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arXiv · 2502.15834

Challenges of Multi-Modal Coreset Selection for Depth Prediction

Viktor Moskvoretskii·Narek Alvandian
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Abstract

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.

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Viktor Moskvoretskii, Narek Alvandian. 2025-02-20. Challenges of Multi-Modal Coreset Selection for Depth Prediction. https://arxiv.org/abs/2502.15834

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