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Attention based Memory video portrait matting

arXiv 2203.06890 ↗First posted 14 Mar 2022cs.CV cs.LGcited by 0

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Abstract (arXiv)

We proposed a novel trimap free video matting method based on the attention mechanism. By the nature of the problem, most existing approaches use either multiple computational expansive modules or complex algorithms to exploit temporal information fully. We designed a temporal aggregation module to compute the temporal coherence between the current frame and its two previous frames.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 36 references that are in the map. First posted on arXiv 14 Mar 2022.

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25 of 36 references.

References · 25

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  3. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  4. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,161
  5. 2018BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentationcited by 645
  6. 2018Context Encoding for Semantic Segmentationcited by 290
  7. 2016One-Shot Video Object Segmentationcited by 235
  8. 2019FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentationcited by 178
  9. 2020Real-Time High-Resolution Background Mattingcited by 173
  10. 2017Deep Image Mattingcited by 150
  11. 2015Delving Deeper into Convolutional Networks for Learning Video Representationscited by 148
  12. 2021Robust High-Resolution Video Matting with Temporal Guidancecited by 137
13 more
  1. 2020Context Prior for Scene Segmentationcited by 121
  2. 2015Ask Me Anything: Dynamic Memory Networks for Natural Language Processingcited by 117
  3. 2018Learning a Discriminative Feature Network for Semantic Segmentationcited by 114
  4. 2020Background Matting: The World is Your Green Screencited by 105
  5. 2018VideoMatch: Matching based Video Object Segmentationcited by 90
  6. 2018Fast End-to-End Trainable Guided Filtercited by 50
  7. 2018MaskRNN: Instance Level Video Object Segmentationcited by 48
  8. 2019Disentangled Image Mattingcited by 47
  9. 2021Deep Video Matting via Spatio-Temporal Alignment and Aggregationcited by 41
  10. 2019Fast User-Guided Video Object Segmentation by Interaction-and-Propagation Networkscited by 33
  11. 2017Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networkscited by 32
  12. 2021MODNet-V: Improving Portrait Video Matting via Background Restorationcited by 7
  13. 2017Automatic Trimap Generation for Image Mattingcited by 6

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