Visual Question Answering - Groups

CLEVR-Humans

The CLEVR-Humans dataset consists of 32,164 questions asked by humans, containing words and reasoning steps that were unseen in CLEVR.

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Image Captioning and Visual Question Answering

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

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LLaVA-Instruct-150k

Visual question answering dataset

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VQA-CPv2

The VQA-CPv2 dataset is used to study the robustness of VQA methods against linguistic biases.

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VQA-CP

The VQA-CP dataset is a split of the VQA dataset, designed to test generalization skills across changes in the answer distribution between the training and the test sets.

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SMART-101 dataset

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,...

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CLEVR-CoGenT

The CLEVR-CoGenT dataset is a dataset for visual question answering, where the questions consist on comparing the position of two objects.

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GQA-OOD: Out-of-Domain VQA Benchmark

GQA-OOD is a benchmark dedicated to the out-of-domain VQA evaluation.

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GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question...

GQA is a new dataset for real-world visual reasoning and compositional question answering.

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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.

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OK-VQA

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

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FVQA

FVQA is a fact-based visual question answering dataset, containing 2190 images and 5826 (question, answer) pairs, with supporting facts selected from knowledge bases.

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Visual7W

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

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Mutan: Multimodal Tucker Fusion for Visual Question Answering

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

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Visual ChatGPT

Visual ChatGPT is a system that integrates different Visual Foundation Models to understand visual information and generation corresponding answers.

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Super-CLEVR

The Super-CLEVR dataset contains synthetic scenes of randomly placed vehicles from 5 categories (car, plane, bicycle, motorbike, bus) with various attributes (color, material,...

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Super-CLEVR-3D

The Super-CLEVR-3D dataset contains questions explicitly querying 3D understanding including object parts, 3D poses, and occlusions.

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VQAvs

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

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VQA-CPv1 and VQA-CPv2

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

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Object Attribute Matters in Visual Question Answering

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

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58 datasets found