Visual Question Answering Deploy select models (i.e. YOLOv8, CLIP) using the Roboflow Hosted API, or your own hardware using Roboflow Inference. Showing of models. PaliGemma Document VQA You can use the set of PaliGemma weights trained on the DocVQA dataset for asking questions about documents...
RVL-CDIP dataset 上的分类结果 文档信息抽取 文档信息抽取结果 文档VQA DocVQA结果 4.4. 进一步分析 图7 (a) 预训练策略、(b) 图像主干和 (c) 输入分辨率分析。 预训练策略 图7(a) 显示 Donut 预训练任务(即文本阅读)是一种简单而有效的方法。对模型(例如图像字幕)施加图像和文本的一般知识的其他任务在微调...
文档上的问题回答 ( Question Answering) 已经极大地改变了人们与人工智能的交互方式,最近的进展使得要求模型回答关于图像的问题成为可能 —— 这被称为文档视觉回答,或简称 DocVQA dataset。在得到一个问题后,模型会分析图像,并回答一个答案,下图是 DocVQA 数据集 的一个例子:...
classical few-shot IR dataset: SOTA 2nd on MS MARCO 工作类比 1. MORES(MORES+)和ColBERT的比较: 相似 - 先尽可能的分离query和document encoding,从而让document embedding pre-computation有可能 - 都需要对document的每个token embedding 存储 (与DPR-family存储single embedding per passage对比) 不同 - ColBE...
In this report we present results of the ICDAR 2021 edition of the Document Visual Question Challenges. This edition complements the previous tasks on Single Document VQA and Document Collection VQA with a newly introduced on Infographics VQA. Infographics VQA is based on a new dataset of more ...
Recent advancements have made it possible to ask models to answer questions about an image - this is known as document visual question answering, or DocVQA for short. After being given a question, the model analyzes the image and responds with an answer. An example from the DocVQA ...
Provide the location coordinates of the answer when answering the question 表1: 各种任务的prompt 3.4 Dataset Construction 在我们的训练过程中,我们仅利用开源数据并将各种特定于任务的增强应用于不同的数据集。 通过整合各种数据集并对不同的任务采用不同的指令,我们增强了模型的学习能力和训练效率。 对于场景...
Question Answering Question Answering. What is the ship year? 1994 Document NLI Document Natural Language Inference. Ship Date to Retail: Week of March 14, 1994 Entailment. Input Sequence: “Joint Text-Layout Reconstruction. to Retail: Week March 14, 1994” Target Sequence: “ Ship Date <100...
[13] Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki. LAION-400M: Open dataset of clip-filtered 400 million image-text pairs. arXiv preprint arXiv:2111.02114, 2021. 5 ...
The dataset includes: A variety of document images. Question-answer pairs for each document. Annotations to facilitate training and evaluation of DocVQA models. License This project is licensed under the MIT License. See the LICENSE file for details. ...