Nothing Special   »   [go: up one dir, main page]

Skip to main content

Exploiting Unlabeled Data with Vision and Language Models for Object Detection

  • Conference paper
  • First Online:
Computer Vision – ECCV 2022 (ECCV 2022)

Abstract

Building robust and generic object detection frameworks requires scaling to larger label spaces and bigger training datasets. However, it is prohibitively costly to acquire annotations for thousands of categories at a large scale. We propose a novel method that leverages the rich semantics available in recent vision and language models to localize and classify objects in unlabeled images, effectively generating pseudo labels for object detection. Starting with a generic and class-agnostic region proposal mechanism, we use vision and language models to categorize each region of an image into any object category that is required for downstream tasks. We demonstrate the value of the generated pseudo labels in two specific tasks, open-vocabulary detection, where a model needs to generalize to unseen object categories, and semi-supervised object detection, where additional unlabeled images can be used to improve the model. Our empirical evaluation shows the effectiveness of the pseudo labels in both tasks, where we outperform competitive baselines and achieve a novel state-of-the-art for open-vocabulary object detection. Our code is available at https://github.com/xiaofeng94/VL-PLM.

S. Zhao1 and Z. Zhang1—Equal contribution.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Agrawal, A., et al.: VQA: visual question answering. In: ICCV (2015)

    Google Scholar 

  2. Agrawal, H., et al.: nocaps: novel object captioning at scale. In: ICCV (2019)

    Google Scholar 

  3. Anderson, P., et al.: Vision-and-Language navigation: interpreting visually-grounded navigation instructions in real environments. In: CVPR (2018)

    Google Scholar 

  4. Bansal, A., Sikka, K., Sharma, G., Chellappa, R., Divakaran, A.: Zero-shot object detection. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 397–414. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01246-5_24

    Chapter  Google Scholar 

  5. Cai, Z., Vasconcelos, N.: Cascade R-CNN: delving into high quality object detection. In: CVPR (2018)

    Google Scholar 

  6. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213–229. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_13

    Chapter  Google Scholar 

  7. Chen, X., et al.: Microsoft COCO captions: data collection and evaluation server (2015)

    Google Scholar 

  8. Chen, Y.C., et al.: UNITER: UNiversal image-TExt representation learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12375, pp. 104–120. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58577-8_7

    Chapter  Google Scholar 

  9. Das, A., Datta, S., Gkioxari, G., Lee, S., Parikh, D., Batra, D.: Embodied question answering. In: CVPR (2018)

    Google Scholar 

  10. Dong, B., Huang, Z., Guo, Y., Wang, Q., Niu, Z., Zuo, W.: Boosting weakly supervised object detection via learning bounding box adjusters. In: ICCV., pp. 2876–2885 (2021)

    Google Scholar 

  11. Everingham, M., Eslami, S., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes challenge: a retrospective. Int. J. Comput. Vision 111(1), 98–136 (2015)

    Article  Google Scholar 

  12. Fang, H., et al.: From captions to visual concepts and back. In: CVPR (2015)

    Google Scholar 

  13. Fukui, A., et al..: Multimodal compact bilinear pooling for visual question answering and visual grounding. In: EMNLP (2016)

    Google Scholar 

  14. Gao, M., Xing, C., Niebles, J.C., Li, J., Xu, R., Liu, W., Xiong, C.: Towards open vocabulary object detection without human-provided bounding boxes. In: ECCV 2022 (2021)

    Google Scholar 

  15. Ghiasi, G., et al.: : Simple copy-paste is a strong data augmentation method for instance segmentation. In: CVPR, pp. 2918–2928 (2021)

    Google Scholar 

  16. Gu, X., Lin, T.Y., Kuo, W., Cui, Y.: Open-vocabulary object detection via vision and language knowledge distillation. In: ICLR (2022)

    Google Scholar 

  17. Gupta, A., Dollár, P., Girshick, R.: LVIS: a dataset for large vocabulary instance segmentation. In: CVPR (2019)

    Google Scholar 

  18. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: ICCV (2017)

    Google Scholar 

  19. Hu, R., Singh, A.: UniT: multimodal Multitask Learning with a unified transformer. In: ICCV (2021)

    Google Scholar 

  20. Hudson, D.A., Manning, C.D.: Learning by abstraction: the neural state machine. In: NeurIPS (2019)

    Google Scholar 

  21. Huynh, D., Kuen, J., Lin, Z., Gu, J., Elhamifar, E.: Open-vocabulary instance segmentation via robust cross-modal pseudo-labeling (2021)

    Google Scholar 

  22. Inoue, N., Furuta, R., Yamasaki, T., Aizawa, K.: Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation. In: CVPR (2018)

    Google Scholar 

  23. Jia, C., et al.: Scaling up visual and vision-language representation learning with noisy text supervision. In: ICML (D2021)

    Google Scholar 

  24. Kamath, A., Singh, M., LeCun, Y., Synnaeve, G., Misra, I., Carion, N.: MDETR - modulated detection for end-to-end multi-modal understanding. In: ICCV (2021)

    Google Scholar 

  25. Karpathy, A., Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions. In: CVPR (2015)

    Google Scholar 

  26. Kazemzadeh, S., Ordonez, V., Matten, M., Berg, T.: ReferItGame: referring to objects in photographs of natural scenes. In: EMNLP (2014)

    Google Scholar 

  27. Kuznetsova, A., et al.: The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale. Int. J. Comput. Vis, 128, 1956–1981 (2020)

    Google Scholar 

  28. Li, B., Weinberger, K.Q., Belongie, S., Koltun, V., Ranftl, R.: Language-driven semantic segmentation. In: ICLR (2022)

    Google Scholar 

  29. Li, J., Selvaraju, R.R., Gotmare, A.D., Joty, S., Xiong, C., Hoi, S.: Align before fuse: vision and language representation learning with momentum distillation. In: NeurIPS (2021)

    Google Scholar 

  30. Li, X., et al.: Oscar: object-semantics aligned pre-training for vision-language tasks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12375, pp. 121–137. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58577-8_8

    Chapter  Google Scholar 

  31. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR (2017)

    Google Scholar 

  32. Lin, T.Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  33. Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach (2019)

    Google Scholar 

  34. Lu, J., Batra, D., Parikh, D., Lee, S.: ViLBERT: pretraining task-agnostic visiolinguistic representations for Vision-and-Language Tasks. In: NeurIPS (2019)

    Google Scholar 

  35. Mao, J., Huang, J., Toshev, A., Camburu, O., Yuille, A., Murphy, K.: Generation and Comprehension of Unambiguous Object Descriptions. In: CVPR (2016)

    Google Scholar 

  36. Peng, G., et al.: Dynamic fusion with Intra- and inter- modality attention flow for visual question answering. In: CVPR (2019)

    Google Scholar 

  37. Radford, A., et al.: Learning transferable visual models from natural language supervision. In: ICML (2021)

    Google Scholar 

  38. Rahman, S., Khan, S., Barnes, N.: Improved visual-semantic alignment for zero-shot object detection. In: AAAI, pp. 11932–11939 (2020)

    Google Scholar 

  39. Rao, Y., et al.: Denseclip: Language-guided dense prediction with context-aware prompting. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)

    Google Scholar 

  40. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with Region Proposal Networks. In: NeurIPS (2015)

    Google Scholar 

  41. Shao, S., et al.: Objects365: a large-scale. high-quality dataset for object detection. In : 2019 IEEE/CVF International Conference on Computer Vision (2019)

    Google Scholar 

  42. Shi, H., Hayat, M., Wu, Y., Cai, J.: ProposalCLIP: unsupervised open-category object proposal generation via exploiting clip cues. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)

    Google Scholar 

  43. Siméoni, O., et al.: Localizing objects with self-supervised transformers and no labels. In: BMVC (2021)

    Google Scholar 

  44. Sohn, K., Zhang, Z., Li, C.L., Zhang, H., Lee, C.Y., Pfister, T.: A simple semi-supervised learning framework for object detection. In: arXiv:2005.04757 (2020)

  45. Sun, C., Myers, A., Vondrick, C., Murphy, K., Schmid, C.: Videobert: A joint model for video and language representation learning. In: ICCV (2019)

    Google Scholar 

  46. Uijlings, J., van de Sande, K., Gevers, T., Smeulders, A.: Selective search for object recognition. Int. J. Comput. Vis. 104, 154–171 (2013)

    Google Scholar 

  47. Wang, L., Li, Y., Lazebnik, S.: Learning Deep Structure-Preserving Image-Text Embeddings. In: CVPR (2016)

    Google Scholar 

  48. Wu, Y., Kirillov, A., Massa, F., Lo, W.Y., Girshick, R.: Detectron2. https://github.com/facebookresearch/detectron2 (2019)

  49. Xu, M., et al.: End-to-end semi-supervised object detection with soft teacher. In: ICCV, pp. 3060–3069 (2021)

    Google Scholar 

  50. Xu, M., et al.: A simple baseline for zero-shot semantic segmentation with pre-trained vision-language model (2021)

    Google Scholar 

  51. Yu, F., et al.: Unsupervised domain adaptation for object detection via cross-domain semi-supervised learning. In: WACV (2022)

    Google Scholar 

  52. Yu, L., et al.: MAttNet: modular attention network for referring expression comprehension. In: CVPR (2018)

    Google Scholar 

  53. Yu, L., Poirson, P., Yang, S., Berg, A.C., Berg, T.L.: Modeling context in referring expressions. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 69–85. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_5

    Chapter  Google Scholar 

  54. Zareian, A., Rosa, K.D., Hu, D.H., Chang, S.F.: Open-vocabulary object detection using captions. In: CVPR (2021)

    Google Scholar 

  55. Zhang, P., et al.: VinVL: revisiting visual representations in vision-language models. In: CVPR (2021)

    Google Scholar 

  56. Zhao, X., Schulter, S., Sharma, G., Tsai, Y.-H., Chandraker, M., Wu, Y.: Object detection with a unified label space from multiple datasets. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 178–193. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58568-6_11

    Chapter  Google Scholar 

  57. Zhong, Y., et al.: RegionCLIP: Region-based language-image pretraining. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)

    Google Scholar 

  58. Zhong, Y., Wang, J., Peng, J., Zhang, L.: Boosting weakly supervised object detection with progressive knowledge transfer. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12371, pp. 615–631. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58574-7_37

    Chapter  Google Scholar 

  59. Zhou, C., Loy, C.C., Dai, B.: DenseCLIP: extract free dense labels from clip. In: ECCV 2022 (2021)

    Google Scholar 

  60. Zhou, Q., Yu, C., Wang, Z., Qian, Q., Li, H.: Instant-teaching: an end-to-end semi-supervised object detection framework. In: CVPR (2021)

    Google Scholar 

  61. Zhu, P., Wang, H., Saligrama, V.: Don’t even look once: synthesizing features for zero-shot detection. In: CVPR, pp. 11693–11702 (2020)

    Google Scholar 

Download references

Acknowledgments

This research has been partially funded by research grants to D. Metaxas from NEC Labs America through NSF IUCRC CARTA-1747778, NSF: 1951890, 2003874, 1703883, 1763523 and ARO MURI SCAN.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shiyu Zhao .

Editor information

Editors and Affiliations

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 8766 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhao, S. et al. (2022). Exploiting Unlabeled Data with Vision and Language Models for Object Detection. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13669. Springer, Cham. https://doi.org/10.1007/978-3-031-20077-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-20077-9_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20076-2

  • Online ISBN: 978-3-031-20077-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics