32 datasets found

The dataset for the SMART-101 challenge consists of 101 unique puzzles that require a mix of several elementary skills, including arithmetic, algebra, and spatial reasoning,...

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

The OK-VQA dataset is a visual question answering benchmark requiring external knowledge.

The Visual7W dataset for Visual Question Answering (VQA). The dataset contains 7,000 images with 7,000 queries.

Visual question answering (VQA) demands simultaneous comprehension of both the image visual content and natural language questions. In some cases, the reasoning needs the help...

The dataset used in the paper is a collection of images and corresponding referring expressions.

VQAvs is a dataset for visual question answering, containing questions answerable using visual images.

VQA-CPv1 and VQA-CPv2 are datasets for visual question answering, containing questions answerable using visual images.

Visual question answering is a multimodal task that requires the joint comprehension of visual and textual information. The proposed approach utilizes object attributes to...

A dataset for visual spatial relationship classification (VSRC) with nine well-defined spatial relations.

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

The Multimodal Visual Patterns (MMVP) benchmark is a dataset used to evaluate the visual question answering capabilities of multimodal large language models (MLLMs).

The VQA dataset is a large-scale visual question answering dataset that consists of pairs of images that require natural language answers.

The Composite dataset, containing 11,985 human judgments over Flickr 8K, Flickr 30K, and COCO captions.

A collection of datasets for visual question answering, including MovieQA, TVQA, AVSD, EQA, and Embodied QA.

We use the VQA v2.0 dataset for the evaluation of our proposed joint model, where the answers are balanced in order to minimize the effectiveness of learning dataset priors.

The GQA dataset is a visual question answering dataset that characterizes in compositional question answering and visual reasoning about real-world images.

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

The dataset used in the paper is Sort-of-CLEVR, a visual question answering dataset.

The CLEVR dataset is a dataset for visual question answering, where each image is annotated with a question.