Skip to main content
SpringerLink
Log in

Class-Agnostic Object Detection with Multi-modal Transformer

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

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13670))

Included in the following conference series:

Abstract

What constitutes an object? This has been a long-standing question in computer vision. Towards this goal, numerous learning-free and learning-based approaches have been developed to score objectness. However, they generally do not scale well across new domains and novel objects. In this paper, we advocate that existing methods lack a top-down supervision signal governed by human-understandable semantics. For the first time in literature, we demonstrate that Multi-modal Vision Transformers (MViT) trained with aligned image-text pairs can effectively bridge this gap. Our extensive experiments across various domains and novel objects show the state-of-the-art performance of MViTs to localize generic objects in images. Based on the observation that existing MViTs do not include multi-scale feature processing and usually require longer training schedules, we develop an efficient MViT architecture using multi-scale deformable attention and late vision-language fusion. We show the significance of MViT proposals in a diverse range of applications including open-world object detection, salient and camouflage object detection, supervised and self-supervised detection tasks. Further, MViTs can adaptively generate proposals given a specific language query and thus offer enhanced interactability. Code: https://git.io/J1HPY.

M. Maaz and H. Rasheed—Equal contribution.

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

Access this chapter

Log in via an institution
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

References

  1. Alexe, B., Deselaers, T., Ferrari, V.: What is an object? In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 73–80. IEEE (2010)

    Google Scholar 

  2. Alexe, B., Deselaers, T., Ferrari, V.: Measuring the objectness of image windows. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2189–2202 (2012)

    Article  Google Scholar 

  3. Bar, A.,et al.: DETReg: unsupervised Pretraining with region priors for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2022)

    Google Scholar 

  4. Bendale, A., Boult, T.: Towards open world recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1893–1902 (2015)

    Google Scholar 

  5. 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 

  6. Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. In: Advances in Neural Information Processing Systems (2020)

    Google Scholar 

  7. Caron, M., et al.: Emerging properties in self-supervised vision transformers. arXiv preprint arXiv:2104.14294 (2021)

  8. Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607. PMLR (2020)

    Google Scholar 

  9. 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 

  10. Cheng, M.M., Zhang, Z., Lin, W.Y., Torr, P.: BING: binarized normed gradients for objectness estimation at 300fps. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3286–3293 (2014)

    Google Scholar 

  11. Dai, Z., Cai, B., Lin, Y., Chen, J.: UP-DETR: unsupervised pre-training for object detection with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1601–1610 (2021)

    Google Scholar 

  12. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL (2019)

    Google Scholar 

  13. Dhamija, A., Gunther, M., Ventura, J., Boult, T.: The overlooked elephant of object detection: open set. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1021–1030 (2020)

    Google Scholar 

  14. Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vision 88(2), 303–338 (2010). https://doi.org/10.1007/s11263-009-0275-4

    Article  Google Scholar 

  15. Fan, D.P., Ji, G.P., Cheng, M.M., Shao, L.: Concealed object detection. IEEE Trans. Pattern Anal. Mach. Intell. 44, 6024–6042 (2021)

    Google Scholar 

  16. Fan, D.P., Ji, G.P., Sun, G., Cheng, M.M., Shen, J., Shao, L.: Camouflaged object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2777–2787 (2020)

    Google Scholar 

  17. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3354–3361. IEEE (2012)

    Google Scholar 

  18. Georgakis, G., Reza, M.A., Mousavian, A., Le, P.H., Košecká, J.: multiview RGB-D dataset for object instance detection. In: CoRR, pp. 426–434. IEEE (2016)

    Google Scholar 

  19. Gupta, A., Dollar, P., Girshick, R.: LVIS: a dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019)

    Google Scholar 

  20. Gupta, T., Kamath, A., Kembhavi, A., Hoiem, D.: Towards general purpose vision systems. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16399–16409 (2022)

    Google Scholar 

  21. He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729–9738 (2020)

    Google Scholar 

  22. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2961–2969 (2017)

    Google Scholar 

  23. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  24. Honnibal, M., Montani, I.: spaCy: industrial-strength natural language processing in python (2020)

    Google Scholar 

  25. Hudson, D.A., Manning, C.D.: 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, pp. 6700–6709 (2019)

    Google Scholar 

  26. Inoue, N., Furuta, R., Yamasaki, T., Aizawa, K.: Cross-domain weakly-supervised object detection through progressive domain adaptation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5001–5009 (2018)

    Google Scholar 

  27. Jaiswal, A., Wu, Y., Natarajan, P., Natarajan, P.: Class-agnostic object detection. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 919–928 (2021)

    Google Scholar 

  28. Joseph, K., Khan, S., Khan, F.S., Balasubramanian, V.N.: Towards open world object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5830–5840 (2021)

    Google Scholar 

  29. Kamath, A., Singh, M., LeCun, Y., Synnaeve, G., Misra, I., Carion, N.: MDETR-modulated detection for end-to-end multi-modal understanding. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1780–1790 (2021)

    Google Scholar 

  30. Kazemzadeh, S., Ordonez, V., Matten, M., Berg, T.: Referitgame: Referring to objects in photographs of natural scenes. In: Conference on Empirical Methods in Natural Language Processing

    Google Scholar 

  31. Kim, D., Lin, T.Y., Angelova, A., Kweon, I.S., Kuo, W.: Learning open-world object proposals without learning to classify. ar0Xiv preprint arXiv:2108.06753 (2021)

  32. Krishna, R., et al.: Visual genome: connecting language and vision using crowdsourced dense image annotations. Int. J. Comput. Vision 123(1), 32–73 (2017). https://doi.org/10.1007/S11263-016-0981-7

    Article  MathSciNet  Google Scholar 

  33. Kuo, W., Hariharan, B., Malik, J.: DeepBox: learning objectness with convolutional networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,pp. 2479–2487 (2015)

    Google Scholar 

  34. Kuznetsova, A.: The open images dataset v4. IJCV 128(7), 1956–1981 (2020). https://doi.org/10.1007/s11263-020-01316-z

    Article  Google Scholar 

  35. Le, T.N., Nguyen, T.V., Nie, Z., Tran, M.T., Sugimoto, A.: Anabranch network for camouflaged object segmentation. Comput. Vis. Image Underst. 184, 45–56 (2019)

    Article  Google Scholar 

  36. Li, L.H., Yatskar, M., Yin, D., Hsieh, C.J., Chang, K.W.: VisualBERT: a simple and performant baseline for vision and language. arXiv preprint arXiv:1908.03557 (2019)

  37. 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 

  38. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,pp. 2117–2125 (2017)

    Google Scholar 

  39. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2980–2988 (2017)

    Google Scholar 

  40. 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 

  41. Liu, J.J., Hou, Q., Cheng, M.M., Feng, J., Jiang, J.: A simple pooling-based design for real-time salient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3917–3926 (2019)

    Google Scholar 

  42. Liu, L., et al.: Deep learning for generic object detection: a survey. Int. J. Comput. Vision 128(2), 261–318 (2020). https://doi.org/10.1007/s11263-019-01247-4

    Article  MATH  Google Scholar 

  43. Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)

  44. Lu, J., Batra, D., Parikh, D., Lee, S.: ViLBERT: pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. In: Advances in Neural Information Processing Systems (2019)

    Google Scholar 

  45. Lu, J., Goswami, V., Rohrbach, M., Parikh, D., Lee, S.: 12-in-1: multi-task vision and language representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10437–10446 (2020)

    Google Scholar 

  46. Misra, I., Maaten, L.V.D.: Self-supervised learning of pretext-invariant representations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6707–6717 (2020)

    Google Scholar 

  47. Oliva, A., Torralba, A.: The role of context in object recognition. Trends Cogn. Sci. 11(12), 520–527 (2007)

    Article  Google Scholar 

  48. Peyré, G., Cuturi, M.: Computational Optimal Transport (2020)

    Google Scholar 

  49. Pinheiro, P.O., Collobert, R., Dollár, P.: Learning to segment object candidates. In: Advances in Neural Information Processing Systems (2015)

    Google Scholar 

  50. Pinheiro, P.O., Lin, T.-Y., Collobert, R., Dollár, P.: Learning to refine object segments. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 75–91. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_5

    Chapter  Google Scholar 

  51. Plummer, B.A., Wang, L., Cervantes, C.M., Caicedo, J.C., Hockenmaier, J., Lazebnik, S.: Flickr30k entities: collecting region-to-phrase correspondences for richer image-to-sentence models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2641–2649 (2015)

    Google Scholar 

  52. Pont-Tuset, J., Arbelaez, P., Barron, J.T., Marques, F., Malik, J.: Multiscale combinatorial grouping for image segmentation and object proposal generation. IEEE Trans. Pattern Anal. Mach. Intell. 39(1), 128–140 (2016)

    Article  Google Scholar 

  53. Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning (2021)

    Google Scholar 

  54. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural. Inf. Process. Syst. 28, 91–99 (2015)

    Google Scholar 

  55. Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y

    Article  MathSciNet  Google Scholar 

  56. Shao, S., et al.: Objects365: a large-scale, high-quality dataset for object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8430–8439 (2019)

    Google Scholar 

  57. Shi, J., Yan, Q., Xu, L., Jia, J.: Hierarchical image saliency detection on extended CSSD. IEEE Trans. Pattern Anal. Mach. Intell. 38(4), 717–729 (2015)

    Article  Google Scholar 

  58. Siméoni, O., et al.: Localizing objects with self-supervised transformers and no labels. In: British Machine Vision Conference (2021)

    Google Scholar 

  59. Skurowski, P., Abdulameer, H., Błaszczyk, J., Depta, T., Kornacki, A., Kozieł, P.: Animal camouflage analysis: Chameleon database. Unpublished Manuscript 2(6), 7 (2018)

    Google Scholar 

  60. Su, W., et al.: VL-BERT: pre-training of generic visual-linguistic representations. In: International Conference on Learning Representations (2019)

    Google Scholar 

  61. Sun, C., Myers, A., Vondrick, C., Murphy, K., Schmid, C.: VideoBERT: a joint model for video and language representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464–7473 (2019)

    Google Scholar 

  62. Tan, H., Bansal, M.: LXMERT: learning cross-modality encoder representations from transformers. In: Conference on Empirical Methods in Natural Language Processing (2019)

    Google Scholar 

  63. Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114. PMLR (2019)

    Google Scholar 

  64. Uijlings, J.R., Van De Sande, K.E., Gevers, T., Smeulders, A.W.: Selective search for object recognition. Int. J. Comput. Vision 104(2), 154–171 (2013). https://doi.org/10.1007/s11263-013-0620-5

    Article  Google Scholar 

  65. Wang, W., Feiszli, M., Wang, H., Tran, D.: Unidentified video objects: a benchmark for dense, open-world segmentation. arXiv preprint arXiv:2104.04691 (2021)

  66. Wang, X., Huang, T.E., Darrell, T., Gonzalez, J.E., Yu, F.: Frustratingly simple few-shot object detection. arXiv preprint arXiv:2003.06957 (2020)

  67. Wang, X., Cai, Z., Gao, D., Vasconcelos, N.: Towards universal object detection by domain attention. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7289–7298 (2019)

    Google Scholar 

  68. Wightman, R.: PyTorch image models (2019). https://github.com/rwightman/pytorch-image-models. https://doi.org/10.5281/zenodo.4414861

  69. Wu, K., Otoo, E., Shoshani, A.: Optimizing connected component labeling algorithms. In: Medical Imaging 2005: Image Processing, vol. 5747, pp. 1965–1976. International Society for Optics and Photonics (2005)

    Google Scholar 

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

  71. Wu, Z., Su, L., Huang, Q.: Cascaded partial decoder for fast and accurate salient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3907–3916 (2019)

    Google Scholar 

  72. Xia, G.S., et al.: DOTA: a large-scale dataset for object detection in aerial images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3974–3983 (2018)

    Google Scholar 

  73. Xiao, T., Reed, C.J., Wang, X., Keutzer, K., Darrell, T.: Region similarity representation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2021)

    Google Scholar 

  74. Xie, E., et al.: DetCo: unsupervised contrastive learning for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8392–8401 (2021)

    Google Scholar 

  75. Xie, Q., Luong, M.T., Hovy, E., Le, Q.V.: Self-training with noisy student improves imageNet classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10687–10698 (2020)

    Google Scholar 

  76. Yan, K., Wang, X., Lu, L., Summers, R.M.: DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. J. Med. Imaging 5(3), 036501 (2018)

    Article  Google Scholar 

  77. Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.H.: Saliency detection via graph-based manifold ranking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3166–3173 (2013)

    Google Scholar 

  78. Zareian, A., Rosa, K.D., Hu, D.H., Chang, S.F.: Open-vocabulary object detection using captions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14393–14402 (2021)

    Google Scholar 

  79. Zbontar, J., Jing, L., Misra, I., LeCun, Y., Deny, S.: Barlow twins: self-supervised learning via redundancy reduction. In: International Conference on Machine Learning (2021)

    Google Scholar 

  80. Zhang, M., Tseng, C., Kreiman, G.: Putting visual object recognition in context. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12985–12994 (2020)

    Google Scholar 

  81. Zhang, Z., et al.: BING++: a fast high quality object proposal generator at 100fps. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, pp. 1209–1223 (2018)

    Google Scholar 

  82. Zhou, M., et al.: UC2: universal cross-lingual cross-modal vision-and-language pre-training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4155–4165 (2021)

    Google Scholar 

  83. Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: deformable transformers for end-to-end object detection. In: International Conference on Learning Representations (2021)

    Google Scholar 

  84. Zitnick, C.L., Dollár, P.: Edge boxes: locating object proposals from edges. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 391–405. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_26

    Chapter  Google Scholar 

Download references

Acknowledgements

Ming-Hsuan Yang is supported by the NSF CAREER grant 1149783. Fahad Shahbaz Khan is supported by the VR starting grant (2016-05543).

Author information

Authors and Affiliations

  1. Mohamed bin Zayed University of AI, Abu Dhabi, UAE

    Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan & Rao Muhammad Anwer

  2. Australian National University, Canberra, Australia

    Salman Khan

  3. Linköping University, Linkoping, Sweden

    Fahad Shahbaz Khan

  4. Aalto University, Espoo, Finland

    Rao Muhammad Anwer

  5. University of California, Merced, Merced, USA

    Ming-Hsuan Yang

  6. Yonsei University, Seoul, South Korea

    Ming-Hsuan Yang

  7. Google Research, Mountain View, USA

    Ming-Hsuan Yang

Authors
  1. Muhammad Maaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hanoona Rasheed
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Salman Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fahad Shahbaz Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rao Muhammad Anwer
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ming-Hsuan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muhammad Maaz .

Editor information

Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 16199 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

Maaz, M., Rasheed, H., Khan, S., Khan, F.S., Anwer, R.M., Yang, MH. (2022). Class-Agnostic Object Detection with Multi-modal Transformer. 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 13670. Springer, Cham. https://doi.org/10.1007/978-3-031-20080-9_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-20080-9_30

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20079-3

  • Online ISBN: 978-3-031-20080-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation