Omkar2118/datasets huggingface dataset:
LLM-Seg40K
Full name: LLM-Seg40K

LLM-Seg40K dataset contains 14K images in total. The dataset is divided into training, validation, and test sets, containing 11K, 1K, and 2K images respectively. For the
training split, each image has 3.95 questions on average and the average question question length is 15.2 words. The training set contains 1458 different categories in total.

Introducing paper: LLM-Seg: Bridging Image Segmentation and Large Language Model Reasoning
OpenViVQA
Full name: Open-domain Visual Question Answering in Vietnamese

In recent years, visual question answering (VQA) has attracted attention from the research community because of its highly potential applications (such as virtual assistance on intelligent cars, assistant devices for blind people, or information retrieval from document images using natural language as queries) and challenge. The VQA task requires methods that have the ability to fuse the information from questions and images to produce appropriate answers. Neural visual question answering models have achieved tremendous growth on large-scale datasets which are mostly for resource-rich languages such as English. However, available datasets narrow the VQA task as the answers selection task or answer classification task. We argue that this form of VQA is far from human ability and eliminates the challenge of the answering aspect in the VQA task by just selecting answers rather than generating them. In this paper, we introduce the OpenViVQA (Open-domain Vietnamese Visual Question Answering) dataset, the first large-scale dataset for VQA with open-ended answers in Vietnamese, consists of 11,000+ images associated with 37,000+ question–answer pairs (QAs).

Introducing paper: OpenViVQA: Task, Dataset, and Multimodal Fusion Models for Visual Question Answering in Vietnamese
EVJVQA
Full name: English-Japanese-Vietnamese Visual Question Answering

EVJVQA, the first multilingual Visual Question Answering dataset with three languages: English, Vietnamese, and Japanese, is released in this task. UIT-EVJVQA includes question-answer pairs created by humans on a set of images taken in Vietnam, with the answer created from the input question and the corresponding image. EVJVQA consists of 33,000+ question-answer pairs for evaluating the mQA models.

Introducing paper: EVJVQA Challenge: Multilingual Visual Question Answering