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Cited by 616Domain Adaptation and Few-Shot LearningAdvanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild

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Chao, Wei-Lun, Changpinyo, Soravit, Gong, Boqing, Sha, Fei·2016·Lecture Notes in Computer Science
DOI 10.1007/978-3-319-46475-6_4
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  • Real-world applications often require models to learn new classes from limited samples while maintaining performance on base classes, which often represent high-frequency classes in the real world.

    “However, real-world applications often require models to learn new classes from limited samples while maintaining performance on base classes, which often represent high-frequency classes in the real world [13, 54].”

    From Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey (2025) · cites this paper as [54]

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  1. [1]1. Sudderth, E.B., Jordan, M.I.: Shared segmentation of natural scenes using dependent pitman-yor processes. In: NIPS. (2008) 1
  2. [2]2. Salakhutdinov, R., Torralba, A., Tenenbaum, J.: Learning to share visual appearance for multiclass object detection. In: CVPR. (2011) 1
  3. [3]3. Zhu, X., Anguelov, D., Ramanan, D.: Capturing long-tail distributions of object subcategories. In: CVPR. (2014) 1
  4. [4]4. Lampert, C.H., Nickisch, H., Harmeling, S.: Learning to detect unseen object classes by between-class attribute transfer. In: CVPR. (2009) 2, 3
  5. [5]5. Farhadi, A., Endres, I., Hoiem, D., Forsyth, D.: Describing objects by their attributes. In: CVPR. (2009) 2
  6. [6]6. Parikh, D., Grauman, K.: Relative attributes. In: ICCV. (2011) 2
  7. [8]8. Frome, A., Corrado, G.S., Shlens, J., Bengio, S., Dean, J., Ranzato, M., Mikolov, T.: Devise: A deep visual-semantic embedding model. In: NIPS. (2013) 2, 3, 9
  8. [9]9. Socher, R., Ganjoo, M., Manning, C.D., Ng, A.: Zero-shot learning through crossmodal transfer. In: NIPS. (2013) 2, 3, 7, 9, 18, 19, 20, 23
  9. [10]10. Palatucci, M., Pomerleau, D., Hinton, G.E., Mitchell, T.M.: Zero-shot learning with semantic output codes. In: NIPS. (2009) 2, 3
  10. [11]11. Yu, X., Aloimonos, Y.: Attribute-based transfer learning for object categorization with zero/one training example. In: ECCV. (2010) 2, 3
  11. [12]12. Rohrbach, M., Stark, M., Schiele, B.: Evaluating knowledge transfer and zero-shot learning in a large-scale setting. In: CVPR. (2011) 2, 3
  12. [13]13. Kankuekul, P., Kawewong, A., Tangruamsub, S., Hasegawa, O.: Online incremental attribute-based zero-shot learning. In: CVPR. (2012) 2, 3
  13. [14]14. Akata, Z., Perronnin, F., Harchaoui, Z., Schmid, C.: Label-embedding for attributebased classification. In: CVPR. (2013) 2, 3, 21
  14. [15]15. Yu, F.X., Cao, L., Feris, R.S., Smith, J.R., Chang, S.F.: Designing category-level attributes for discriminative visual recognition. In: CVPR. (2013) 2, 3
  15. [16]16. Mensink, T., Gavves, E., Snoek, C.G.: Costa: Co-occurrence statistics for zero-shot classification. In: CVPR. (2014) 2, 3
  16. [17]17. Norouzi, M., Mikolov, T., Bengio, S., Singer, Y., Shlens, J., Frome, A., Corrado, G.S., Dean, J.: Zero-shot learning by convex combination of semantic embeddings. In: ICLR. (2014) 2, 3, 4, 5, 8, 9, 18
  17. [18]18. Jayaraman, D., Grauman, K.: Zero-shot recognition with unreliable attributes. In: NIPS. (2014) 2, 3
  18. [19]19. Al-Halah, Z., Stiefelhagen, R.: How to transfer? zero-shot object recognition via hierarchical transfer of semantic attributes. In: WACV. (2015) 2, 3
  19. [20]20. Akata, Z., Reed, S., Walter, D., Lee, H., Schiele, B.: Evaluation of output embeddings for fine-grained image classification. In: CVPR. (2015) 2, 3, 8, 21
  20. [21]21. Fu, Y., Hospedales, T.M., Xiang, T., Gong, S.: Transductive multi-view zero-shot learning. TPAMI (2015) 2, 3
  21. [22]22. Fu, Z., Xiang, T., Kodirov, E., Gong, S.: Zero-shot object recognition by semantic manifold distance. In: CVPR. (2015) 2, 3
  22. [23]23. Li, X., Guo, Y., Schuurmans, D.: Semi-supervised zero-shot classification with label representation learning. In: ICCV. (2015) 2, 3
  23. [24]24. Romera-Paredes, B., Torr, P.H.S.: An embarrassingly simple approach to zero-shot learning. In: ICML. (2015) 2, 3, 21, 23
  24. [25]25. Kodirov, E., Xiang, T., Fu, Z., Gong, S.: Unsupervised domain adaptation for zero-shot learning. In: ICCV. (2015) 2, 3
  25. [26]26. Zhang, Z., Saligrama, V.: Zero-shot learning via semantic similarity embedding. In: ICCV. (2015) 2, 3, 4, 18
  26. [27]27. Zhang, Z., Saligrama, V.: Zero-shot learning via joint latent similarity embedding. In: CVPR. (2016) 2, 3
  27. [28]28. Changpinyo, S., Chao, W.L., Gong, B., Sha, F.: Synthesized classifiers for zero-shot learning. In: CVPR. (2016) 2, 3, 4, 5, 6, 7, 8, 9, 12, 18, 23
  28. [29]29. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR. (2009) 2, 8
  29. [31]31. Tang, K.D., Tappen, M.F., Sukthankar, R., Lampert, C.H.: Optimizing one-shot recognition with micro-set learning. In: CVPR. (2010) 3
  30. [32]32. Gan, C., Yang, Y., Zhu, L., Zhao, D., Zhuang, Y.: Recognizing an action using its name: A knowledge-based approach. IJCV (2016) 1-17 3
  31. [33]33. Elhoseiny, M., Saleh, B., Elgammal, A.: Write a classifier: Zero-shot learning using purely textual descriptions. In: ICCV. (2013) 3, 4, 18
  32. [34]34. Lei Ba, J., Swersky, K., Fidler, S., Salakhutdinov, R.: Predicting deep zero-shot convolutional neural networks using textual descriptions. In: ICCV. (2015) 3
  33. [35]35. Scheirer, W.J., de Rezende Rocha, A., Sapkota, A., Boult, T.E.: Toward open set recognition. TPAMI 35 (7) (2013) 1757-1772 3
  34. [36]36. Scheirer, W.J., Jain, L.P., Boult, T.E.: Probability models for open set recognition. TPAMI 36 (11) (2014) 2317-2324 3
  35. [37]37. Jain, L.P., Scheirer, W.J., Boult, T.E.: Multi-class open set recognition using probability of inclusion. In: ECCV. (2014) 393-409 3
  36. [38]38. Lampert, C.H., Nickisch, H., Harmeling, S.: Attribute-based classification for zeroshot visual object categorization. TPAMI 36 (3) (2014) 453-465 4, 5, 7, 8, 18, 21, 23
  37. [40]40. Kriegel, H.P., Kr¨ oger, P., Schubert, E., Zimek, A.: LoOP: local outlier probabilities. In: CIKM. (2009) 7, 18, 19
  38. [42]42. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS. (2013) 7
  39. [43]43. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: CVPR. (2015) 8, 23
  40. [44]44. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. In: ACM Multimedia. (2014) 8
  41. [45]45. Crammer, K., Singer, Y.: On the algorithmic implementation of multiclass kernelbased vector machines. JMLR 2 (2002) 265-292 8
  42. [46]46. Patterson, G., Xu, C., Su, H., Hays, J.: The sun attribute database: Beyond categories for deeper scene understanding. IJCV 108 (1-2) (2014) 59-81 23

Cited by

Sorted by citations

Authors
Chao, W.-L., Changpinyo, S., Gong, B., & Sha, F.
Affiliations
  • University of Southern California (US)
  • University of Central Florida (US)
  • University of California, Los Angeles (US)
Corresponding author
Chao, Wei-Lun
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2016
Pages
52-68
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-319-46475-6_4
OpenAlex
W2400717490
Cited by
616
References
3
Citations by year
2026: 25 · 2025: 40 · 2024: 67 · 2023: 66 · 2022: 78 · 2021: 106 · 2020: 81 · 2019: 75 · 2018: 49 · 2017: 25 · 2016: 3 · 2012: 1
FWCI
27.30
Type
conference paper
Language
en
Open access
Licence
Locations
  • Lecture notes in computer science (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Journal of International Crisis and Risk Communication Research (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Science Foundation (1513966, 1208500, 1566511, 1139148, 1065243, 1451412)
  • Army Research Office (W911NF-15-1-, W911NF, W911NF-15)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
Keywords
generalized zero-shot learning, classifier calibration, calibration factor, performance indicators, semantic embeddings, performance upper bound, ImageNet

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