【圖像分割論文閱讀】MNC:Instance-aware Semantic Segmentation via Multi-task Network Cascades
本文出自微軟研究院，由清華教授代季峯、何凱明、曠視孫劍三位前輩合力完成。收錄於CVPR2016. 論文地址：MNC:Instance-aware Semantic Segmentation via Multi-task Network Cascades 背景 自從FCN提出後綜合深度學習特徵、大規模註解以及基於圖像模型的高級推理，語義分割任務發展迅速。儘管如此，精確快速的實例級語義分割任務仍然是一
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相關文章
- 1. 論文閱讀：Instance-aware Semantic Segmentation via Multi-task Network Cascades
- 2. 論文閱讀：MNC：Instance-aware Semantic Segmentation via Multi-task Network Cascades
- 3. 閱讀筆記：Instance-aware semantic segmentation via Multi-task Network Cascades
- 4. 【圖像分割論文閱讀】Efficient ConvNet for Real-time Semantic Segmentation
- 5. 【圖像分割論文閱讀】LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
- 6. 【圖像分割論文閱讀】DenseASPP for Semantic Segmentation in Street Scenes
- 7. 論文筆記之：Instance-aware Semantic Segmentation via Multi-task Network Cascades
- 8. 【圖像分割論文閱讀】ENet：A Deep Neural Network Architecture for Real-Time Semantic Segmentation
- 9. 【圖像分割論文閱讀】OANet：An End-to-End Network for Panoptic Segmentation
- 10. 【論文閱讀】Learning Deconvolution Network for Semantic Segmentation