58 datasets found

The RSVQAxBEN dataset is a large-scale benchmark dataset for remote sensing visual question answering, based on the BigEarthNet (BEN) archive and containing 590,326 Sentinel-2...

The RSVQA-LR dataset is a large-scale benchmark dataset for remote sensing visual question answering, constructed using 7 Sentinel-2 tiles acquired over the Netherlands, from...

The dataset used in the paper "Scaling Laws of Synthetic Images for Model Training". The dataset is used for supervised image classification and zero-shot classification tasks.

The dataset used in the paper is a set of sequential vision-and-language tasks, where each task consists of an image and a text input.

Measuring machine intelligence through visual question answering.

Visual Question Answering (VQA) has emerged as a prominent multi-discipline research problem in both academia and industry.

A number of recent works have proposed attention models for Visual Question Answering (VQA) that generate spatial maps highlighting image regions relevant to answering the...

The LXMERT dataset is used for visual question answering task. It uses pre-trained weights provided by Tan and Bansal (2019) and fine-tunes it with adaptive approaches mentioned...

The VQA 2.0 dataset is used for visual question answering task. It consists of three sets with a train set containing 83k images and 444k questions, a validation set containing...

The dataset used in this paper is a multimodal large language model (LLaMA) dataset, specifically LLaVA-1.5, which consists of 7 billion parameters and is used for multimodal...

The SBU Captions dataset is a large-scale image-text dataset used for vision-language pre-training.

The Amazon Berkeley Objects Dataset (ABO) is a public available e-commerce dataset with multiple images per product.

CLEVR images contain objects characterized by a set of attributes (shape, color, size and material). The questions are grouped into 5 categories: Exist, Count, CompareInteger,...

The Visual Genome dataset is a large-scale visual question answering dataset, containing 1.5 million images, each with 15-30 annotated entities, attributes, and relationships.

Large scale datasets [18, 17, 27, 6] boosted text conditional image generation quality. However, in some domains it could be difficult to make such datasets and usually it could...

The COCO-QA dataset is used for visual question answering task. It consists of 123,287 images and 78,736 train and 38,948 test questions.

The Microsoft COCO dataset was used for training and evaluating the CNNs because it has become a standard benchmark for testing algorithms aimed at scene understanding and...

Human Pose Estimation (HPE) aims to estimate the position of each joint point of the human body in a given image. HPE tasks support a wide range of downstream tasks such as...