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Efficient Deep Space Filling Curve
Space-filling curves (SFCs) act as a linearization approach to map data in higher dimensional space to lower dimensional space, which is used comprehensively in computer vision, such as image/point cloud compression, hashing and etc. Currently, researchers formulate the problem of searching for an optimal SFC to the problem of finding a single Hamiltonian circuit on the image grid graph. Existing methods adopt graph neural networks (GNN) for SFC search. By modeling the pixel grid as a graph, they first adopt GNN to predict the edge weights and then generate a minimum spanning tree (MST) based on the predictions, which is further used to construct the SFC. However, GNN-based methods suffer from high computational costs and memory footprint usage. Besides, MST generation is un-differentiable, which is infeasible to optimize via gradient descent. To remedy these issues, we propose a GNN-based SFC-search framework with a tailored algorithm that largely reduces computational cost of GNN. Additionally, we propose a siamese network learning scheme to optimize DNN-based models in an end-to-end fashion. Extensive experiments show that our proposed method outperforms both DNN-based methods and traditional SFCs, e.g. Hilbert curve, by a large margin on various benchmarks.