[IDW-CNN]Learning Object Interactions and Descriptions for Semantic Image Segmentation
IDW-CNN Abstract CVPR 2017的文章，中山，港中文，商湯和中科院深研院合作的文章，和DIS是相同的作者. 本文仍然是立足於說現有的圖像詳細標註太少，導致語義分割的性能的首選，所以採用一種若監督的方法來提升性能(這個motivation似乎可以是所有弱監督方法的mt).本文提出使用Image Descriptions in the Wild(IDW)數據集結合VOC2012聯合
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相關文章
- 1. 語義分割--Learning Object Interactions and Descriptions for Semantic Image Segmentation
- 2. Rich feature hierarchies for accurate object detection and semantic segmentation
- 3. RCNN：Rich feature hierarchies for accurate object detection and semantic segmentation
- 4. CVPR 2015 Image Caption: Deep Visual-Semantic Alignments for Generating Image Descriptions
- 5. ICNet for Real-Time Semantic Segmentation on High-Resolution Image
- 6. 【簡評】[CVPR2017]Loss Max-Pooling for Semantic Image Segmentation
- 7. 【簡評】Loss Max-Pooling for Semantic Image Segmentation
- 8. Loss Max-Pooling for Semantic Image Segmentation(2017cvpr）
- 9. 論文解讀：R-CNN《Rich feature hierarchies for accurate object detection and semantic segmentation》
- 10. R-CNN:Rich feature hierarchies for accurate object detection and semantic segmentation