Dynamic Cluster Data Sampling for Efficient and Long-Tail-Aware Vision-Language Pre-training

TL;DR

This paper introduces DynamiCS, a dynamic cluster-based sampling method that reduces training costs and improves long-tail concept representation in vision-language models by adjusting data sampling at each epoch.

Contribution

The paper proposes a novel dynamic sampling approach that emphasizes long-tail concepts, contrasting with existing methods that flatten data distribution.

Findings

DynamiCS reduces computational cost of VLM training.

It improves representation of long-tail concepts.

Dynamic sampling outperforms static approaches.

Abstract

The computational cost of training a vision-language model (VLM) can be reduced by sampling the training data. Previous work on efficient VLM pre-training has pointed to the importance of semantic data balance, adjusting the distribution of topics in the data to improve VLM accuracy. However, existing efficient pre-training approaches may disproportionately remove rare concepts from the training corpus. As a result, \emph{long-tail concepts} remain insufficiently represented in the training data and are not effectively captured during training. In this work, we introduce a \emph{dynamic cluster-based sampling approach (DynamiCS)} that downsamples large clusters of data and upsamples small ones. The approach is dynamic in that it applies sampling at each epoch. We first show the importance of dynamic sampling for VLM training. Then, we demonstrate the advantage of our cluster-scaling approach, which maintains the relative order of semantic clusters in the data and emphasizes the long-tail. This approach contrasts with current work, which focuses only on flattening the semantic distribution of the data. Our experiments show that DynamiCS reduces the computational cost of VLM training and provides a performance advantage for long-tail concepts.