CoChat
Sign Up
Cited by 79Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot LearningMultimodal Machine Learning Applications

PACO: Parts and Attributes of Common Objects

Open access
Ramanathan, Vignesh, Kalia, Anmol, Petrovic, Vladan, Wen, Yi, Zheng, Baixue, Guo, Baishan, Wang, Rui, Marquez, Aaron +6 more·2023·2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
DOI 10.1109/cvpr52729.2023.00690
Open this paper in CoChat — free accountOpen PDF

Abstract

Object models are gradually progressing from predicting just category labels to providing detailed descriptions of object instances. This motivates the need for large datasets which go beyond traditional object masks and provide richer annotations such as part masks and attributes. Hence, we introduce PACO: Parts and Attributes of Common Objects. It spans 75 object categories, 456 object-part categories and 55 attributes across image (LVIS) and video (Eg04D) datasets. We provide 641K part masks an-notated across 260K object boxes, with roughly half of them exhaustively annotated with attributes as well. We design evaluation metrics and provide benchmark results for three tasks on the dataset: part mask segmentation, object and part attribute prediction and zero-shot instance detection. Dataset, models, and code are open-sourced at https://github.com/jacebookresearch/paco.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/cvpr52729.2023.00690

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Gemini 2.0 significantly outperforms state-of-the-art VLMs like GPT and Claude on pointing benchmarks.

    “Gemini 2.0 significantly outperforms state-of-the-art vision-language models (VLMs) like GPT and Claude.”

    From Gemini Robotics: Bringing AI into the Physical World (2025) · cites this paper as Ramanathan et al., 2023

    The zero-shot instance detection results (Section 5.3, p. 8; Appendix H, p. 18) compare PACO detection models, not Gemini 2.0 with GPT or Claude. Searches for Gemini 2.0 and for GPT, Claude, and pointing benchmarks found no passage in our copy that addresses the claimed comparison. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 46

  1. [2][2] Mathilde Caron, Hugo Touvron, Ishan Misra, Herv´ e J´ egou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the International Conference on Computer Vision (ICCV) , 2021. 13 -
  2. [3][3] Xianjie Chen, Roozbeh Mottaghi, Xiaobai Liu, Sanja Fidler, Raquel Urtasun, and Alan Yuille. Detect what you can: Detecting and representing objects using holistic models and body parts. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 1971-1978, 2014. 1, 2 -
  3. [4][4] Zhenfang Chen, Peng Wang, Lin Ma, Kwan-Yee K. Wong, and Qi Wu. Cops-ref: A new dataset and task on compositional referring expression comprehension. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2020. 1, 3, 6 -
  4. [5][5] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition , pages 248-255. Ieee, 2009. 2 -
  5. [6][6] Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. Arcface: Additive angular margin loss for deep face recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 4690-4699, 2019. 18 -
  6. [7][7] Li Deng. The mnist database of handwritten digit images for machine learning research [best of the web]. IEEE signal processing magazine , 29(6):141-142, 2012. 2 -
  7. [8][8] N. Dinesh Reddy, Minh Vo, and Srinivasa G. Narasimhan. Carfusion: Combining point tracking and part detection for dynamic 3d reconstruction of vehicles. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2018. 2 -
  8. [10][10] Chengjian Feng, Yujie Zhong, Zequn Jie, Xiangxiang Chu, Haibing Ren, Xiaolin Wei, Weidi Xie, and Lin Ma. Promptdet: Expand your detector vocabulary with uncurated images. arXiv preprint arXiv:2203.16513 , 2022. 1 - · arXiv
  9. [11][11] Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, TsungYi Lin, Ekin D Cubuk, Quoc V Le, and Barret Zoph. Simple copy-paste is a strong data augmentation method for instance segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 29182928, 2021. 7 -
  10. [12][12] Ke Gong, Xiaodan Liang, Dongyu Zhang, Xiaohui Shen, and Liang Lin. Look into person: Self-supervised structuresensitive learning and a new benchmark for human parsing. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 932-940, 2017. 2 -
  11. [13][13] Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, et al. Ego4d: Around the world in 3,000 hours of egocentric video. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 18995-19012, 2022. 2, 3 -
  12. [14][14] Sheng Guo, Weilin Huang, Xiao Zhang, Prasanna Srikhanta, Yin Cui, Yuan Li, Hartwig Adam, Matthew R Scott, and Serge Belongie. The imaterialist fashion attribute dataset. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops , pages 0-0, 2019. 2 -
  13. [16][16] Akshita Gupta, Sanath Narayan, KJ Joseph, Salman Khan, Fahad Shahbaz Khan, and Mubarak Shah. Ow-detr: Open-world detection transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 9235-9244, 2022. 1 -
  14. [17][17] Ju He, Shuo Yang, Shaokang Yang, Adam Kortylewski, Xiaoding Yuan, Jie-Neng Chen, Shuai Liu, Cheng Yang, and Alan Yuille. Partimagenet: A large, high-quality dataset of parts. arXiv preprint arXiv:2112.00933 , 2021. 1, 2 - · arXiv
  15. [18][18] Kaiming He, Georgia Gkioxari, Piotr Doll´ ar, and Ross Girshick. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision , pages 2961-2969, 2017. 2, 18 -
  16. [19][19] Drew A Hudson and Christopher D Manning. Gqa: A new dataset for real-world visual reasoning and compositional question answering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 6700-6709, 2019. 1, 3 -
  17. [20][20] Menglin Jia, Mengyun Shi, Mikhail Sirotenko, Yin Cui, Claire Cardie, Bharath Hariharan, Hartwig Adam, and Serge Belongie. Fashionpedia: Ontology, segmentation, and an attribute localization dataset. In European conference on computer vision , pages 316-332. Springer, 2020. 1, 2, 3 -
  18. [21][21] Licheng Jiao, Ruohan Zhang, Fang Liu, Shuyuan Yang, Biao Hou, Lingling Li, and Xu Tang. New generation deep learning for video object detection: A survey. IEEE Transactions on Neural Networks and Learning Systems , 2021. 2 -
  19. [22][22] Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. Mdetrmodulated detection for end-to-end multi-modal understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 1780-1790, 2021. 1, 8, 17 -
  20. [23][23] Sahar Kazemzadeh, Vicente Ordonez, Mark Matten, and Tamara Berg. Referitgame: Referring to objects in photographs of natural scenes. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) , pages 787-798, 2014. 1, 3, 6 -
  21. [24][24] Ronak Kosti, Jose M Alvarez, Adria Recasens, and Agata Lapedriza. Emotion recognition in context. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 1667-1675, 2017. 2 -
  22. [26][26] Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Alexander Kolesnikov, et al. The open images dataset v4. International Journal of Computer Vision , 128(7):1956-1981, 2020. 2 -
  23. [27][27] Dangwei Li, Zhang Zhang, Xiaotang Chen, Haibin Ling, and Kaiqi Huang. A richly annotated dataset for pedestrian attribute recognition. arXiv preprint arXiv:1603.07054 , 2016. 1 - · arXiv
  24. [28][28] Jianshu Li, Jian Zhao, Yunchao Wei, Congyan Lang, Yidong Li, Terence Sim, Shuicheng Yan, and Jiashi Feng. Multiplehuman parsing in the wild. arXiv preprint arXiv:1705.07206 , 2017. 2 - · arXiv
  25. [29][29] Yining Li, Chen Huang, Chen Change Loy, and Xiaoou Tang. Human attribute recognition by deep hierarchical contexts. In European conference on computer vision , pages 684-700. Springer, 2016. 2 -
  26. [30][30] Yanghao Li, Hanzi Mao, Ross B. Girshick, and Kaiming He. Exploring plain vision transformer backbones for object detection. ArXiv , abs/2203.16527, 2022. 2, 7, 14 - · arXiv
  27. [32][32] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll´ ar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision , pages 740-755. Springer, 2014. 2 -
  28. [33][33] Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, and Xiaoou Tang. Deepfashion: Powering robust clothes recognition and retrieval with rich annotations. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 1096-1104, 2016. 2 -
  29. [34][34] Junhua Mao, Jonathan Huang, Alexander Toshev, Oana Camburu, Alan L Yuille, and Kevin Murphy. Generation 14. and comprehension of unambiguous object descriptions. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 11-20, 2016. 1, 3, 6 -
  30. [35][35] Panagiotis Meletis, Xiaoxiao Wen, Chenyang Lu, Daan de Geus, and Gijs Dubbelman. Cityscapes-panoptic-parts and pascal-panoptic-parts datasets for scene understanding. arXiv preprint arXiv:2004.07944 , 2020. 2 - · arXiv
  31. [36][36] Genevieve Patterson and James Hays. Coco attributes: Attributes for people, animals, and objects. European Conference on Computer Vision , 2016. 2 -
  32. [37][37] Khoi Pham, Kushal Kafle, Zhe Lin, Zhihong Ding, Scott Cohen, Quan Tran, and Abhinav Shrivastava. Learning to predict visual attributes in the wild. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 13018-13028, 2021. 1, 3 -
  33. [38][38] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems , 28, 2015. 19 -
  34. [39][39] Bernardino Romera-Paredes and Philip Torr. An embarrassingly simple approach to zero-shot learning. In International conference on machine learning , pages 2152-2161. PMLR, 2015. 3 -
  35. [41][41] Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela. Flava: A foundational language and vision alignment model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 15638-15650, 2022. 7 -
  36. [42][42] Amanpreet Singh, Guan Pang, Mandy Toh, Jing Huang, Wojciech Galuba, and Tal Hassner. Textocr: Towards large-scale end-to-end reasoning for arbitrary-shaped scene text. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 8802-8812, 2021. 2 -
  37. [43][43] Xibin Song, Peng Wang, Dingfu Zhou, Rui Zhu, Chenye Guan, Yuchao Dai, Hao Su, Hongdong Li, and Ruigang Yang. Apollocar3d: A large 3d car instance understanding benchmark for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 5452-5462, 2019. 2 -
  38. [44][44] Andreas Veit, Tomas Matera, Lukas Neumann, Jiri Matas, and Serge Belongie. Coco-text: Dataset and benchmark for text detection and recognition in natural images. arXiv preprint arXiv:1601.07140 , 2016. 2, 4, 5, 6, 7 - · arXiv
  39. [46][46] Chenyun Wu, Zhe Lin, Scott Cohen, Trung Bui, and Subhransu Maji. Phrasecut: Language-based image segmentation in the wild. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 10216-10225, 2020. 3 -
  40. [48][48] Yongqin Xian, Bernt Schiele, and Zeynep Akata. Zero-shot learning-the good, the bad and the ugly. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 4582-4591, 2017. 3 -
  41. [49][49] Ke Yan, Youbao Tang, Yifan Peng, Veit Sandfort, Mohammadhadi Bagheri, Zhiyong Lu, and Ronald M Summers. Mulan: multitask universal lesion analysis network for joint lesion detection, tagging, and segmentation. In International Conference on Medical Image Computing and ComputerAssisted Intervention , pages 194-202. Springer, 2019. 2 -
  42. [50][50] Lu Yang, Qing Song, Zhihui Wang, and Ming Jiang. Parsing r-cnn for instance-level human analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 364-373, 2019. 2 -
  43. [51][51] Shuai Zheng, Fan Yang, M Hadi Kiapour, and Robinson Piramuthu. Modanet: A large-scale street fashion dataset with polygon annotations. In Proceedings of the 26th ACM international conference on Multimedia , pages 1670-1678, 2018. 1, 2 -
  44. [52][52] Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba. Places: A 10 million image database for scene recognition. IEEE transactions on pattern analysis and machine intelligence , 40(6):1452-1464, 2017. 2 -
  45. [53][53] Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Scene parsing through ade20k dataset. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 633-641, 2017. 2 -
  46. [54][54] Bolei Zhou, Hang Zhao, Xavier Puig, Tete Xiao, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Semantic understanding of scenes through the ade20k dataset. International Journal of Computer Vision , 127(3):302-321, 2019. 1, 2 -

Cited by

Sorted by citations

Authors
Ramanathan, V., Kalia, A., Petrovic, V., Wen, Y., Zheng, B., Guo, B., Wang, R., Marquez, A., Kovvuri, R., Kadian, A., Mousavi, A., Song, Y., Dubey, A., & Mahajan, D.
Affiliations
  • Meta (United States) (US)
  • Meta AI
  • Simon Fraser University (CA)
ORCID
Venue
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2023-06
Pages
7141-7151
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr52729.2023.00690
OpenAlex
W4386071509
Cited by
79
References
8
Citations by year
2026: 19 · 2025: 24 · 2024: 32 · 2023: 4
FWCI
8.04
Type
conference paper
Language
en
Open access
Licence
Locations
  • IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, public-domain)
  • arXiv (Cornell University) (public-domain)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
Keywords
object properties, part segmentation, attribute prediction, lymphovascular invasion, benchmark dataset

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review