CoChat
Sign Up
Cited by 449Advanced Neural Network ApplicationsVideo Surveillance and Tracking MethodsVisual Attention and Saliency Detection

XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model

Open access
Cheng, Ho Kei, Schwing, Alexander G.·2022·Lecture Notes in Computer Science
DOI 10.1007/978-3-031-19815-1_37
Open this paper in CoChat — free accountOpen PDF

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 · 10.1007/978-3-031-19815-1_37

Cites

6 of 6 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

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

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: 56

  1. [1]1. Atkinson, R.C., Shiffrin, R.M.: Human memory: A proposed system and its control processes. In: Psychology of learning and motivation, vol. 2, pp. 89-195. Elsevier (1968)
  2. [3]3. Caelles, S., Maninis, K.K., Pont-Tuset, J., Leal-Taix´ e, L., Cremers, D., Van Gool, L.: One-shot video object segmentation. In: CVPR (2017)
  3. [4]4. Chang, A.X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., Xiao, J., Yi, L., Yu, F.: ShapeNet: An Information-Rich 3D Model Repository. In: arXiv:1512.03012 (2015) · arXiv
  4. [5]5. Chen, X., Li, Z., Yuan, Y., Yu, G., Shen, J., Qi, D.: State-aware tracker for realtime video object segmentation. In: CVPR (2020)
  5. [7]7. Cheng, H.K., Chung, J., Tai, Y.W., Tang, C.K.: Cascadepsp: Toward class-agnostic and very high-resolution segmentation via global and local refinement. In: CVPR (2020)
  6. [8]8. Cheng, H.K., Tai, Y.W., Tang, C.K.: Modular interactive video object segmentation: Interaction-to-mask, propagation and difference-aware fusion. In: CVPR (2021)
  7. [9]9. Cheng, H.K., Tai, Y.W., Tang, C.K.: Rethinking space-time networks with improved memory coverage for efficient video object segmentation. In: NeurIPS (2021)
  8. [10]10. Cheng, J., Tsai, Y.H., Hung, W.C., Wang, S., Yang, M.H.: Fast and accurate online video object segmentation via tracking parts. In: CVPR (2018)
  9. [11]11. Cho, K., Van Merri¨ enboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: Encoder-decoder approaches. In: arXiv (2014)
  10. [12]12. Denninger, M., Sundermeyer, M., Winkelbauer, D., Zidan, Y., Olefir, D., Elbadrawy, M., Lodhi, A., Katam, H.: Blenderproc. In: arXiv:1911.01911 (2019) · arXiv
  11. [13]13. Duarte, K., Rawat, Y.S., Shah, M.: Capsulevos: Semi-supervised video object segmentation using capsule routing. In: ICCV (2019)
  12. [14]14. Duke, B., Ahmed, A., Wolf, C., Aarabi, P., Taylor, G.W.: Sstvos: Sparse spatiotemporal transformers for video object segmentation. In: CVPR (2021)
  13. [16]16. Ge, W., Lu, X., Shen, J.: Video object segmentation using global and instance embedding learning. In: CVPR (2021)
  14. [18]18. Hu, L., Zhang, P., Zhang, B., Pan, P., Xu, Y., Jin, R.: Learning position and target consistency for memory-based video object segmentation. In: CVPR (2021)
  15. [19]19. Hu, P., Wang, G., Kong, X., Kuen, J., Tan, Y.P.: Motion-guided cascaded refinement network for video object segmentation. In: CVPR (2018)
  16. [20]20. Hu, Y.T., Huang, J.B., Schwing, A.: Maskrnn: Instance level video object segmentation. In: NIPS (2017)
  17. [21]21. Huang, X., Xu, J., Tai, Y.W., Tang, C.K.: Fast video object segmentation with temporal aggregation network and dynamic template matching. In: CVPR (2020)
  18. [22]22. Jang, W.D., Kim, C.S.: Online video object segmentation via convolutional trident network. In: CVPR (2017)
  19. [23]23. Johnander, J., Danelljan, M., Brissman, E., Khan, F.S., Felsberg, M.: A generative appearance model for end-to-end video object segmentation. In: CVPR (2019)
  20. [24]24. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: ICLR (2015)
  21. [25]25. Li, X., Wei, T., Chen, Y.P., Tai, Y.W., Tang, C.K.: Fss-1000: A 1000-class dataset for few-shot segmentation. In: CVPR (2020)
  22. [26]26. Li, X., Loy, C.C.: Video object segmentation with joint re-identification and attention-aware mask propagation. In: ECCV (2018)
  23. [27]27. Li, Y., Shen, Z., Shan, Y.: Fast video object segmentation using the global context module. In: ECCV (2020)
  24. [28]28. Liang, S., Shen, X., Huang, J., Hua, X.S.: Video object segmentation with dynamic memory networks and adaptive object alignment. In: ICCV (2021)
  25. [29]29. Liang, Y., Li, X., Jafari, N., Chen, J.: Video object segmentation with adaptive feature bank and uncertain-region refinement. In: NeurIPS (2020)
  26. [30]30. Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. ICLR (2019)
  27. [31]31. Lu, X., Wang, W., Martin, D., Zhou, T., Shen, J., Luc, V.G.: Video object segmentation with episodic graph memory networks. In: ECCV (2020)
  28. [33]33. Mao, Y., Wang, N., Zhou, W., Li, H.: Joint inductive and transductive learning for video object segmentation. In: ICCV (2021)
  29. [34]34. Meinhardt, T., Leal-Taix´ e, L.: Make one-shot video object segmentation efficient again. In: NeurIPS (2020)
  30. [35]35. Oh, S.W., Lee, J.Y., Sunkavalli, K., Joo Kim, S.: Fast video object segmentation by reference-guided mask propagation. In: CVPR (2018)
  31. [36]36. Oh, S.W., Lee, J.Y., Xu, N., Kim, S.J.: Video object segmentation using space-time memory networks. In: ICCV (2019)
  32. [37]37. Park, H., Yoo, J., Jeong, S., Venkatesh, G., Kwak, N.: Learning dynamic network using a reuse gate function in semi-supervised video object segmentation. In: CVPR (2021)
  33. [38]38. Patrick, M., Campbell, D., Asano, Y.M., Metze, I.M.F., Feichtenhofer, C., Vedaldi, A., Henriques, J., et al.: Keeping your eye on the ball: Trajectory attention in video transformers. In: NeurIPS (2021)
  34. [39]39. Perazzi, F., Khoreva, A., Benenson, R., Schiele, B., Sorkine-Hornung, A.: Learning video object segmentation from static images. In: CVPR (2017)
  35. [40]40. Perazzi, F., Pont-Tuset, J., McWilliams, B., Van Gool, L., Gross, M., SorkineHornung, A.: A benchmark dataset and evaluation methodology for video object segmentation. In: CVPR (2016)
  36. [41]41. Pont-Tuset, J., Perazzi, F., Caelles, S., Arbel´ aez, P., Sorkine-Hornung, A., Van Gool, L.: The 2017 davis challenge on video object segmentation. In: arXiv:1704.00675 (2017) · arXiv
  37. [42]42. Robinson, A., Lawin, F.J., Danelljan, M., Khan, F.S., Felsberg, M.: Learning fast and robust target models for video object segmentation. In: CVPR (2020)
  38. [43]43. Seong, H., Hyun, J., Kim, E.: Kernelized memory network for video object segmentation. In: ECCV (2020)
  39. [44]44. Seong, H., Oh, S.W., Lee, J.Y., Lee, S., Lee, S., Kim, E.: Hierarchical memory matching network for video object segmentation. In: ICCV (2021)
  40. [45]45. Shi, J., Yan, Q., Xu, L., Jia, J.: Hierarchical image saliency detection on extended cssd. In: TPAMI (2015)
  41. [47]47. Ventura, C., Bellver, M., Girbau, A., Salvador, A., Marques, F., Giro-i Nieto, X.: Rvos: End-to-end recurrent network for video object segmentation. In: CVPR (2019)
  42. [48]48. Voigtlaender, P., Chai, Y., Schroff, F., Adam, H., Leibe, B., Chen, L.C.: Feelvos: Fast end-to-end embedding learning for video object segmentation. In: CVPR (2019)
  43. [50]50. Wang, H., Jiang, X., Ren, H., Hu, Y., Bai, S.: Swiftnet: Real-time video object segmentation. In: CVPR (2021)
  44. [51]51. Wang, L., Lu, H., Wang, Y., Feng, M., Wang, D., Yin, B., Ruan, X.: Learning to detect salient objects with image-level supervision. In: CVPR (2017)
  45. [52]52. Wang, Q., Zhang, L., Bertinetto, L., Hu, W., Torr, P.H.: Fast online object tracking and segmentation: A unifying approach. In: CVPR (2019)
  46. [53]53. Wang, Z., Xu, J., Liu, L., Zhu, F., Shao, L.: Ranet: Ranking attention network for fast video object segmentation. In: ICCV (2019)
  47. [54]54. Xie, H., Yao, H., Zhou, S., Zhang, S., Sun, W.: Efficient regional memory network for video object segmentation. In: CVPR (2021)
  48. [55]55. Xiong, Y., Zeng, Z., Chakraborty, R., Tan, M., Fung, G., Li, Y., Singh, V.: Nystr¨ omformer: A nystr¨ om-based algorithm for approximating self-attention. In: AAAI (2021)
  49. [56]56. Xu, K., Wen, L., Li, G., Bo, L., Huang, Q.: Spatiotemporal cnn for video object segmentation. In: CVPR (2019)
  50. [57]57. Xu, N., Yang, L., Fan, Y., Yue, D., Liang, Y., Yang, J., Huang, T.: Youtube-vos: A large-scale video object segmentation benchmark. In: ECCV (2018)
  51. [58]58. Xu, X., Wang, J., Li, X., Lu, Y.: Reliable propagation-correction modulation for video object segmentation. In: AAAI (2022)
  52. [59]59. Yang, Z., Wei, Y., Yang, Y.: Collaborative video object segmentation by foreground-background integration. In: ECCV (2020)
  53. [60]60. Yang, Z., Wei, Y., Yang, Y.: Associating objects with transformers for video object segmentation. In: NeurIPS (2021)
  54. [62]62. Zeng, Y., Zhang, P., Zhang, J., Lin, Z., Lu, H.: Towards high-resolution salient object detection. In: ICCV (2019)
  55. [63]63. Zhang, L., Lin, Z., Zhang, J., Lu, H., He, Y.: Fast video object segmentation via dynamic targeting network. In: ICCV (2019)
  56. [64]64. Zhang, Y., Wu, Z., Peng, H., Lin, S.: A transductive approach for video object segmentation. In: CVPR (2020)

Cited by

Sorted by citations

Authors
Cheng, H. K., & Schwing, A. G.
Affiliations
University of Illinois Urbana-Champaign (US)
Corresponding author
Cheng, Ho Kei
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer Nature Switzerland
Published
2022
Pages
640-658
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-031-19815-1_37
OpenAlex
W4312396403
Cited by
449
References
6
Citations by year
2026: 86 · 2025: 151 · 2024: 145 · 2023: 66 · 2022: 1
FWCI
32.90
Type
conference paper
Language
en
Open access
Licence
Locations
  • Lecture notes in computer science (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
Keywords
video object segmentation, sensory memory, working memory, long-term memory, memory consolidation, unified memory architecture

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