3 papers
cs.CV2026
Dynamic Cluster Data Sampling for Efficient and Long-Tail-Aware Vision-Language Pre-training
Mingliang Liang, Zhuoran Liu, Arjen P. de Vries +1
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 import…
cs.CV2026
Frequency Is What You Need: Considering Word Frequency When Text Masking Benefits Vision-Language Model Pre-training
Mingliang Liang, Martha Larson
Vision Language Models (VLMs) can be trained more efficiently if training sets can be reduced in size. Recent work has shown the benefits of masking text during VLM training using…
cs.LG2024
Enhancing Vision-Language Model Pre-training with Image-text Pair Pruning Based on Word Frequency
Mingliang Liang, Martha Larson
We propose Word-Frequency-based Image-Text Pair Pruning (WFPP), a novel data pruning method that improves the efficiency of VLMs. Unlike MetaCLIP, our method does not need metadata…