SlowGuess/NeurIPS_2023_OCR huggingface dataset:
(b) Who created this dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)? We prefer to stay anonymous in this submission. We refer to Ego4D [43] and EgoTracks [86] for information on dataset collection and annotations of bounding boxes. We will release this information upon paper acceptance.
(c) Who funded the creation of the dataset? (If there is an associated grant, please provide the name of the grantor and the grant name and number.) We prefer to stay anonymous in this submission. We will release this information upon paper acceptance.
(d) Any other comments? None.
Each instance is a video; our annotations are labeling object tracklets from EgoTracks with a static vs. non-static label.
There are 5708 instances in total.
The dataset does not contain all videos in Ego4D. Instead, we only have videos that are present in the Ego4D VQ benchmark [43]. Since these videos are the same, we share the same human and geographic bias and limitations in video selection.
Each object tracklet in a video is labeled as "static" or not.
Yes, see answer above.
No, we do not remove any information for individual instances already present in Ego4D [43].
No. Since videos in Ego4D can be hours long, we follow the same procedure to split a video into multiple shorter video clips [43]. Some videos clips may may have been captured by the same individual, but we do not expose this information in the dataset.
(h) Are there recommended data splits (e.g., training, development/Validation, testing)? (If so, please provide a description of these splits, explaining the rationale behind them.) No. The benchmark is mainly designed for evaluation. However, given the large-scale of this benchmark, one may leverage this to train models. We leave this as promising future works.
(i) Are there any errors, sources of noise, or redundancies in the dataset? (If so, please provide a description.) Since the labels are annotated by human raters, they are prone to human bias and errors. We applied quality assurance procedures to minimize such errors where we can.
The underlying videos are sourced from [43]. The [43] dataset is maintained by Ego4D consortium which can be regarded as guaranteed to exist and remain constant. The license can be found https://ego4d-data.org/pdfs/Ego4D-Licensees-Draft.pdf.
No. Ego4D [43] was collected with careful consideration of ethics and consent. We largely inherit these characteristics. The additional attribute ("static") annotations of our dataset do not reveal any confidential information.
We share the same underlying videos as [43] and objects as [86], so we inherit its privacy and ethics standards, as well as any potential risks. The additional attribute annotations of our dataset do not add any additional objectionable content.
Yes. The annotations we provide do not contain any information relating to people; however, they are labeled from Ego4D [43] videos, which do contain people, and indirectly are related to people due to its egocentric nature.
EgoStatic does not identify subpopulations, but the underlying dataset Ego4D [43] does. See Appendix C of [43] for more details.
EgoStatic does not contain tracks of people, focusing instead on objects. Ego4D [43] does contain some visually identifiable individuals, who provided their consent to appear in the dataset. Other individuals have their faces blurred.
EgoStatic does not add any labels for people, focusing instead on static objects. Ego4D [43] does contain demographics information. See (n) above.
The attribute annotation for each object track was acquired by human annotators. They were instructed to label a track as "static" or not.
We use the proprietary annotation software to collect "static" attribute annotations. The software shows a video frame by frame, and the annotator is able to select from a dropdown menu whether the object is "static". The software will then record the response for each object track.
We used the entirety of Ego4D's Visual Queries benchmark [43] and object tracks from EgoTracks [86] as the basis of EgoStatic.
The dataset was created in winter and spring of 2023, which is not the time the videos were collected.
Yes. The annotations we provide in EgoStatic do not contain any information relating to people; however, they are labeled from Ego4D [43] videos, which do contain people, and indirectly are related to people due to its egocentric nature.
Ego4D [43] collected the data from the individual directly. We do not collect any additional videos beyond those in Ego4D.
(i) Were the individuals in question notified about the data collection? (If so, please describe (or show with screenshots or other information) how notice was provided, and
provide a link or other access point to, or otherwise reproduce, the exact language of the notification itself.)
Ego4D [43] was collected by willing and consenting individuals. See Section 3.4 in [43].
(k) If consent was obtained, were the consenting individuals provided with a mechanism to revoke their consent in the future or for certain uses? (If so, please provide a description, as well as a link or other access point to the mechanism (if appropriate).) Yes, see Section 3.4 in [43].
(1) Has an analysis of the potential impact of the dataset and its use on data subjects (e.g., a data protection impact analysis) been conducted? (If so, please provide a description of this analysis, including the outcomes, as well as a link or other access point to any supporting documentation.) No.
(m) Any other comments?
(a) Was any preprocessing/cleaning/labeling of the data done (e.g., discretization or bucketing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)? (If so, please provide a description. If not, you may skip the remainder of the questions in this section.) No.
(b) Was the "raw" data saved in addition to the preprocessed/cleaned/labeled data (e.g., to support unanticipated future uses)? (If so, please provide a link or other access point to the "raw" data.) No.
(c) Is the software used to preprocess/clean/label the instances available? (If so, please provide a link or other access point.) No.
(d) Any other comments? None.
(a) Has the dataset been used for any tasks already? (If so, please provide a description.) The dataset has been used for understanding the performance of SLAM methods.
(b) Is there a repository that links to any or all papers or systems that use the dataset? (If so, please provide a link or other access point.) N/A.
(c) What (other) tasks could the dataset be used for? The dataset could possibly be used for training SLAM system that focus on the semantic relationships between objects.
(d) Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? (For example, is there anything that a future user might need to know to avoid uses that could result in unfair treatment of individuals or groups (e.g., stereotyping, quality of service issues) or other undesirable harms (e.g., financial harms, legal risks) If so, please provide a description. Is there anything a future user could do to mitigate these undesirable harms?)
The selection of objects is inherited from the Ego4D visual-query benchmark and EgoTracks, which is biased towards objects with appearances of at least modest duration. Consequently, our benchmark may contain less instances that only appear very briefly in the video.
(e) Are there tasks for which the dataset should not be used? (If so, please provide a description.) We currently do not foresee anything such tasks.
(f) Any other comments? None.
(a) Will the dataset be distributed to third parties outside of the entity (e.g., company, institution, organization) on behalf of which the dataset was created? (If so, please provide a description.) Yes, we will share the same license and distribution as [43].
(b) How will the dataset will be distributed (e.g., tarball on website, API, GitHub)? (Does the dataset have a digital object identifier (DOI)?) The dataset will be distributed via GitHub.
(c) When will the dataset be distributed? Upon acceptance of this work.
(d) Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)? (If so, please describe this license and/or ToU, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms or ToU, as well as any fees associated with these restrictions.) The dataset will share the same license as Ego4D. Please see https://ego4d-data.org/pdfs/Ego4D-Licensees-Draft.pdf.
(e) Have any third parties imposed IP-based or other restrictions on the data associated with the instances? (If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms, as well as any fees associated with these restrictions.) Not to our knowledge.
(f) Do any export controls or other regulatory restrictions apply to the dataset or to individual instances? (If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any supporting documentation.) Not to our knowledge.
(g) Any other comments? None.
(a) Who is supporting/hosting/maintaining the dataset?
All authors will maintain the dataset.
(b) How can the owner/curator/manager of the dataset be contacted (e.g., email address)? Authors can be contacted via emails.
(c) Is there an erratum? (If so, please provide a link or other access point.) Not currently. Future versions of the dataset may be released if we find errors, which will be provided within the same GitHub.
(d) Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)? (If so, please describe how often, by whom, and how updates will be communicated to users (e.g., mailing list, GitHub)?)
(e) If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances (e.g., were individuals in question told that their data would be retained for a fixed period of time and then deleted)? (If so, please describe these limits and explain how they will be enforced.) N/A. The dataset is not related to people.
(f) Will older versions of the dataset continue to be supported/hosted/maintained? (If so, please describe how. If not, please describe how its obsolescence will be communicated to users.)
(g) If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so? (If so, please provide a description. Will these contributions be validated/verified? If so, please describe how. If not, why not? Is there a process for communicating/distributing these contributions to other users? If so, please provide a description.) Errors/features can be submitted as issues/pull requests on GitHub.
(h) Any other comments? None.