Challenges of Multi-Modal Coreset Selection for Depth Prediction
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866917933792362496 |
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| author | Moskvoretskii, Viktor Alvandian, Narek |
| author_facet | Moskvoretskii, Viktor Alvandian, Narek |
| contents | Coreset selection methods are effective in accelerating training and reducing memory requirements but remain largely unexplored in applied multimodal settings. We adapt a state-of-the-art (SoTA) coreset selection technique for multimodal data, focusing on the depth prediction task. Our experiments with embedding aggregation and dimensionality reduction approaches reveal the challenges of extending unimodal algorithms to multimodal scenarios, highlighting the need for specialized methods to better capture inter-modal relationships. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15834 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Challenges of Multi-Modal Coreset Selection for Depth Prediction Moskvoretskii, Viktor Alvandian, Narek Machine Learning Coreset selection methods are effective in accelerating training and reducing memory requirements but remain largely unexplored in applied multimodal settings. We adapt a state-of-the-art (SoTA) coreset selection technique for multimodal data, focusing on the depth prediction task. Our experiments with embedding aggregation and dimensionality reduction approaches reveal the challenges of extending unimodal algorithms to multimodal scenarios, highlighting the need for specialized methods to better capture inter-modal relationships. |
| title | Challenges of Multi-Modal Coreset Selection for Depth Prediction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.15834 |