Learning the Superpixel in a Non-iterative and Lifelong Manner
This work addresses the limitations of existing CNN-based superpixel methods for computer vision applications by improving flexibility and efficiency, though it is incremental as it builds on prior CNN approaches.
The paper tackles the problem of CNN-based superpixel segmentation by proposing an unsupervised method that eliminates the need for manual labels and reduces computational complexity, achieving significantly better performance on three benchmarks with nearly ten times lower complexity compared to state-of-the-art methods.
Superpixel is generated by automatically clustering pixels in an image into hundreds of compact partitions, which is widely used to perceive the object contours for its excellent contour adherence. Although some works use the Convolution Neural Network (CNN) to generate high-quality superpixel, we challenge the design principles of these networks, specifically for their dependence on manual labels and excess computation resources, which limits their flexibility compared with the traditional unsupervised segmentation methods. We target at redefining the CNN-based superpixel segmentation as a lifelong clustering task and propose an unsupervised CNN-based method called LNS-Net. The LNS-Net can learn superpixel in a non-iterative and lifelong manner without any manual labels. Specifically, a lightweight feature embedder is proposed for LNS-Net to efficiently generate the cluster-friendly features. With those features, seed nodes can be automatically assigned to cluster pixels in a non-iterative way. Additionally, our LNS-Net can adapt the sequentially lifelong learning by rescaling the gradient of weight based on both channel and spatial context to avoid overfitting. Experiments show that the proposed LNS-Net achieves significantly better performance on three benchmarks with nearly ten times lower complexity compared with other state-of-the-art methods.