Sparse Refinement for Efficient High-Resolution Semantic Segmentation

Zhijian Liu, Zhuoyang Zhang, Shang Yang, Haotian Tang, Chenfeng Xu, Kurt Keutzer, Song Han

Primary Area: representation learning for computer vision, audio, language, and other modalities

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Keywords: Efficient machine learning, Semantic segmentation, Sparsity, Efficient model design, Model compression and acceleration

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Abstract:

Semantic segmentation empowers numerous real-world applications, such as autonomous driving and augmented/mixed reality. These applications often operate on high-resolution images (e.g., 8 megapixels) to capture the fine details. However, this comes at the cost of considerable computational complexity, hindering the deployment in latency-sensitive scenarios. In this paper, we introduce SparseRefine, a novel approach that enhances dense low-resolution predictions with sparse high-resolution refinements. Based on coarse low-resolution outputs, SparseRefine first uses an entropy selector to identify a sparse set of pixels with the least confidence. It then employs a sparse feature extractor to efficiently generate the refinements for those pixels of interest. Finally, it leverages a gated ensembler to apply these sparse refinements to the initial coarse predictions. SparseRefine can be seamlessly integrated into any existing semantic segmentation model, regardless of CNN- or ViT-based. SparseRefine achieves significant speedup: 1.5 to 3.9 times when applied to HRNet-W48, SegFormer-B5, Mask2Former-T/L and SegNeXt-L on Cityscapes, with negligible to no loss of accuracy. We will release the code to reproduce our results. We hope that our "dense+sparse" paradigm could inspire future research on efficient high-resolution visual computing.

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Submission Number: 8034