The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.
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The ActivityNet-QA dataset contains 58,000 human-annotated QA pairs on 5,800 videos derived from the popular ActivityNet dataset. The dataset provides a benchmark for testing the performance of VideoQA models on long-term spatio-temporal reasoning.
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NExT-QA is a VideoQA benchmark targeting the explanation of video contents. It challenges QA models to reason about the causal and temporal actions and understand the rich object interactions in daily activities. It supports both multi-choice and open-ended QA tasks. The videos are untrimmed and the questions usually invoke local video contents for answers.
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The TGIF-QA dataset contains 165K QA pairs for the animated GIFs from the TGIF dataset [Li et al. CVPR 2016]. The question & answer pairs are collected via crowdsourcing with a carefully designed user interface to ensure quality. The dataset can be used to evaluate video-based Visual Question Answering techniques.
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COCO-QA is a dataset for visual question answering. It consists of:
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The Tumblr GIF (TGIF) dataset contains 100K animated GIFs and 120K sentences describing visual content of the animated GIFs. The animated GIFs have been collected from Tumblr, from randomly selected posts published between May and June of 2015. The dataset provides the URLs of animated GIFs. The sentences are collected via crowdsourcing, with a carefully designed annotation interface that ensures high quality dataset. There is one sentence per animated GIF for the training and validation splits, and three sentences per GIF for the test split. The dataset can be used to evaluate animated GIF/video description techniques.
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TVQA+ contains 310.8K bounding boxes, linking depicted objects to visual concepts in questions and answers.
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The large-scale MUSIC-AVQA dataset of musical performance contains 45,867 question-answer pairs, distributed in 9,288 videos for over 150 hours. All QA pairs types are divided into 3 modal scenarios, which contain 9 question types and 33 question templates. Finally, as an open-ended problem of our AVQA tasks, all 42 kinds of answers constitute a set for selection.
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SQA3D is a dataset for embodied scene understanding, where an agent needs to comprehend the scene it situates from an first person's perspective and answer questions. The questions are designed to be situated, embodied and knowledge-intensive. We offer three different modalities to represent a 3D scene: 3D scan, egocentric video and BEV picture.
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SUTD-TrafficQA (Singapore University of Technology and Design - Traffic Question Answering) is a dataset which takes the form of video QA based on 10,080 in-the-wild videos and annotated 62,535 QA pairs, for benchmarking the cognitive capability of causal inference and event understanding models in complex traffic scenarios. Specifically, the dataset proposes 6 challenging reasoning tasks corresponding to various traffic scenarios, so as to evaluate the reasoning capability over different kinds of complex yet practical traffic events.
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TV show Caption is a large-scale multimodal captioning dataset, containing 261,490 caption descriptions paired with 108,965 short video moments. TVC is unique as its captions may also describe dialogues/subtitles while the captions in the other datasets are only describing the visual content.
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The DramaQA focuses on two perspectives: 1) Hierarchical QAs as an evaluation metric based on the cognitive developmental stages of human intelligence. 2) Character-centered video annotations to model local coherence of the story. The dataset is built upon the TV drama "Another Miss Oh" and it contains 17,983 QA pairs from 23,928 various length video clips, with each QA pair belonging to one of four difficulty levels.
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Amazon Mechanical Turk (AMT) is used to collect annotations on HowTo100M videos. 30k 60-second clips are randomly sampled from 9,421 videos and present each clip to the turkers, who are asked to select a video segment containing a single, self-contained scene. After this segment selection step, another group of workers are asked to write descriptions for each displayed segment. Narrations are not provided to the workers to ensure that their written queries are based on visual content only. These final video segments are 10-20 seconds long on average, and the length of queries ranges from 8 to 20 words. From this process, 51,390 queries are collected for 24k 60-second clips from 9,371 videos in HowTo100M, on average 2-3 queries per clip. The video clips and its associated queries are split into 80% train, 10% val and 10% test.
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EgoTask QA benchmark contains 40K balanced question-answer pairs selected from 368K programmatically generated questions generated over 2K egocentric videos. It provides a single home for the crucial dimensions of task understanding through question-answering on real-world egocentric videos.
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KnowIT VQA is a video dataset with 24,282 human-generated question-answer pairs about The Big Bang Theory. The dataset combines visual, textual and temporal coherence reasoning together with knowledge-based questions, which need of the experience obtained from the viewing of the series to be answered.
A quantitative benchmark for developing and understanding video of fill-in-the-blank question-answering dataset with over 300,000 examples, based on descriptive video annotations for the visually impaired.
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Contains ~9K videos of human agents performing various actions, annotated with 3 types of commonsense descriptions.
NExT-QA is a VideoQA benchmark targeting the explanation of video contents. It challenges QA models to reason about the causal and temporal actions and understand the rich object interactions in daily activities. This page records LLMs for answer evaluation.
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Perception Test is a benchmark designed to evaluate the perception and reasoning skills of multimodal models. It introduces real-world videos designed to show perceptually interesting situations and defines multiple tasks that require understanding of memory, abstract patterns, physics, and semantics – across visual, audio, and text modalities. The benchmark consists of 11.6k videos, 23s average length, filmed by around 100 participants worldwide. The videos are densely annotated with six types of labels: object and point tracks, temporal action and sound segments, multiple-choice video question-answers and grounded video question-answers. The benchmark probes pre-trained models for their transfer capabilities, in a zero-shot / few-shot or fine tuning regime.
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TutorialVQA is a new type of dataset used to find answer spans in tutorial videos. The dataset includes about 6,000 triples, comprised of videos, questions, and answer spans manually collected from screencast tutorial videos with spoken narratives for a photo-editing software.
The VideoNavQA dataset contains pairs of questions and videos generated in the House3D environment. The goal of this dataset is to assess question-answering performance from nearly-ideal navigation paths, while considering a much more complete variety of questions than current instantiations of the Embodied Question Answering (EQA) task.
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