# Datasets: HuggingFaceM4 /something_something_v2

Other
Languages: English
Multilinguality: monolingual
Size Categories: 100K<n<1M
Language Creators: crowdsourced
Annotations Creators: crowdsourced
Source Datasets: original
ArXiv:
 --- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - other multilinguality: - monolingual size_categories: - 100K", "text": "moving drawer of night stand", "label": 33, "placeholders": ["drawer", "night stand"]} }  ### Data Fields - video_id: str Unique identifier for each video. - video: str File object - placeholders: List[str] Objects present in the video - text: str Description of what is happening in the video - labels: int Action found in the video. Indices from 0 to 173.
### Data Splits | |train |validation| test | |-------------|------:|---------:|------:| |# of examples|168913|24777 |27157 | ## Dataset Creation ### Curation Rationale From the paper: > Neural networks trained on datasets such as ImageNet have led to major advances in visual object classification. One obstacle that prevents networks from reasoning more deeply about complex scenes and situations, and from integrating visual knowledge with natural language, like humans do, is their lack of common sense knowledge about the physical world. Videos, unlike still images, contain a wealth of detailed information about the physical world. However, most labelled video datasets represent high-level concepts rather than detailed physical aspects about actions and scenes. In this work, we describe our ongoing collection of the “something-something” database of video prediction tasks whose solutions require a common sense understanding of the depicted situation ### Source Data #### Initial Data Collection and Normalization From the paper: > As outlined is Section 3 videos available online are largely unsuitable for the goal of learning simple (but finegrained) visual concepts. We therefore ask crowd-workers to provide videos given labels instead of the other way around. #### Who are the source language producers? The dataset authors ### Annotations #### Annotation process The label is given first and then the video is collected by an AMT worker. More fine-grained details on the process are in the Section 4 of the work. #### Who are the annotators? AMT workers ### Personal and Sensitive Information Nothing specifically discussed in the paper. ## Considerations for Using the Data ### Social Impact of Dataset The dataset is useful for action recognition pretraining due to diverse set of actions that happen in it. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators ### Licensing Information License is a one-page document as defined by QualComm. Please read the license document in detail before using this dataset [here](https://developer.qualcomm.com/downloads/data-license-agreement-research-use?referrer=node/68935). ### Citation Information bibtex @inproceedings{goyal2017something, title={The" something something" video database for learning and evaluating visual common sense}, author={Goyal, Raghav and Ebrahimi Kahou, Samira and Michalski, Vincent and Materzynska, Joanna and Westphal, Susanne and Kim, Heuna and Haenel, Valentin and Fruend, Ingo and Yianilos, Peter and Mueller-Freitag, Moritz and others}, booktitle={Proceedings of the IEEE international conference on computer vision}, pages={5842--5850}, year={2017} } ` ### Contributions Thanks to [@apsdehal](https://github.com/apsdehal) for adding this dataset.