CoChat
Sign Up
Cited by 754Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot LearningAdvanced Image and Video Retrieval Techniques

Deep learning in computer vision: A critical review of emerging techniques and application scenarios

Open access
Chai, Junyi, Zeng, Hao, Li, Anming, Ngai, Eric W.T.·2021·Machine Learning with Applications
DOI 10.1016/j.mlwa.2021.100134
Open this paper in CoChat — free accountOpen PDF

Abstract

Deep learning has been overwhelmingly successful in computer vision (CV), natural language processing, and video/speech recognition. In this paper, our focus is on CV. We provide a critical review of recent achievements in terms of techniques and applications. We identify eight emerging techniques, investigate their origins and updates, and finally emphasize their applications in four key scenarios, including recognition, visual tracking, semantic segmentation, and image restoration. We recognize three development stages in the past decade and emphasize research trends for future works. The summarizations, knowledge accumulations, and creations could benefit researchers in the academia and participators in the CV industries.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.mlwa.2021.100134

Cites

22 of 22 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 72

  1. Agarwal, S., Farid, H., Fried, O., & Agrawala, M. (2020). Detecting deep-fake videos from phoneme-viseme mismatches. In Proceedings of the 2020 ieee/cvf conference on computer vision and pattern recognition workshops (cvprw) (pp. 2814-2822). USA: http://dx.doi.org/10.1109/CVPRW50498.2020.00338. · DOI · Paper page
  2. Al-Shabandar, R., Jaddoa, A., Liatsis, P., & Hussain, A. J. (2021). A deep gated recur- rent neural network for petroleum production forecasting. Machine Learning with Applications, 3, Article 100013. http://dx.doi.org/10.1016/j.mlwa.2020.100013. · DOI
  3. Altan, A., Karasu, S., & Zio, E. (2021). A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer. Applied Soft Computing, 100, Article 106996. http://dx.doi. org/10.1016/j.asoc.2020.106996. · DOI
  4. Burger, H. C., Schuler, C. J., & Harmeling, S. (2012). Image denoising: Can plain neural networks compete with BM3D?. In Proceedings of the 2012 ieee conference on computer vision and pattern recognition (pp. 2392-2399). Rhode Island: http: //dx.doi.org/10.1109/CVPR.2012.6247952. · DOI · Paper page
  5. Cai, Z., Fan, Q., Feris, R. S., & Vasconcelos, N. (2016). A unified multiscale deep convolutional neural network for fast object detection. In Proceedings of the 14th european conference on computer vision (eccv) (pp. 354-370). Netherlands: http: //dx.doi.org/10.1007/978-3-319-46493-0_22. · DOI · Paper page
  6. Chai, J., & Li, A. (2019). Deep learning in natural language processing: A state-of-the-art survey. In Proceedings of the 2019 international conference on machine learning and cybernetics (icmlc) (pp. 1-6). Japan: http://dx.doi.org/10.1109/ICMLC48188.2019. 8949185. · DOI
  7. Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2015). Semantic image segmentation with deep convolutional nets and fully connected CRFs. arxiv preprint arXiv:1412.7062 [Cs]. http://arxiv.org/abs/1412.7062. · arXiv
  8. Chen, L. C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the 15th european conference on computer vision (eccv) (pp. 801-818). Germany: http://dx.doi.org/10.1007/978-3-030-01234-2_49. · DOI · Paper page
  9. Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. (2007). Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Transactions on Image Processing, 16(8), 2080-2095. http://dx.doi.org/10.1109/TIP.2007.901238. · DOI · Paper page
  10. Dai, J., Li, Y., He, K., & Sun, J. (2016). R-FCN: Object detection via region-based fully convolutional networks. In Proceedings of the 30th international conference on neural information processing systems (nips), Spain, (pp. 379-387).
  11. Dambrogio, J., Ghassaei, A., Smith, D. S., Jackson, H., Demaine, M. L., Davis, G., Mills, D., Ahrendt, R., Akkerman, N., van der Linden, D., & Demaine, E. D. (2021). Unlocking history through automated virtual unfolding of sealed documents imaged by X-ray microtomography. Nature Communications, 12(1), 1184. http://dx.doi.org/ 10.1038/s41467-021-21326-w. · DOI
  12. Dosovitskiy, A., & Brox, T. (2016). Inverting visual representations with convolutional networks. In Proceedings of the 2016 ieee conference on computer vision and pattern recognition (cvpr) (pp. 4829-4837). USA: http://dx.doi.org/10.1109/CVPR.2016. 522. · DOI
  13. Fu, C.-Y., Liu, W., Ranga, A., Tyagi, A., & Berg, A. C. (2017). DSSD: Deconvolutional single shot detector. arxiv preprint arxiv:1701.06659 [Cs]. http://arxiv.org/abs/ 1701.06659. · arXiv
  14. Gando, G., Yamada, T., Sato, H., Oyama, S., & Kurihara, M. (2016). Fine-tuning deep convolutional neural networks for distinguishing illustrations from photographs. Expert Systems with Applications, 66, 295-301. http://dx.doi.org/10.1016/j.eswa. 2016.08.057. · DOI
  15. Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the 2014 ieee conference on computer vision and pattern recognition (pp. 580-587). USA: http://dx.doi.org/10.1109/CVPR.2014. · DOI · Paper page
  16. Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187, 27-48. http://dx.doi.org/ 10.1016/j.neucom.2015.09.116. · DOI · Paper page
  17. Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., & Xu, C. (2020). GhostNet: More features from cheap operations. In Proceedings of the 2020 ieee/cvf conference on computer vision and pattern recognition (cvpr) (pp. 1577-1586). USA: http://dx.doi.org/10. 1109/CVPR42600.2020.00165. · DOI · Paper page
  18. He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask r-cnn. In Proceedings of the 2017 ieee international conference on computer vision (iccv) (pp. 2961-2969). Italy: http://dx.doi.org/10.1109/ICCV.2017.322. · DOI · Paper page
  19. He, K., Zhang, X., Ren, S., & Sun, J. (2015). Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Anal- ysis and Machine Intelligence, 37(9), 1904-1916. http://dx.doi.org/10.1109/TPAMI. 2015.2389824. · DOI · Paper page
  20. Hong, S., Noh, H., & Han, B. (2015). Decoupled deep neural network for semi- supervised semantic segmentation. In Proceedings of the 28th international conference on neural information processing systems (nips), Canada (pp. 1495-1503). · DOI · Paper page
  21. Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 [Cs]. http://arxiv.org/abs/1704.04861. · arXiv
  22. Huang, J., Chai, J., & Cho, S. (2020). Deep learning in finance and banking: A literature review and classification. Frontiers of Business Research in China, 14, 1-24. http://dx.doi.org/10.1186/s11782-020-00082-6. · DOI
  23. Huang, Y., Cheng, Y., Bapna, A., Firat, O., Chen, D., Chen, M., Lee, H., Ngiam, J., Le, Q. V., & Wu, Y. (2019). Gpipe: Efficient training of giant neural networks using pipeline parallelism. In Proceedings of the 33rd conference on neural information processing systems (neurips), Vol. 32, Canada, (pp. 103-112).
  24. Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the 2017 ieee conference on computer vision and pattern recognition (cvpr) (pp. 2261-2269). USA: http://dx.doi.org/10.1109/ CVPR.2017.243. · DOI · Paper page
  25. Kim, S. Y., Oh, J., & Kim, M. (2020). Jsi-gan: Gan-based joint super-resolution and inverse tone-mapping with pixel-wise task-specific filters for uhd hdr video. Pro- ceedings of the AAAI Conference on Artificial Intelligence, USA, 34(07), 11287-11295. http://dx.doi.org/10.1609/aaai.v34i07.6789. · DOI
  26. Krull, A., Buchholz, T.-O., & Jug, F. (2019). Noise2Void-LEarning denoising from single noisy images. In Proceedings of the 2019 ieee/cvf conference on computer vision and pattern recognition (cvpr) (pp. 2124-2132). USA: http://dx.doi.org/10.1109/ CVPR.2019.00223. · DOI
  27. Kumar, A., Walia, G. S., & Sharma, K. (2020). Recent trends in multicue based visual tracking: A review. Expert Systems with Applications, 162, Article 113711. http://dx.doi.org/10.1016/j.eswa.2020.113711. · DOI
  28. Lai, W.-S., Huang, J. B., Ahuja, N., & Yang, M. H. (2017). Deep laplacian pyramid networks for fast and accurate super-resolution. In Proceedings of the 2017 ieee conference on computer vision and pattern recognition (cvpr) (pp. 5835-5843). USA: http://dx.doi.org/10.1109/CVPR.2017.618. · DOI
  29. Lapuschkin, S., Binder, A., Montavon, G., Muller, K.-R., & Samek, W. (2016). The LRP toolbox for artificial neural networks. Journal of Machine Learning Research, 17(114), 1-5, http://jmlr.org/papers/v17/15-618.html.
  30. Law, H., & Deng, J. (2018). CornerNet: Detecting objects as paired keypoints. In Proceedings of the 15th european conference on computer vision (eccv), Vol. 11218 (pp. 734-750). Germany: http://dx.doi.org/10.1007/978-3-030-01264-9_45. · DOI · Paper page
  31. Lehtinen, J., Munkberg, J., Hasselgren, J., Laine, S., Karras, T., Aittala, M., & Aila, T. (2018). Noise2Noise: Learning image restoration without clean data. In Proceedings of the 35th international conference on machine learning (icml), Vol. 80, (Stockholm SWEDEN) (pp. 2965-2974). · DOI · Paper page
  32. Li, B., Yan, J., Wu, W., Zhu, Z., & Hu, X. (2018). High performance visual tracking with siamese region proposal network. In Proceedings of the 2018 ieee/cvf conference on computer vision and pattern recognition (cvpr) (pp. 8971-8980). USA: http://dx. doi.org/10.1109/CVPR.2018.00935. · DOI · Paper page
  33. Lienhart, R., & Maydt, J. (2002). An extended set of haar-like features for rapid object detection. In Proceedings. Proceedings of the international conference on image processing, Vol. 1 (pp. I-900-I-903). USA: http://dx.doi.org/10.1109/ICIP.2002. 1038171. · DOI · Paper page
  34. Lin, M., Chen, Q., & Yan, S. (2014). Network in network. In Proceedings of the 2014 international conference on learning representations (iclr), Canada.
  35. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). SSD: Single shot multibox detector. In Proceedings of the european conference on computer vision (eccv), Vol. 9905 (pp. 21-37). Netherlands: http://dx.doi.org/10. 1007/978-3-319-46448-0_2. · DOI · Paper page
  36. Long, X., Deng, K., Wang, G., Zhang, Y., Dang, Q., Gao, Y., Shen, H., Ren, J., Han, S., Ding, E., & Wen, S. (2020). PP-YOLO: An effective and efficient implementation of object detector. arXiv preprint arXiv:2007.12099 [Cs]. http://arxiv.org/abs/2007. 12099. · arXiv
  37. Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the 2015 ieee conference on computer vision and pattern recognition (cvpr) (pp. 3431-3440). USA: http://dx.doi.org/10.1109/cvpr. 2015.7298965. · DOI · Paper page
  38. Mehrotra, R., Ansari, M. A., Agrawal, R., & Anand, R. S. (2020). A transfer learn- ing approach for AI-based classification of brain tumors. Machine Learning with Applications, 2, Article 100003. http://dx.doi.org/10.1016/j.mlwa.2020.100003. · DOI
  39. Milletari, F., Navab, N., & Ahmadi, S.-A. (2016). V-NEt: Fully convolutional neural networks for volumetric medical image segmentation. In Proceedings of the 2016 fourth international conference on 3d vision (3dv) (pp. 565-571). USA: http://dx.doi. org/10.1109/3DV.2016. · DOI · Paper page
  40. Muzammel, M., Salam, H., Hoffmann, Y., Chetouani, M., & Othmani, A. (2020). AudVowelConsNet: A phoneme-level based deep CNN architecture for clinical depression diagnosis. Machine Learning with Applications, 2, Article 100005. http: //dx.doi.org/10.1016/j.mlwa.2020.100005. · DOI
  41. Noh, H., Hong, S., & Han, B. (2015). Learning deconvolution network for semantic segmentation. In Proceedings of the 2015 ieee international conference on computer vision (iccv) (pp. 1520-1528). Chile: http://dx.doi.org/10.1109/iccv.2015.178. · DOI · Paper page
  42. Pinheiro, P. O., Lin, T.-Y., Collobert, R., & Dollàr, P. (2016). Learning to refine object segments. (pp. 75-91). Netherlands: http://dx.doi.org/10.1007/978-3-319-46448- 0_5. · DOI
  43. Qi, Y., Zhang, S., Qin, L., Yao, H., Huang, Q., Lim, J., & Yang, M. H. (2016). Hedged deep tracking. In Proceedings of the ieee conference on computer vision and pattern recognition (cvpr) (pp. 4303-4311). USA: http://dx.doi.org/10.1109/CVPR.2016. 466. · DOI
  44. Radosavovic, I., Kosaraju, R. P., Girshick, R., He, K., & Dollár, P. (2020). Designing network design spaces. In Proceedings of the 2020 ieee/cvf conference on computer vision and pattern recognition (cvpr) (pp. 10425-10433). USA: http://dx.doi.org/10. 1109/CVPR42600.2020.01044. · DOI · Paper page
  45. Rai, H. M., & Chatterjee, K. (2020). Detection of brain abnormality by a novel lu-net deep neural CNN model from MR images. Machine Learning with Applications, 2, Article 100004. http://dx.doi.org/10.1016/j.mlwa.2020.100004. · DOI
  46. Redmon, J., & Farhadi, A. (2017). YOLO9000: better, faster, stronger. Proceedings of the 2017 ieee conference on computer vision and pattern recognition (cvpr), 6517-6525. http://dx.doi.org/10.1109/CVPR.2017.690. · DOI · Paper page
  47. Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767. http://arxiv.org/abs/1804.02767. · arXiv
  48. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the 2018 ieee conference on computer vision and pattern recognition (cvpr) (pp. 4510-4520). USA: http://dx. doi.org/10.1109/CVPR.2018.00474. · DOI · Paper page
  49. Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large- scale image recognition. In Proceedings of the 3rd international conference on learning representations (iclr2015), USA.
  50. Song, Y., Ma, C., Wu, X., Gong, L., Bao, L., Zuo, W., Shen, C., Lau, R. W., & Yang, M. H. (2018). Vital: Visual tracking via adversarial learning. In Proceedings of the ieee conference on computer vision and pattern recognition (cvpr) (pp. 8990-8999). USA: http://dx.doi.org/10.1109/CVPR.2018.00937. · DOI
  51. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the 2015 ieee conference on computer vision and pattern recognition (cvpr) (pp. 1-9). USA: http://dx.doi.org/10.1109/CVPR.2015.7298594. · DOI · Paper page
  52. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the 2016 IEEE conference on computer vision and pattern recognition (cvpr) (pp. 2818-2826). USA: http://dx.doi.org/10.1109/CVPR.2016.308. · DOI · Paper page
  53. Ulyanov, D., Vedaldi, A., & Lempitsky, V. (2018). Deep image prior. International Journal of Computer Vision, 128(7), 1867-1888. http://dx.doi.org/10.1007/s11263- 020-01303-4. · DOI
  54. Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 ieee computer society conference on computer vision and pattern recognition (cvpr). USA: http://dx.doi.org/10.1109/CVPR.2001.990517. · DOI · Paper page
  55. Walia, G. S., & Kapoor, R. (2016). Recent advances on multicue object tracking: A survey. The Artificial Intelligence Review, 46(1), 1-39. http://dx.doi.org/10.1007/ s10462-015-9454-6. · DOI
  56. Wang, L., Kim, T. K., & Yoon, K. J. (2020). Eventsr: From asynchronous events to image reconstruction, restoration, and super-resolution via end-to-end adversarial learning. In Proceedings of the 2020 ieee/cvf conference on computer vision and pattern recognition (cvpr) (pp. 8312-8322). USA: http://dx.doi.org/10.1109/cvpr42600. 2020.00834. · DOI
  57. Wang, N., Li, S., Gupta, A., & Yeung, D. Y. (2015). Transferring rich feature hierarchies for robust visual tracking. arXiv preprint arXiv:1501.04587 http://arxiv.org/abs/ 1501.04587. · arXiv
  58. Wang, L., Liu, T., Wang, G., Chan, K. L., & Yang, Q. (2015). Video tracking using learned hierarchical features. IEEE Transactions on Image Processing, 24(4), 1424-1435. http://dx.doi.org/10.1109/TIP.2015.2403231. · DOI
  59. Wang, N., & Yeung, D. Y. (2013). Learning a deep compact image representation for visual Tracking. In Proceedings of the 27th annual conference on neural information processing systems, Vol. 1, USA (pp. 809-817).
  60. Xie, J., Xu, L., & Chen, E. (2012). Image denoising and inpainting with deep neural networks. Advances in Neural Information Processing Systems, 25, 341-349.
  61. Xu, T., Feng, Z.-H., Wu, X.-J., & Kittler, J. (2019). Joint group feature selection and discriminative filter learning for robust visual object tracking. In Proceedings of the 2019 ieee/cvf international conference on computer vision (iccv) (pp. 7949-7959). Korea (South): http://dx.doi.org/10.1109/ICCV.2019.00804. · DOI
  62. Xu, S., Wang, J., Shou, W., Ngo, T., Sadick, A.-M., & Wang, X. (2020). Computer vision techniques in construction: A critical review. Archives of Computational Methods in Engineering, http://dx.doi.org/10.1007/s11831-020-09504-3, 2020. · DOI · Paper page
  63. Yang, Z., Liu, S., Hu, H., Wang, L., & Lin, S. (2019). RepPoints: Point set representation for object detection. In Proceedings of the 2019 ieee/cvf international conference on computer vision (iccv) (pp. 9656-9665). Korea (South): http://dx.doi.org/10.1109/ ICCV.2019.00975. · DOI · Paper page
  64. Ye, L., Liu, Z., & Wang, Y. (2020). Dual convolutional LSTM network for referring image segmentation. IEEE Transactions on Multimedia, 22(12), 3224-3235. http: //dx.doi.org/10.1109/TMM.2020.2971171. · DOI
  65. Young, A. L., & Quan-Haase, A. (2013). Privacy protection strategies on facebook: The internet privacy paradox revisited. Information, Communication & Society, 16(4), 479-500. http://dx.doi.org/10.1080/1369118X.2013.777757. · DOI · Paper page
  66. Yun, S., Choi, J., Yoo, Y., Yun, K., & Choi, J. Y. (2017). Action-decision networks for visual tracking with deep reinforcement learning. In Proceedings of the 2017 ieee conference on computer vision and pattern recognition (cvpr) (pp. 1349-1358). USA: http://dx.doi.org/10.1109/CVPR.2017.148. · DOI
  67. Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional net- works. In Proceedings of the 13th european conference on computer vision (eccv), Vol. 8689 (pp. 818-833). Switzerland: http://dx.doi.org/10.1007/978-3-319-10590-1_ · DOI · Paper page
  68. Zhang, C., Lin, G., Liu, F., Yao, R., & Shen, C. (2019). Canet: Class-agnostic seg- mentation networks with iterative refinement and attentive few-shot learning. In Proceedings of the 2019 ieee/cvf conference on computer vision and pattern recognition (cvpr) (pp. 5212-5221). USA: http://dx.doi.org/10.1109/cvpr.2019.00536. · DOI
  69. Zhang, K., Liu, Q., Wu, Y., & Yang, M. H. (2016). Robust visual tracking via convolutional networks without training. IEEE Transactions on Image Processing, 25(4), 1779-1792. http://dx.doi.org/10.1109/TIP.2016.2531283. · DOI
  70. Zhao, Z., Jiao, L., Zhao, J., Gu, J., & Zhao, J. (2017). Discriminant deep belief network for high-resolution SAR image classification. Pattern Recognition, 61, 686-701. http://dx.doi.org/10.1016/j.patcog.2016.05.028. · DOI
  71. Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. (2020). Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Transac- tions on Medical Imaging, 39(6), 1856-1867. http://dx.doi.org/10.1109/TMI.2019. 2959609. · DOI
  72. Zhou, X., Wang, D., & Krähenbühl, P. (2019). Objects as points. arXiv preprint arXiv: 1904.07850 [Cs]. http://arxiv.org/abs/1904.07850. · arXiv

Cited by

Sorted by citations

Authors
Chai, J., Zeng, H., Li, A., & Ngai, E. W. T.
Affiliations
  • Beijing Normal-Hong Kong Baptist University (CN)
  • Hong Kong Polytechnic University (HK)
Corresponding author
Chai, Junyi
ORCID
Venue
Machine Learning with Applications
Publisher
Elsevier BV
Published
2021-12
Volume
6
Pages
100134
ISSN
2666-8270 (ISSN-L)
DOI
10.1016/j.mlwa.2021.100134
OpenAlex
W3195997474
Cited by
754
References
22
Citations by year
2026: 65 · 2025: 206 · 2024: 246 · 2023: 165 · 2022: 65 · 2021: 7
FWCI
43.43
Type
journal article
Language
en
Open access
Licence
Locations
  • Machine Learning with Applications (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Hong Kong Baptist University
  • Beijing Normal University
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
Keywords
deep learning, computer vision, image recognition, visual object tracking, semantic segmentation, image restoration, research trends

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review