11 datasets found

Groups: Visual Question Answering Formats: JSON

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  • Image Captioning and Visual Question Answering

    The dataset is used for image captioning and visual question answering.

  • High Quality Image Text Pairs

    The High Quality Image Text Pairs (HQITP-134M) dataset consists of 134 million diverse and high-quality images paired with descriptive captions and titles.

  • Winoground

    The Winoground dataset consists of 400 items, each containing two image-caption pairs (I0, C0), (I1, C1).

  • Conceptual Captions 12M

    The Conceptual Captions 12M (CC-12M) dataset consists of 12 million diverse and high-quality images paired with descriptive captions and titles.

  • Conceptual Captions

    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.

  • LLaVA-1.5

    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...

  • Amazon Berkeley Objects Dataset (ABO)

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

  • Visual Genome

    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.

  • MS-COCO

    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...

  • Microsoft COCO

    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...

  • MSCOCO

    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...