Reducing Language Biases In Visual Question Answering With Visually Using data from the retail 786k [10] dataset, we investigate the capabilities of pre trained vlms to answer detailed questions about advertised products in images. Our caption based model, denoted by cbm, is divided in two steps: (i) a caption generation system that generates a short description of a given image and (ii) a language model that takes this caption and a question in order to answer it.
Enriching Visual Question Answering Models With Textual Information Vlms are general purpose ai models capable of performing multiple vision language tasks, whereas vqa is task specific, focusing only on answering image based questions. Bibliographic details on can visual language models replace ocr based visual question answering pipelines in production? a case study in retail. This project demonstrates how to use the qwen2 vl model from hugging face for optical character recognition (ocr) and visual question answering (vqa). the model combines vision and language capabilities, enabling users to analyze images and generate context based responses. Vqa tasks require models to understand both visual content (e.g., images or videos) and textual questions, then generate accurate answers. vlms, which are pre trained on large scale image text datasets, excel at aligning visual and linguistic features.
Can Visual Language Models Replace Ocr Based Visual Question Answering This project demonstrates how to use the qwen2 vl model from hugging face for optical character recognition (ocr) and visual question answering (vqa). the model combines vision and language capabilities, enabling users to analyze images and generate context based responses. Vqa tasks require models to understand both visual content (e.g., images or videos) and textual questions, then generate accurate answers. vlms, which are pre trained on large scale image text datasets, excel at aligning visual and linguistic features. A multimodal fusing mechanism that combines visual and text references. key criteria to assess the best vlms of 2025: 1. accuracy of vision text reasoning vision language models (vlms) are required to do complicated things, like visual question answering (vqa), optical character recognition (ocr), and image captioning. This paper explores whether visual language models can replace traditional ocr based visual question answering pipelines in production settings, using a retail case study. Hence, the research question arises: can we replace ocr based vqa pipelines with vlms at a production level? we investigate this question on a use case derived from the retail domain. As vision language models (vlms) demonstrate remarkable capabilities in zero shot inference, the need for a structured approach to evaluate these models has never been more urgent.
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