.png){kind=icon}
.png){kind=icon}
Return
Disassembling Convolutional Segmentation Network
.png){kind=icon}
PRE
AI
DOI：10.1007/s11263-023-01776-z.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In recent years, the convolutional segmentation network has achieved remarkable performance in the computer vision area. However, training a practicable segmentation network is time- and resource-consuming. In this paper, focusing on the semantic image segmentation task, we attempt to disassemble a convolutional segmentation network into category-aware convolution kernels and achieve customizable tasks without additional training by utilizing those kernels. The core of disassembling convolutional segmentation networks is how to identify the relevant convolution kernels for a specific category. According to the encoder-decoder network architecture, the disassembling framework, named Disassembler, is devised to be composed of the forward channel-wise activation attribution and backward gradient attribution. In the forward channel-wise activation attribution process, for each image, the activation values of each feature map in the high-confidence mask area are summed into category-aware probability vectors. In the backward gradient attribution process, the positive gradients w.r.t. each feature map in the high-confidence mask area are summed into a relative coefficient vector for each category. With the cooperation of two vectors, the Disassembler can effectively disassemble category-aware convolution kernels. Extensive experiments demonstrate that the proposed Disassembler can accomplish the category-customizable task without additional training. The disassembled category-aware sub-network achieves comparable performance without any finetuning and will outperform existing state-of-the-art methods with one epoch of finetuning.
Keywords:
Semantic segmentation
Convolutional neural network
Disassembling
Activation
Gradient
Journal
IF:
9.3
Papers:
3.9K
Citations:
2.8W