Advertisement

International Journal of Computer Vision

, Volume 123, Issue 1, pp 74–93 | Cite as

Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models

  • Bryan A. Plummer
  • Liwei Wang
  • Chris M. Cervantes
  • Juan C. Caicedo
  • Julia Hockenmaier
  • Svetlana Lazebnik
Article

Abstract

The Flickr30k dataset has become a standard benchmark for sentence-based image description. This paper presents Flickr30k Entities, which augments the 158k captions from Flickr30k with 244k coreference chains, linking mentions of the same entities across different captions for the same image, and associating them with 276k manually annotated bounding boxes. Such annotations are essential for continued progress in automatic image description and grounded language understanding. They enable us to define a new benchmark for localization of textual entity mentions in an image. We present a strong baseline for this task that combines an image-text embedding, detectors for common objects, a color classifier, and a bias towards selecting larger objects. While our baseline rivals in accuracy more complex state-of-the-art models, we show that its gains cannot be easily parlayed into improvements on such tasks as image-sentence retrieval, thus underlining the limitations of current methods and the need for further research.

Keywords

Computer vision Language Region phrase correspondence Datasets Crowdsourcing 

Notes

Acknowledgments

This material is based upon work supported by the National Science Foundation under Grants No. 1053856, 1205627, 1405883, 1228082, 1302438, 1563727, as well as support from Xerox UAC and the Sloan Foundation. We thank the NVIDIA Corporation for the generous donation of the GPUs used for our experiments.

References

  1. Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C.L., & Parikh, D. (2015). Vqa: Visual question answering. In ICCV.Google Scholar
  2. Chen, X. & Zitnick, C. L. (2015). Minds eye: A recurrent visual representation for image caption generation. In CVPR.Google Scholar
  3. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In CVPR.Google Scholar
  4. Devlin, J., Cheng, H., Fang, H., Gupta, S., Deng, L., He, X., Zweig, G., & Mitchell, M. (2015). Language models for image captioning: The quirks and what works. In ACL.Google Scholar
  5. Dodge, J., Goyal, A., Han, X., Mensch, A., Mitchell, M., Stratos, K., Yamaguchi, K., Choi, Y., III, H. D., Berg, A. C., & Berg, T. L. (2012). Detecting visual text. In NAACL.Google Scholar
  6. Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., & Darrell, T. (2015). Long-term recurrent convolutional networks for visual recognition and description. In CVPR.Google Scholar
  7. Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2008). The PASCAL Visual Object Classes Challenge 2008 (VOC2008) Results. http://www.pascal-network.org/challenges/VOC/voc2008/workshop/index.html.
  8. Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2012). The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html.
  9. Fang, H., Gupta, S., Iandola, F., Srivastava, R., Deng, L., Dollar, P., Gao, J., He, X., Mitchell, M., Platt, J., Zitnick, L., & Zweig, G. (2015). From captions to visual concepts and back. In CVPR.Google Scholar
  10. Farhadi, A., Hejrati, S., Sadeghi, A., Young, P., Rashtchian, C., Hockenmaier, J., & Forsyth, D. A. (2010). Every picture tells a story: Generating sentences from images. In ECCV.Google Scholar
  11. Fidler, S., Sharma, A., & Urtasun, R. (2013). A sentence is worth a thousand pixels. In CVPR.Google Scholar
  12. Fukui, A., Park, D. H., Yang, D., Rohrbach, A., Darrell, T., & Rohrbach, M. (2016). Multimodal compact bilinear pooling for visual question answering and visual grounding. arXiv:1606.01847.
  13. Gao, H., Mao, J., Zhou, J., Huang, Z., Wang, L., & Xu, W. (2015). Are you talking to a machine? dataset and methods for multilingual image question answering. In NIPS.Google Scholar
  14. Girshick, R. (2015). Fast r-cnn. In ICCV.Google Scholar
  15. Gong, Y., Ke, Q., Isard, M., & Lazebnik, S. (2014a). A multi-view embedding space for modeling internet images, tags, and their semantics. IJCV, 106(2), 210–233.CrossRefGoogle Scholar
  16. Gong, Y., Wang, L., Hodosh, M., Hockenmaier, J., & Lazebnik, S. (2014b). Improving image-sentence embeddings using large weakly annotated photo collections. In ECCV.Google Scholar
  17. Grubinger, M., Clough, P., Müller, H., & Deselaers, T. (2006). The iapr tc-12 benchmark: A new evaluation resource for visual information systems. In International Workshop OntoImage, pp. 13–23.Google Scholar
  18. Hodosh, M., Young, P., & Hockenmaier, J. (2013). Framing image description as a ranking task: Data, models and evaluation metrics. In JAIR.Google Scholar
  19. Hodosh, M., Young, P., Rashtchian, C., and Hockenmaier, J. (2010). Cross-caption coreference resolution for automatic image understanding. In CoNLL, pages 162-171. ACL.Google Scholar
  20. Hotelling, H. (1936). Relations between two sets of variates. In Biometrika, pp. 321–377.Google Scholar
  21. Hu, R., Xu, H., Rohrbach, M., Feng, J., Saenko, K., & Darrell, T. (2016). Natural language object retrieval. In CVPR.Google Scholar
  22. Johnson, J., Karpathy, A., & Fei-Fei, L. (2016). Densecap: Fully convolutional localization networks for dense captioning. In CVPR.Google Scholar
  23. Johnson, J., Krishna, R., Stark, M., Li, L.-J., Shamma, D. A., Bernstein, M., & Fei-Fei, L. (2015). Image retrieval using scene graphs. In CVPR.Google Scholar
  24. Karpathy, A. & Fei-Fei, L. (2015). Deep visual-semantic alignments for generating image descriptions. In CVPR.Google Scholar
  25. Karpathy, A., Joulin, A., & Fei-Fei, L. (2014). Deep fragment embeddings for bidirectional image sentence mapping. In NIPS.Google Scholar
  26. Kazemzadeh, S., Ordonez, V., Matten, M., & Berg, T. (2014). Referitgame: Referring to objects in photographs of natural scenes. In EMNLP.Google Scholar
  27. Kiros, R., Salakhutdinov, R., & Zemel, R. S. (2014). Unifying visual-semantic embeddings with multimodal neural language models. arXiv:1411.2539.
  28. Klein, B., Lev, G., Sadeh, G., & Wolf, L. (2014). Fisher vectors derived from hybrid gaussian-laplacian mixture models for image annotation. arXiv:1411.7399.
  29. Kong, C., Lin, D., Bansal, M., Urtasun, R., & Fidler, S. (2014). What are you talking about? text-to-image coreference. In CVPR.Google Scholar
  30. Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., Bernstein, M., & Fei-Fei, L. (2016). Visual genome: Connecting language and vision using crowdsourced dense image annotations. arXiv:1602.07332.
  31. Kulkarni, G., Premraj, V., Dhar, S., Li, S., Choi, Y., Berg, A. C., & Berg, T. L. (2011). Baby talk: Understanding and generating image descriptions. In CVPR.Google Scholar
  32. Lebret, R., Pinheiro, P. O., & Collobert, R. (2015). Phrase-based image captioning. In ICML.Google Scholar
  33. Lev, G., Sadeh, G., Klein, B., & Wolf, L. (2016). RNN fisher vectors for action recognition and image annotation. In ECCV.Google Scholar
  34. Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014). Microsoft COCO: Common objects in context. In ECCV.Google Scholar
  35. Ma, L., Lu, Z., Shang, L., & Li, H. (2015). Multimodal convolutional neural networks for matching image and sentence. In ICCV.Google Scholar
  36. Malinowski, M. & Fritz, M. (2014). A multi-world approach to question answering about real-world scenes based on uncertain input. In Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., and Weinberger, K., (eds) NIPS.Google Scholar
  37. Mao, J., Jonathan, H., Toshev, A., Camburu, O., Yuille, A., & Murphy, K. (2016). Generation and comprehension of unambiguous object descriptions. CVPR.Google Scholar
  38. Mao, J., Xu, W., Yang, Y., Wang, J., & Yuille, A. (2015). Deep captioning with multimodal recurrent neural networks (m-RNN). In ICLR.Google Scholar
  39. McCarthy, J. F. & Lehnert, W. G. (1995). Using decision trees for coreference resolution. http://arxiv.org/abs/cmp-lg/9505043.
  40. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In NIPS.Google Scholar
  41. Ordonez, V., Kulkarni, G., & Berg, T. L. (2011). Im2Text: Describing images using 1 million captioned photographs. NIPS.Google Scholar
  42. Perronnin, F., Sánchez, J., & Mensink, T. (2010). Improving the Fisher kernel for large-scale image classification. In ECCV.Google Scholar
  43. Plummer, B. A., Wang, L., Cervantes, C. M., Caicedo, J. C., Hockenmaier, J., & Lazebnik, S. (2015). Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models. In ICCV.Google Scholar
  44. Ramanathan, V., Joulin, A., Liang, P., & Fei-Fei, L. (2014). Linking people in videos with “their” names using coreference resolution. In ECCV.Google Scholar
  45. Rashtchian, C., Young, P., Hodosh, M., & Hockenmaier, J. (2010). Collecting image annotations using Amazon’s mechanical turk. In NAACL HLT Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk, pp. 139-147. ACL.Google Scholar
  46. Ren, M., Kiros, R., & Zemel, R. (2015). Exploring models and data for image question answering. In NIPS.Google Scholar
  47. Rohrbach, A., Rohrbach, M., Hu, R., Darrell, T., & Schiele, B. (2016). Grounding of textual phrases in images by reconstruction. In ECCV.Google Scholar
  48. Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012). Indoor segmentation and support inference from RGBD images. In ECCV.Google Scholar
  49. Simonyan, K. & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556.
  50. Soon, W. M., Ng, H. T., & Lim, D. C. Y. (2001). A machine learning approach to coreference resolution of noun phrases. Computational Linguistics, 27(4), 521–544.CrossRefGoogle Scholar
  51. Sorokin, A. & Forsyth, D. (2008). Utility data annotation with Amazon Mechanical Turk. In Internet Vision Workshop.Google Scholar
  52. Su, H., Deng, J., & Fei-Fei, L. (2012). Crowdsourcing annotations for visual object detection. In AAAI Technical Report, 4th Human Computation Workshop.Google Scholar
  53. Tommasi, T., Mallya, A., Plummer, B. A., Lazebnik, S., Berg, A., & Berg., T. (2016). Solving visual madlibs with multiple cues. In BMVC.Google Scholar
  54. Uijlings, J., van de Sande, K., Gevers, T., & Smeulders, A. (2013). Selective search for object recognition. IJCV, 104(2), 154–171.CrossRefGoogle Scholar
  55. Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2015). Show and tell: A neural image caption generator. In CVPR.Google Scholar
  56. Wang, L., Li, Y., & Lazebnik, S. (2016a). Learning deep structure-preserving image-text embeddings. In CVPR.Google Scholar
  57. Wang, M., Azab, M., Kojima, N., Mihalcea, R., & Deng, J. (2016b). Structured matching for phrase localization. In ECCV.Google Scholar
  58. Xu, K., Ba, J., Kiros, R., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015). Show, attend and tell: Neural image caption generation with visual attention. In ICML.Google Scholar
  59. Yao, B., Yang, X., Lin, L., Lee, M. W., & Zhu, S.-C. (2010). I2T: Image parsing to text description. Proceedings of the IEEE, 98(8), 1485–1508.CrossRefGoogle Scholar
  60. Young, P., Lai, A., Hodosh, M., & Hockenmaier, J. (2014). From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions. TACL, 2, 67–78.Google Scholar
  61. Yu, L., Park, E., Berg, A. C., & Berg, T. L. (2015). Visual Madlibs: Fill in the blank Image Generation and Question Answering. In ICCV.Google Scholar
  62. Zhang, J., Lin, Z., Brandt, Jonathan, S. X., & Sclaroff, S. (2016). Top-down neural attention by excitation backprop. In ECCV.Google Scholar
  63. Zitnick, C. L. & Dollár, P. (2014). Edge boxes: Locating object proposals from edges. In ECCV.Google Scholar
  64. Zitnick, C. L. & Parikh, D. (2013). Bringing semantics into focus using visual abstraction. In CVPR.Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Bryan A. Plummer
    • 1
  • Liwei Wang
    • 1
  • Chris M. Cervantes
    • 1
  • Juan C. Caicedo
    • 2
  • Julia Hockenmaier
    • 1
  • Svetlana Lazebnik
    • 1
  1. 1.University of Illinois at Urbana ChampaignUrbanaUSA
  2. 2.Broad Institute of MIT and HarvardBostonUSA

Personalised recommendations