arxiv preprint – LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding

In this episode we discuss LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding by Yanzhe Zhang, Ruiyi Zhang, Jiuxiang Gu, Yufan Zhou, Nedim Lipka, Diyi Yang, Tong Sun. The paper introduces LLaVAR, an enhanced visual instruction tuning method for text-rich image understanding. The method addresses the limitation of existing pipelines in comprehending textual details within images by incorporating text-rich images and OCR tools. Experimental results show that LLaVAR improves the performance of the LLaVA model on text-based visual question answering datasets, achieving up to 20% accuracy improvement. The model also exhibits promising interaction skills with humans based on real-world online content that combines text and images.