mlsanityData to 7 Oct 2026

Shuffle Transformer with Feature Alignment for Video Face Parsing

arXiv 2106.08650 ↗First posted 16 Jun 2021cs.CVcited by 0

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Abstract (arXiv)

This is a short technical report introducing the solution of the Team TCParser for Short-video Face Parsing Track of The 3rd Person in Context (PIC) Workshop and Challenge at CVPR 2021. In this paper, we introduce a strong backbone which is cross-window based Shuffle Transformer for presenting accurate face parsing representation. To further obtain the finer segmentation results, especially on the edges, we introduce a Feature Alignment Aggregation (FAA) module. It can effectively relieve the feature misalignment issue caused by multi-resolution feature aggregation. Benefiting from the stronger backbone and better feature aggregation, the proposed method achieves 86.9519% score in the Short-video Face Parsing track of the 3rd Person in Context (PIC) Workshop and Challenge, ranked the first place.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 24 references that are in the map. First posted on arXiv 16 Jun 2021.

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References · 19

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  12. 2021Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformercited by 115
7 more
  1. 2018Learning a Discriminative Feature Network for Semantic Segmentationcited by 114
  2. 2018Devil in the Details: Towards Accurate Single and Multiple Human Parsingcited by 64
  3. 2020AlignSeg: Feature-Aligned Segmentation Networkscited by 53
  4. 2019Face Parsing with RoI Tanh-Warpingcited by 33
  5. 2020Edge-aware Graph Representation Learning and Reasoning for Face Parsingcited by 26
  6. 2019SPGNet: Semantic Prediction Guidance for Scene Parsingcited by 21
  7. 2018Interlinked Convolutional Neural Networks for Face Parsingcited by 16

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