論文閱讀：《LIP: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing》
CVPR 2017 Workshop 數據集：http://hcp.sysu.edu.cn/lip 主要貢獻 提出了一個新的large-scale benchmark，以及一個測評服務器用支持進一步的人物解析研究，新的數據集共有50462 張圖片，20 個分類標籤（19個類別+背景） 通過在新的數據集上的實驗，詳細分析之前state-of-the-art 的人物解析方法的優勢和不足 提出了一個自監
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相關文章
- 1. 圖像分割"LIP: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing"
- 2. CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark 論文閱讀
- 3. 論文閱讀：A New Meta-Baseline for Few-Shot Learning
- 4. 論文閱讀：Learning Human-Object Interactions by Graph Parsing Neural Networks
- 5. Spatio-Temporal Fusion based Convolutional Sequence Learning for Lip Reading論文閱讀
- 6. 【論文閱讀筆記】Deep Learning based Recommender System: A Survey and New Perspectives
- 7. 論文閱讀：Detecting and Recognizing Human-Object Interactions
- 8. 論文閱讀：《 Lip Reading Sentences in the Wild》
- 9. 論文閱讀：Action recognition for human-marionette interaction.
- 10. 論文閱讀 Day-1: GSLAM: A General SLAM Framework and Benchmark