Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 435

The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.

ActionFormer: Localizing Moments of Actions with Transformers

Open access
Zhang, Chen-Lin, Wu, Jianxin, Li, Yin·2022·Lecture Notes in Computer Science
DOI 10.1007/978-3-031-19772-7_29
Open this paper in CoChat — free account

Overview

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 · 10.1007/978-3-031-19772-7_29

Cites

19 of 19 references verified0 citation errors · 0 not checkable

Statistics

Verification

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.

Citations

Cites

Research works printed but not linked: 65

  1. Alwassel, H., Giancola, S., Ghanem, B.: TSP: Temporally-sensitive pretraining of video encoders for localization tasks. In: International Conference on Computer Vision Workshops, pp. 1–11 (2021) · DOI
  2. Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lučić, M., Schmid, C.: ViViT: a video vision transformer. In: International Conference on Computer Vision (2021) · DOI · Paper page
  3. Bai, Y., Wang, Y., Tong, Y., Yang, Y., Liu, Q., Liu, J.: Boundary content graph neural network for temporal action proposal generation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12373, pp. 121–137. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58604-1_8 · DOI
  4. Beltagy, I., Peters, M.E., Cohan, A.: LongFormer: the long-document transformer. arXiv:2004.05150 (2020) · arXiv
  5. Buch, S., Escorcia, V., Shen, C., Ghanem, B., Carlos Niebles, J.: SST: Single-stream temporal action proposals. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2911–2920 (2017) · DOI · Paper page
  6. Caba Heilbron, F., Carlos Niebles, J., Ghanem, B.: Fast temporal activity proposals for efficient detection of human actions in untrimmed videos. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1914–1923 (2016) · DOI · Paper page
  7. Chang, S., Wang, P., Wang, F., Li, H., Feng, J.: Augmented transformer with adaptive graph for temporal action proposal generation. arXiv preprint arXiv:2103.16024 (2021) · DOI
  8. Chao, Y.W., Vijayanarasimhan, S., Seybold, B., Ross, D.A., Deng, J., Sukthankar, R.: Rethinking the Faster-RCNN architecture for temporal action localization. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1130–1139 (2018) · DOI · Paper page
  9. Cheng, B., Schwing, A.G., Kirillov, A.: Per-pixel classification is not all you need for semantic segmentation. In: Advances in Neural Information Processing Systems (2021)
  10. Damen, D., et al.: Rescaling egocentric vision. arXiv preprint arXiv:2006.13256 (2020) · arXiv
  11. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: North American Chapter of the Association for Computational Linguistics, pp. 4171–4186 (2019)
  12. Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., Tian, Q.: CenterNet: keypoint triplets for object detection. In: International Conference on Computer Vision, pp. 6569–6578 (2019) · DOI · Paper page
  13. Escorcia, V., Caba Heilbron, F., Niebles, J.C., Ghanem, B.: DAPs: deep action proposals for action understanding. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 768–784. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_47 · DOI · Paper page
  14. Girdhar, R., Carreira, J., Doersch, C., Zisserman, A.: Video action transformer network. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 244–253 (2019) · DOI
  15. Gong, G., Zheng, L., Mu, Y.: Scale matters: temporal scale aggregation network for precise action localization in untrimmed videos. In: International Conference on Multimedia and Expo, pp. 1–6. IEEE (2020) · DOI
  16. Idrees, H., et al.: The THUMOS challenge on action recognition for videos “in the wild’’. Comput. Vis. Image Under. 155, 1–23 (2017) · DOI · Paper page
  17. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations, pp. 1–11 (2015)
  18. Li, S., et al.: Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
  19. Li, X., et al.: Deep concept-wise temporal convolutional networks for action localization. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 4004–4012 (2020) · DOI
  20. Lin, C., et al.: Fast learning of temporal action proposal via dense boundary generator. In: AAAI, pp. 11499–11506 (2020) · DOI
  21. Lin, C., et al.: Learning salient boundary feature for anchor-free temporal action localization. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3320–3329 (2021) · DOI · Paper page
  22. Lin, T., Liu, X., Li, X., Ding, E., Wen, S.: BMN: boundary-matching network for temporal action proposal generation. In: International Conference on Computer Vision, pp. 3889–3898 (2019) · DOI · Paper page
  23. Lin, T., Zhao, X., Shou, Z.: Single shot temporal action detection. In: ACM International Conference on Multimedia, pp. 988–996 (2017) · DOI · Paper page
  24. Lin, T., Zhao, X., Su, H., Wang, C., Yang, M.: BSN: boundary sensitive network for temporal action proposal generation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 3–21. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01225-0_1 · DOI · Paper page
  25. Liu, D., Jiang, T., Wang, Y.: Completeness modeling and context separation for weakly supervised temporal action localization. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1298–1307 (2019) · DOI
  26. Liu, L., Liu, X., Gao, J., Chen, W., Han, J.: Understanding the difficulty of training transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 5747–5763 (2020) · DOI
  27. Liu, Q., Wang, Z.: Progressive boundary refinement network for temporal action detection. In: AAAI, vol. 34, pp. 11612–11619 (2020) · DOI
  28. Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2 · DOI · Paper page
  29. Liu, X., Hu, Y., Bai, S., Ding, F., Bai, X., Torr, P.H.: Multi-shot temporal event localization: a benchmark. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 12596–12606 (2021) · DOI
  30. Liu, X., Wang, Q., Hu, Y., Tang, X., Bai, S., Bai, X.: End-to-end temporal action detection with transformer. arXiv preprint arXiv:2106.10271 (2021) · DOI · Paper page
  31. Liu, Y., Ma, L., Zhang, Y., Liu, W., Chang, S.F.: Multi-granularity generator for temporal action proposal. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3604–3613 (2019) · DOI
  32. Long, F., Yao, T., Qiu, Z., Tian, X., Luo, J., Mei, T.: Gaussian temporal awareness networks for action localization. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 344–353 (2019) · DOI · Paper page
  33. Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
  34. Qing, Z., et al.: Temporal context aggregation network for temporal action proposal refinement. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 485–494 (2021) · DOI
  35. Qiu, Z., Yao, T., Mei, T.: Learning spatio-temporal representation with pseudo-3D residual networks. In: IEEE Conference on Computer Vision , pp. 5533–5541 (2017) · DOI · Paper page
  36. Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: a metric and a loss for bounding box regression. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 658–666 (2019) · DOI · Paper page
  37. Shou, Z., Chan, J., Zareian, A., Miyazawa, K., Chang, S.F.: CDC: convolutional-de-convolutional networks for precise temporal action localization in untrimmed videos. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5734–5743 (2017) · DOI · Paper page
  38. Shou, Z., Wang, D., Chang, S.F.: Temporal action localization in untrimmed videos via multi-stage CNNs. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1049–1058 (2016) · DOI · Paper page
  39. Sridhar, D., Quader, N., Muralidharan, S., Li, Y., Dai, P., Lu, J.: Class semantics-based attention for action detection. In: IEEE Conference on Computer Vision, pp. 13739–13748 (2021) · DOI
  40. Tan, J., Tang, J., Wang, L., Wu, G.: Relaxed transformer decoders for direct action proposal generation. In: IEEE Conference on Computer Vision, pp. 13526–13535 (2021) · DOI
  41. Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jégou, H.: Training data-efficient image transformers & distillation through attention. In: International Conference on Machine Learning, pp. 10347–10357 (2021)
  42. Touvron, H., Cord, M., Sablayrolles, A., Synnaeve, G., Jégou, H.: Going deeper with image transformers. In: IEEE Conference on Computer Vision (2021) · DOI · Paper page
  43. Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., Paluri, M.: A closer look at spatiotemporal convolutions for action recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6450–6459 (2018) · DOI
  44. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
  45. Wang, L., et al.: Temporal segment networks: towards good practices for deep action recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 20–36. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_2 · DOI · Paper page
  46. Wang, L., Yang, H., Wu, W., Yao, H., Huang, H.: Temporal action proposal generation with transformers. arXiv preprint arXiv:2105.12043 (2021) · arXiv
  47. Wang, S., Li, B., Khabsa, M., Fang, H., Ma, H.: LinFormer: self-attention with linear complexity. arXiv preprint arXiv:2006.04768 (2020) · arXiv
  48. Wang, T., Yuan, L., Chen, Y., Feng, J., Yan, S.: PnP-DETR: towards efficient visual analysis with transformers. In: IEEE Conference on Computer Vision, pp. 4661–4670 (2021) · DOI
  49. Wang, Y., et al.: End-to-end video instance segmentation with transformers. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 8741–8750 (2021) · DOI · Paper page
  50. Xiao, T., Singh, M., Mintun, E., Darrell, T., Dollár, P., Girshick, R.: Early convolutions help transformers see better. In: Advances in Neural Information Processing Systems (2021)
  51. Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: SegFormer: simple and efficient design for semantic segmentation with transformers. In: Advances in Neural Information Processing Systems (2021)
  52. Xu, M., Zhao, C., Rojas, D.S., Thabet, A., Ghanem, B.: G-TAD: sub-graph localization for temporal action detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 10156–10165 (2020) · DOI · Paper page
  53. Yang, J., et al.: Focal self-attention for local-global interactions in vision transformers. In: Advances in Neural Information Processing Systems (2021)
  54. Yang, L., Peng, H., Zhang, D., Fu, J., Han, J.: Revisiting anchor mechanisms for temporal action localization. IEEE Trans. Image Process. 29, 8535–8548 (2020) · DOI
  55. Yang, Z., Qin, J., Huang, D.: AcgNet: action complement graph network for weakly-supervised temporal action localization. In: AAAI, vol. 36–3, pp. 3090–3098 (2022) · DOI
  56. Yuan, L., et al.: Tokens-to-token ViT: training vision transformers from scratch on ImageNet. In: IEEE Conference on Computer Vision (2021) · DOI · Paper page
  57. Zeng, R., et al.: Graph convolutional networks for temporal action localization. In: IEEE Conference on Computer Vision, pp. 7094–7103 (2019) · DOI · Paper page
  58. Zeng, R., Xu, H., Huang, W., Chen, P., Tan, M., Gan, C.: Dense regression network for video grounding. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 10287–10296 (2020) · DOI · Paper page
  59. Zhang, S., Chi, C., Yao, Y., Lei, Z., Li, S.Z.: Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 9759–9768 (2020) · DOI · Paper page
  60. Zhao, C., Thabet, A.K., Ghanem, B.: Video self-stitching graph network for temporal action localization. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 13658–13667 (2021) · DOI
  61. Zhao, P., Xie, L., Ju, C., Zhang, Y., Wang, Y., Tian, Q.: Bottom-up temporal action localization with mutual regularization. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 539–555. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58598-3_32 · DOI
  62. Zhao, Y., Xiong, Y., Wang, L., Wu, Z., Tang, X., Lin, D.: Temporal action detection with structured segment networks. In: IEEE Conference on Computer Vision, pp. 2914–2923 (2017) · DOI · Paper page
  63. Zhao, Y., et al.: CUHK & ETHZ & SIAT submission to ActivityNet challenge 2017. arXiv preprint arXiv:1710.08011 (2017) · arXiv
  64. 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, pp. 1–11 (2021)
  65. Zhu, Z., Tang, W., Wang, L., Zheng, N., Hua, G.: Enriching local and global contexts for temporal action localization. In: International Conference on Computer Vision, pp. 13516–13525 (2021) · DOI

Cited by

See all 435

Metadata

Authors
Zhang, C.-L., Wu, J., & Li, Y.
Venue
Lecture Notes in Computer Science
Publisher
Springer Nature Switzerland
Published
2022
Pages
492-510
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-031-19772-7_29
OpenAlex
W4312508181
Cited by
435
References
19
FWCI
34.77
Type
conference paper
Language
en
Open access
green
Licence

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

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.