CVPR/CVPR2024-papers dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset is the official release for the 4th Anti-UAV Challenge.

• Train
  • Track 1 & Track 2: Google Drive | Baidu
  • Track 3: Google Drive | Baidu
• Test
  • Track 1: Google Drive | Baidu
  • Track 2: Google Drive | Baidu
  • Track 3: Google Drive | Baidu

Dataset Card for VR-Folding Dataset

  Dataset Summary

This is the VR-Folding dataset created by the CVPR 2023 paper GarmentTracking: Category-Level Garment Pose Tracking. This dataset is recorded with a system called VR-Garment, which is a garment-hand interaction environment based on Unity. To download the dataset, use the following shell snippet: git lfs install git clone https://huggingface.co/datasets/robotflow/garment-tracking

if you want to clone without large… See the full description on the dataset page: https://huggingface.co/datasets/robotflow/vr-folding.

HoHo 5k Subset

This dataset is being used as the training set for the S23DR Challenge. This is a living dataset. Today, we provide 4316 samples for training, and 175 for validation and hold back an additional 1072 for computing the private and public leaderboards. Additional, we intend to continue releasing training data throughout the challenge and beyond. The data take the following form: Features({ "order_id": Value(dtype="string"), # inputs "K":… See the full description on the dataset page: https://huggingface.co/datasets/usm3d/hoho-train-set.

Dataset Card

Number of samples: 25196 Columns / Features:

order_id: Value(dtype='string', id=None) image_ids: Sequence(feature=Value(dtype='string', id=None), length=-1, id=None) ade: Sequence(feature=Image(mode=None, decode=True, id=None), length=-1, id=None) depth: Sequence(feature=Image(mode=None, decode=True, id=None), length=-1, id=None) gestalt: Sequence(feature=Image(mode=None, decode=True, id=None), length=-1, id=None) K: Sequence(feature=Array2D(shape=(3, 3)… See the full description on the dataset page: https://huggingface.co/datasets/usm3d/hoho25k.

HEVC-SVS Datasets

Proposed HEVC feature sets along with CNN features from GoogleNet, AlexNet, Inception-ResNet-V2, and VGG16 for TVSum, SumMe, OVP and VSUMM datasets. The new modified datasets names are "HEVC-SVS-TVSum", "HEVC-SVS-SumMe", "HEVC-SVS-OVP" and "HEVC-SVS-VSUMM", respectively.

The datasets contain the original ground truth data they came with, and these stayed unmodified.

Upon using any of these datasets, please do cite our publication where we proposed the HEVC feature set for the first time:

If you are using (HEVC-SVS-OVP) and/or (HEVC-SVS-VSUMM) datasets:

@article{issa_cnn_2022, title = {{CNN} and {HEVC} {Video} {Coding} {Features} for {Static} {Video} {Summarization}}, volume = {10}, copyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)}, issn = {2169-3536}, url = {https://ieeexplore.ieee.org/document/9815254/}, doi = {10.1109/ACCESS.2022.3188638}, urldate = {2022-09-29}, journal = {IEEE Access}, author = {Issa, Obada and Shanableh, Tamer}, year = {2022}, pages = {72080--72091}, }

If you are using (HEVC-SVS-TVSum) and/or (HEVC-SVS-SumMe) datasets:

{ PENDING }

Make sure to also cite the original authors for each of the datasets:

TVSum:

@INPROCEEDINGS{7299154, author = {Yale Song and Vallmitjana, Jordi and Stent, Amanda and Jaimes, Alejandro}, booktitle = {2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, title = {TVSum: Summarizing web videos using titles}, year = {2015}, volume = {}, number = {}, pages = {5179-5187}, doi = {10.1109/CVPR.2015.7299154} }

SumMe:

@inproceedings{GygliECCV14, author ={Gygli, Michael and Grabner, Helmut and Riemenschneider, Hayko and Van Gool, Luc}, title = {Creating Summaries from User Videos}, booktitle = {ECCV}, year = {2014} }

OVP and VSUMM:

@article{Avila, title = "VSUMM: A mechanism designed to produce static video summaries and a novel evaluation method", journal = "Pattern Recognition Letters", volume = "32", number = "1", pages = "56 - 68", year = "2011", note = "ce:titleImage Processing, Computer Vision and Pattern Recognition in Latin America/ce:title", issn = "0167-8655", doi = "10.1016/j.patrec.2010.08.004", author = "Sandra Eliza Fontes de Avila and Ana Paula Brand„o Lopes and Antonio da Luz Jr. and Arnaldo de Albuquerque Ara˙jo", }

Acknowledgement: The work in this repository is supported by the American University of Sharjah under research grant number FRG22-E-E44. This work represents the opinions of the author(s) and does not mean to represent the position or opinions of the American University of Sharjah.

Link to original evaluation code for: https://github.com/hendrycks/natural-adv-examples @article{hendrycks2021nae, title={Natural Adversarial Examples}, author={Dan Hendrycks and Kevin Zhao and Steven Basart and Jacob Steinhardt and Dawn Song}, journal={CVPR}, year={2021} }