【論文筆記】DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks
這篇論文提出了一種給定弱標註的實例分割方法。其將微軟研究院提出的GrabCut進行擴展，可以實現給定bounding boxes的神經網絡分類器訓練。該論文將分類問題視爲在稠密連接的條件隨機場下的能量最小化問題，並通過不斷迭代實現實例分割。 Abstract In this paper, we propose DeepCut, a method to obtain pixelwise object
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相關文章
- 1. 論文筆記——OnAVOS：Online Adaptation of Convolutional Neural Networks for Video Object Segmentation
- 2. 論文筆記：RGB-D Object Recognition Using Deep Convolutional Neural Networks
- 3. Notes on Convolutional Neural Networks (from deeplearning.ai) WEEK 3
- 4. 論文筆記1.3——Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
- 5. Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks
- 6. Convolutional Neural Network - Object Detection
- 7. 論文 | Predicting Mortgage Default Using Convolutional Neural Networks
- 8. 【DDFD】《Multi-view Face Detection Using Deep Convolutional Neural Networks》
- 9. 論文 | Credit Card Fraud Detection Using Convolutional Neural Networks
- 10. [CVPR2017] Weakly Supervised Cascaded Convolutional Networks論文筆記