Skip to main content
Springer Nature Link
Log in

Exploring Phrase-Level Grounding with Text-to-Image Diffusion Model

  • Conference paper
  • First Online:
Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15111))

Included in the following conference series:

  • 218 Accesses

Abstract

Recently, diffusion models have increasingly demonstrated their capabilities in vision understanding. By leveraging prompt-based learning to construct sentences, these models have shown proficiency in classification and visual grounding tasks. However, existing approaches primarily showcase their ability to perform sentence-level localization, leaving the potential for leveraging contextual information for phrase-level understanding largely unexplored. In this paper, we utilize Panoptic Narrative Grounding (PNG) as a proxy task to investigate this capability further. PNG aims to segment object instances mentioned by multiple noun phrases within a given narrative text. Specifically, we introduce the DiffPNG framework, a straightforward yet effective approach that fully capitalizes on the diffusion’s architecture for segmentation by decomposing the process into a sequence of localization, segmentation, and refinement steps. The framework initially identifies anchor points using cross-attention mechanisms and subsequently performs segmentation with self-attention to achieve zero-shot PNG. Moreover, we introduce a refinement module based on SAM to enhance the quality of the segmentation masks. Our extensive experiments on the PNG dataset demonstrate that DiffPNG achieves strong performance in the zero-shot PNG task setting, conclusively proving the diffusion model’s capability for context-aware, phrase-level understanding. Source code is available at https://github.com/nini0919/DiffPNG.

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

Access this chapter

Log in via an institution

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Baranchuk, D., Rubachev, I., Voynov, A., Khrulkov, V., Babenko, A.: Label-efficient semantic segmentation with diffusion models. arXiv preprint arXiv:2112.03126 (2021)

  2. Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096 (2018)

  3. Caron, M., et al.: Emerging properties in self-supervised vision transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9650–9660 (2021)

    Google Scholar 

  4. Chen, S., Sun, P., Song, Y., Luo, P.: Diffusiondet: diffusion model for object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 19830–19843 (2023)

    Google Scholar 

  5. Chen, Y.H., Chen, W.Y., Chen, Y.T., Tsai, B.C., Frank Wang, Y.C., Sun, M.: No more discrimination: cross city adaptation of road scene segmenters. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1992–2001 (2017)

    Google Scholar 

  6. Cho, J.H., Mall, U., Bala, K., Hariharan, B.: Picie: unsupervised semantic segmentation using invariance and equivariance in clustering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16794–16804 (2021)

    Google Scholar 

  7. Cornia, M., Stefanini, M., Baraldi, L., Cucchiara, R.: Meshed-memory transformer for image captioning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10578–10587 (2020)

    Google Scholar 

  8. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

  9. Ding, J., Xue, N., Xia, G.S., Dai, D.: Decoupling zero-shot semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11583–11592 (2022)

    Google Scholar 

  10. Ding, Z., et al.: PPMN: pixel-phrase matching network for one-stage panoptic narrative grounding. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 5537–5546 (2022)

    Google Scholar 

  11. Dockhorn, T., Vahdat, A., Kreis, K.: Score-based generative modeling with critically-damped langevin diffusion. arXiv preprint arXiv:2112.07068 (2021)

  12. Ehsani, K., Mottaghi, R., Farhadi, A.: Segan: segmenting and generating the invisible. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6144–6153 (2018)

    Google Scholar 

  13. Esser, P., Rombach, R., Ommer, B.: Taming transformers for high-resolution image synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12873–12883 (2021)

    Google Scholar 

  14. Feng, Q., Gadde, R., Liao, W., Ramon, E., Martinez, A.: Network-free, unsupervised semantic segmentation with synthetic images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 23602–23610 (2023)

    Google Scholar 

  15. Galeev, D., Sofiiuk, K., Rukhovich, D., Romanov, M., Barinova, O., Konushin, A.: Learning high-resolution domain-specific representations with a GAN generator. In: Torsello, A., Rossi, L., Pelillo, M., Biggio, B., Robles-Kelly, A. (eds.) S+SSPR 2021. LNCS, vol. 12644, pp. 108–118. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73973-7_11

    Chapter  Google Scholar 

  16. González, C., Ayobi, N., Hernández, I., Hernández, J., Pont-Tuset, J., Arbeláez, P.: Panoptic narrative grounding. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1364–1373 (2021)

    Google Scholar 

  17. González, C., Ayobi, N., Hernández, I., Pont-Tuset, J., Arbeláez, P.: Piglet: pixel-level grounding of language expressions with transformers. IEEE Trans. Pattern Anal. Mach. Intell. (2023)

    Google Scholar 

  18. Guo, T., Wang, H., Ma, Y., Ji, J., Sun, X.: Improving panoptic narrative grounding by harnessing semantic relationships and visual confirmation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 1985–1993 (2024)

    Google Scholar 

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

    Google Scholar 

  20. Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840–6851 (2020)

    Google Scholar 

  21. Hoffman, J., Wang, D., Yu, F., Darrell, T.: FCNs in the wild: pixel-level adversarial and constraint-based adaptation. arXiv preprint arXiv:1612.02649 (2016)

  22. Hui, T., et al.: Enriching phrases with coupled pixel and object contexts for panoptic narrative grounding. arXiv preprint arXiv:2311.01091 (2023)

  23. Jiang, B., Luo, R., Mao, J., Xiao, T., Jiang, Y.: Acquisition of localization confidence for accurate object detection. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 784–799 (2018)

    Google Scholar 

  24. Jolicoeur-Martineau, A., Piché-Taillefer, R., Combes, R.T.d., Mitliagkas, I.: Adversarial score matching and improved sampling for image generation. arXiv preprint arXiv:2009.05475 (2020)

  25. Kafle, K., Kanan, C.: Visual question answering: datasets, algorithms, and future challenges. Comput. Vis. Image Underst. 163, 3–20 (2017)

    Article  Google Scholar 

  26. Karazija, L., Laina, I., Vedaldi, A., Rupprecht, C.: Diffusion models for zero-shot open-vocabulary segmentation. arXiv preprint arXiv:2306.09316 (2023)

  27. Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4401–4410 (2019)

    Google Scholar 

  28. Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of stylegan. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8110–8119 (2020)

    Google Scholar 

  29. Kirillov, A., et al.: Segment anything. arXiv preprint arXiv:2304.02643 (2023)

  30. Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFs with gaussian edge potentials. In: Advances in Neural Information Processing Systems, vol. 24 (2011)

    Google Scholar 

  31. Li, A.C., Prabhudesai, M., Duggal, S., Brown, E., Pathak, D.: Your diffusion model is secretly a zero-shot classifier. arXiv preprint arXiv:2303.16203 (2023)

  32. Li, D., Ling, H., Kim, S.W., Kreis, K., Fidler, S., Torralba, A.: Bigdatasetgan: synthesizing imagenet with pixel-wise annotations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 21330–21340 (2022)

    Google Scholar 

  33. Li, K., et al.: Acseg: adaptive conceptualization for unsupervised semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7162–7172 (2023)

    Google Scholar 

  34. Li, Z., Zhou, Q., Zhang, X., Zhang, Y., Wang, Y., Xie, W.: Guiding text-to-image diffusion model towards grounded generation. arXiv preprint arXiv:2301.05221 (2023)

  35. Li, Z., Zhou, Q., Zhang, X., Zhang, Y., Wang, Y., Xie, W.: Open-vocabulary object segmentation with diffusion models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7667–7676 (2023)

    Google Scholar 

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

    Google Scholar 

  37. Lin, Y., Jin, X.B., Wang, Q., Huang, K.: Context does matter: end-to-end panoptic narrative grounding with deformable attention refined matching network. In: 2023 IEEE International Conference on Data Mining (ICDM), pp. 1163–1168. IEEE (2023)

    Google Scholar 

  38. Liu, X., Huang, S., Kang, Y., Chen, H., Wang, D.: Vgdiffzero: text-to-image diffusion models can be zero-shot visual grounders. arXiv preprint arXiv:2309.01141 (2023)

  39. Luc, P., Couprie, C., Chintala, S., Verbeek, J.: Semantic segmentation using adversarial networks. arXiv preprint arXiv:1611.08408 (2016)

  40. Luo, G., et al.: Multi-task collaborative network for joint referring expression comprehension and segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10034–10043 (2020)

    Google Scholar 

  41. Luo, Y., et al.: Dual-level collaborative transformer for image captioning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2286–2293 (2021)

    Google Scholar 

  42. Lutz, S., Amplianitis, K., Smolic, A.: Alphagan: generative adversarial networks for natural image matting. arXiv preprint arXiv:1807.10088 (2018)

  43. Ma, C., et al.: Diffusionseg: adapting diffusion towards unsupervised object discovery. arXiv preprint arXiv:2303.09813 (2023)

  44. Meng, C., et al.: Sdedit: guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073 (2021)

  45. Xu, M., et al.: A simple baseline for open vocabulary semantic segmentation with pre-trained vision-language model. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13689, pp. 736–753. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19818-2_42

    Chapter  Google Scholar 

  46. Mokady, R., Hertz, A., Aberman, K., Pritch, Y., Cohen-Or, D.: Null-text inversion for editing real images using guided diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6038–6047 (2023)

    Google Scholar 

  47. Nguyen, Q., Vu, T., Tran, A., Nguyen, K.: Dataset diffusion: diffusion-based synthetic data generation for pixel-level semantic segmentation. In: Advances in Neural Information Processing Systems, vol. 36 (2024)

    Google Scholar 

  48. Ni, M., Zhang, Y., Feng, K., Li, X., Guo, Y., Zuo, W.: Ref-diff: zero-shot referring image segmentation with generative models. arXiv preprint arXiv:2308.16777 (2023)

  49. Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: International Conference on Machine Learning, pp. 8162–8171. PMLR (2021)

    Google Scholar 

  50. Pan, Y., Yao, T., Li, Y., Mei, T.: X-linear attention networks for image captioning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10971–10980 (2020)

    Google Scholar 

  51. Pnvr, K., Singh, B., Ghosh, P., Siddiquie, B., Jacobs, D.: LD-ZNet: a latent diffusion approach for text-based image segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4157–4168 (2023)

    Google Scholar 

  52. Qian, Z., Ma, Y., Ji, J., Sun, X.: X-refseg3d: enhancing referring 3D instance segmentation via structured cross-modal graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 4551–4559 (2024)

    Google Scholar 

  53. Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., Chen, M.: Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, vol. 1, no. 2, p. 3 (2022)

  54. Ren, T., et al.: Grounded SAM: assembling open-world models for diverse visual tasks (2024)

    Google Scholar 

  55. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684–10695 (2022)

    Google Scholar 

  56. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  57. Saharia, C.: Photorealistic text-to-image diffusion models with deep language understanding. Adv. Neural. Inf. Process. Syst. 35, 36479–36494 (2022)

    Google Scholar 

  58. Schuhmann, C.: Laion-5b: an open large-scale dataset for training next generation image-text models. Adv. Neural. Inf. Process. Syst. 35, 25278–25294 (2022)

    Google Scholar 

  59. Shao, Z., Sengupta, S., Li, H., Anastasio, M.A.: Semi-supervised semantic segmentation of cell nuclei via diffusion-based large-scale pre-training and collaborative learning. arXiv preprint arXiv:2308.04578 (2023)

  60. Shih, K.J., Singh, S., Hoiem, D.: Where to look: focus regions for visual question answering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4613–4621 (2016)

    Google Scholar 

  61. Shin, G., Xie, W., Albanie, S.: Reco: retrieve and co-segment for zero-shot transfer. Adv. Neural. Inf. Process. Syst. 35, 33754–33767 (2022)

    Google Scholar 

  62. Shin, G., Xie, W., Albanie, S.: Namedmask: distilling segmenters from complementary foundation models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4960–4969 (2023)

    Google Scholar 

  63. Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: International Conference on Machine Learning, pp. 2256–2265. PMLR (2015)

    Google Scholar 

  64. Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)

  65. Song, Y., Ermon, S.: Generative modeling by estimating gradients of the data distribution. In: Advances in Neural Information Processing Systems, vol. 32 (2019)

    Google Scholar 

  66. Song, Y., Ermon, S.: Improved techniques for training score-based generative models. Adv. Neural. Inf. Process. Syst. 33, 12438–12448 (2020)

    Google Scholar 

  67. Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456 (2020)

  68. Tang, R., et al.: What the DAAM: interpreting stable diffusion using cross attention. arXiv preprint arXiv:2210.04885 (2022)

  69. Tian, J., Aggarwal, L., Colaco, A., Kira, Z., Gonzalez-Franco, M.: Diffuse, attend, and segment: unsupervised zero-shot segmentation using stable diffusion. arXiv preprint arXiv:2308.12469 (2023)

  70. Tritrong, N., Rewatbowornwong, P., Suwajanakorn, S.: Repurposing GANs for one-shot semantic part segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4475–4485 (2021)

    Google Scholar 

  71. Tsai, Y.H., Hung, W.C., Schulter, S., Sohn, K., Yang, M.H., Chandraker, M.: Learning to adapt structured output space for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7472–7481 (2018)

    Google Scholar 

  72. Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell: lessons learned from the 2015 MSCOCO image captioning challenge. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 652–663 (2016)

    Article  Google Scholar 

  73. Wang, H., et al.: Nice: improving panoptic narrative detection and segmentation with cascading collaborative learning. arXiv preprint arXiv:2310.10975 (2023)

  74. Wang, H., Ji, J., Zhou, Y., Wu, Y., Sun, X.: Towards real-time panoptic narrative grounding by an end-to-end grounding network. arXiv preprint arXiv:2301.03160 (2023)

  75. Wang, X., Zhang, X., Cao, Y., Wang, W., Shen, C., Huang, T.: Seggpt: towards segmenting everything in context. In: 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 1130–1140 (2023). https://doi.org/10.1109/ICCV51070.2023.00110

  76. Wu, Q., Teney, D., Wang, P., Shen, C., Dick, A., Van Den Hengel, A.: Visual question answering: a survey of methods and datasets. Comput. Vis. Image Underst. 163, 21–40 (2017)

    Article  Google Scholar 

  77. Wu, W., Zhao, Y., Shou, M.Z., Zhou, H., Shen, C.: Diffumask: synthesizing images with pixel-level annotations for semantic segmentation using diffusion models. arXiv preprint arXiv:2303.11681 (2023)

  78. Xu, J., Liu, S., Vahdat, A., Byeon, W., Wang, X., De Mello, S.: Open-vocabulary panoptic segmentation with text-to-image diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2955–2966 (2023)

    Google Scholar 

  79. Xu, K., et al.: Show, attend and tell: Neural image caption generation with visual attention. In: International Conference on Machine Learning, pp. 2048–2057. PMLR (2015)

    Google Scholar 

  80. Xu, M., et al.: A simple baseline for open-vocabulary semantic segmentation with pre-trained vision-language model. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13689, pp. 736–753. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19818-2_42

    Chapter  Google Scholar 

  81. Yang, D., et al.: Sam as the guide: mastering pseudo-label refinement in semi-supervised referring expression segmentation. arXiv preprint arXiv:2406.01451 (2024)

  82. Yang, D., et al.: Semi-supervised panoptic narrative grounding. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 7164–7174 (2023)

    Google Scholar 

  83. Yang, Z., Wang, J., Tang, Y., Chen, K., Zhao, H., Torr, P.H.: Lavt: language-aware vision transformer for referring image segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18155–18165 (2022)

    Google Scholar 

  84. 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 

  85. Yu, S., Seo, P.H., Son, J.: Zero-shot referring image segmentation with global-local context features. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19456–19465 (2023)

    Google Scholar 

  86. Zhang, Y., et al.: Datasetgan: efficient labeled data factory with minimal human effort. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10145–10155 (2021)

    Google Scholar 

  87. Zhou, B., Tian, Y., Sukhbaatar, S., Szlam, A., Fergus, R.: Simple baseline for visual question answering. arXiv preprint arXiv:1512.02167 (2015)

  88. Zhou, Z., Lei, Y., Zhang, B., Liu, L., Liu, Y.: Zegclip: towards adapting clip for zero-shot semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11175–11185 (2023)

    Google Scholar 

  89. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)

    Google Scholar 

Download references

Acknowledgements

This work was supported by National Science and Technology Major Project (No. 2022ZD0118201), the National Science Fund for Distinguished Young Scholars (No. 62025603), theNational Natural Science Foundation of China (No. U21B2037, No. U22B2051, No. 62072389), the National Natural Science Fund for Young Scholars of China (No. 62302411), China Postdoctoral Science Foundation (No. 2023M732948), the NaturalScience Foundation of Fujian Province of China (No. 2021J06003, No. 2022J06001), andpartially sponsored by CCF-NetEase ThunderFire lnnovation Research Funding (No. CCF-Netease 202301).

Author information

Authors and Affiliations

  1. Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, School of Informatics, Xiamen University, Xiamen, China

    Danni Yang, Ruohan Dong, Jiayi Ji, Yiwei Ma, Haowei Wang, Xiaoshuai Sun & Rongrong Ji

  2. Youtu Lab, Tencent, Shanghai, China

    Haowei Wang

Authors
  1. Danni Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruohan Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiayi Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yiwei Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Haowei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaoshuai Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Rongrong Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaoshuai Sun .

Editor information

Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 2649 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2025 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

Yang, D. et al. (2025). Exploring Phrase-Level Grounding with Text-to-Image Diffusion Model. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15111. Springer, Cham. https://doi.org/10.1007/978-3-031-73668-1_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-73668-1_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-73667-4

  • Online ISBN: 978-3-031-73668-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics