Memory Based Video Scene Parsing
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Abstract (arXiv)
Video scene parsing is a long-standing challenging task in computer vision, aiming to assign pre-defined semantic labels to pixels of all frames in a given video. Compared with image semantic segmentation, this task pays more attention on studying how to adopt the temporal information to obtain higher predictive accuracy. In this report, we introduce our solution for the 1st Video Scene Parsing in the Wild Challenge, which achieves a mIoU of 57.44 and obtained the 2nd place (our team name is CharlesBLWX).
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 7 of its 11 references that are in the map. First posted on arXiv 1 Sep 2021.
Builds on
7 of 11 references.
References · 7
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2016Feature Pyramid Networks for Object Detectioncited by 5,021
- 2016Pyramid Scene Parsing Networkcited by 2,472
- 2021BEiT: BERT Pre-Training of Image Transformerscited by 2,360
- 2018Unified Perceptual Parsing for Scene Understandingcited by 1,265
- 2021Temporal Memory Attention for Video Semantic Segmentationcited by 49
- 2021Mining Contextual Information Beyond Image for Semantic Segmentationcited by 48
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Citing papers per year: 2022: 2; 2023: 1.
3 papers.