[深度学习论文笔记][Instance Segmentation] Hypercolumns for Object Segmentation and Fine-Grained Localization
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Hariharan, Bharath, et al. “Hypercolumns for object segmentation and fine-grained localization.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recogni-
tion. 2015. (Citations: 185).
corresponding to each feature map, run 1 × 1 convolutions on each feature map to produce score maps, upsample all score maps to the target resolution, and sum.
tion. 2015. (Citations: 185).
1 Motivation
The high layer features care more about “what” but lose localization information, while the low layer features care more about “where” but are not category-level sensitive enough. See Fig.2 Pipeline
See Fig. This is used in the refinement step. Upsampling is used to resize feature maps to have same size.Then divide the feature map into S × S grid (S = 5 or S = 10 in our case). A logistic regression classifier is trained for grid cell. The classification prediction of each position is
the bilinear interpolation of nearby grid prediction functions. Interpolations are used only at test time.3 Implementation Details
Applying a classifier to each location in a feature map is the same as a 1 × 1 convolution. Thus, to run a linear classifier on top of hypercolumn features, we break it into blockscorresponding to each feature map, run 1 × 1 convolutions on each feature map to produce score maps, upsample all score maps to the target resolution, and sum.
4 References
[1]. CVPR 2015. http://techtalks.tv/talks/hypercolumns-for-object-segmentation-and-fine-grained-localization/61568/.
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