What are some research on small-scale vision language models?
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Research on small-scale vision language models includes studies on distillation techniques to transfer knowledge from large models to smaller ones, self-taught data augmentation for finetuning large models on small datasets without additional annotations, and the use of sparsely-gated mixture-of-experts (MoE) to scale vision-language models effectively. These approaches aim to address the challenges of deploying large models on resource-constrained devices and time-sensitive tasks by creating smaller, more efficient models while maintaining performance. Techniques like distillation and self-taught data augmentation have shown significant improvements in zero-shot and few-shot student performance on out-of-distribution classification tasks, highlighting the effectiveness of these methods in enhancing model generalization and robustness.
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| Papers (5) | Insight |
|---|---|
13 Mar 2023 | Not addressed in the paper. |
| "DIME-FM introduces distillation to transfer knowledge from large VLFMs to smaller models using minimal data, achieving comparable performance on various benchmarks with limited resources." | |
| Self-Taught Data Augmentation (SelTDA) is a method for finetuning large vision language models on small-scale VQA datasets using unlabeled images, enhancing robustness and domain generalization. | |
06 Jun 2023 | Research introduces SelTDA, a method for finetuning large VLMs on small-scale VQA datasets using unlabeled images, enhancing robustness, domain generalization, and numerical reasoning without additional annotations. |
| The research paper explores distilling large vision-language models into smaller ones for out-of-distribution generalization, enhancing student performance on open-vocabulary tasks with proposed principles and metrics. |
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