate nearest neighborhood models via locality-sensitive hashing (LSH) for prediction and to aid in the self-supervised contrastive learning of embeddings through tile2vec. Our best competition model utilized geolocation features with a leaderboard score of 0.152 and a best post-competition score of 0.161. Source code and models are available at https://github.com/dsgt-kaggle-clef/geolifeclef-2024. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_06326 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Tile Compression and Embeddings for Multi-Label Classification in GeoLifeCLEF 2024 Miyaguchi, Anthony Aphiwetsa, Patcharapong McDuffie, Mark Computer Vision and Pattern Recognition We explore methods to solve the multi-label classification task posed by the GeoLifeCLEF 2024 competition with the DS@GT team, which aims to predict the presence and absence of plant species at specific locations using spatial and temporal remote sensing data. Our approach uses frequency-domain coefficients via the Discrete Cosine Transform (DCT) to compress and pre-compute the raw input data for convolutional neural networks. We also investigate nearest neighborhood models via locality-sensitive hashing (LSH) for prediction and to aid in the self-supervised contrastive learning of embeddings through tile2vec. Our best competition model utilized geolocation features with a leaderboard score of 0.152 and a best post-competition score of 0.161. Source code and models are available at https://github.com/dsgt-kaggle-clef/geolifeclef-2024. |
| title | Tile Compression and Embeddings for Multi-Label Classification in GeoLifeCLEF 2024 |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.06326 |