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Found 2 papers, 2 papers with code
Genixer: Empowering Multimodal Large Language Models as a Powerful Data Generator
Multimodal Large Language Models (MLLMs) demonstrate exceptional problem-solving capabilities, but there is limited research focusing on their ability to generate data by converting unlabeled images into visual instruction tuning data.
SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient Channels
Experiments on 19 visual transfer learning downstream tasks demonstrate that our SCT outperforms full fine-tuning on 18 out of 19 tasks by adding only 0. 11M parameters of the ViT-B, which is 780$\times$ fewer than its full fine-tuning counterpart.
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