CAP: Context-Aware Pruning for Semantic Segmentation

By Wei He, Meiqing Wu, Mingfu Liang, Siew Kei Lam. 2021

42 citations recorded by the source corpus.

Network pruning for deep convolutional neural networks (CNNs) has recently achieved notable research progress on image-level classification. However, most existing pruning methods are not catered to or evaluated on semantic segmentation networks. In this paper, we advocate the importance of contextual information during channel pruning for semantic segmentation networks by presenting a novel Context-aware Pruning framework. Concretely, we formulate the embedded contextual information by leveraging the layer-wise channels interdependency via the Context-aware Guiding Module (CAGM) and introduce the Context-aware Guided Sparsification (CAGS) to adaptively identify the informative channels on the cumbersome model by inducing channel-wise sparsity on the scaling factors in batch normalization (BN) layers. The resulting pruned models require significantly lesser operations for inference while maintaining comparable performance to (at times outperforming) the original models. We evaluated our framework on widely-used benchmarks and showed its effectiveness on both large and lightweight models. On Cityscapes dataset, our framework reduces the number of parameters by 32%, 47%, 54%, and 63%, on PSPNet101, PSPNet50, ICNet, and SegNet, respectively, while preserving the performance.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Wei He
  2. Zhongzhan Huang
  3. Senwei Liang
  4. Ming Liang

In Computer Vision and Pattern Recognition

  1. Wei He
  2. Zhongzhan Huang
  3. Senwei Liang
  4. Ming Liang
  5. Xiaogang Wang
  6. Hongsheng Li
  7. Shaoshuai Shi
  8. Jiaya Jia

Citations in the corpus