mlsanityData to 7 Oct 2026

Superpixel-informed Implicit Neural Representation for Multi-Dimensional Data

arXiv 2411.11356 ↗First posted 18 Nov 2024cs.CVcited by 7

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Abstract (arXiv)

Recently, implicit neural representations (INRs) have attracted increasing attention for multi-dimensional data recovery. However, INRs simply map coordinates via a multi-layer perception (MLP) to corresponding values, ignoring the inherent semantic information of the data. To leverage semantic priors from the data, we propose a novel Superpixel-informed INR (S-INR). Specifically, we suggest utilizing generalized superpixel instead of pixel as an alternative basic unit of INR for multi-dimensional data (e.g., images and weather data). The coordinates of generalized superpixels are first fed into exclusive attention-based MLPs, and then the intermediate results interact with a shared dictionary matrix. The elaborately designed modules in S-INR allow us to ingenuously exploit the semantic information within and across generalized superpixels. Extensive experiments on various applications validate the effectiveness and efficacy of our S-INR compared to state-of-the-art INR methods.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 36 of its 53 references that are in the map. First posted on arXiv 18 Nov 2024.

Builds on

36 of 53 references.

References · 36

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24 more
  1. 2021Implicit Neural Representations for Image Compressioncited by 161
  2. 2021BACON: Band-limited Coordinate Networks for Multiscale Scene Representationcited by 142
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  5. 2023Implicit Neural Representation for Cooperative Low-light Image Enhancementcited by 92
  6. 2021DiGS : Divergence guided shape implicit neural representation for unoriented point cloudscited by 76
  7. 2021Dynamic CT Reconstruction from Limited Views with Implicit Neural Representations and Parametric Motion Fieldscited by 69
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  12. 2023Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolutioncited by 33
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  17. 2021Implicit Neural Representation Learning for Hyperspectral Image Super-Resolutioncited by 17
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  19. 2024HOSC: A Periodic Activation Function for Preserving Sharp Features in Implicit Neural Representationscited by 14
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  22. 2024Implicit Neural Representation for Physics-driven Actuated Soft Bodiescited by 5
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 3.

7 papers.

  1. 2025Continuous Representation Methods, Theories, and Applications: An Overview and Perspectivescited by 4
  2. 2025Superpixel Anything: A general object-based framework for accurate yet regular superpixel segmentationcited by 3
  3. 2025Bias for Action: Video Implicit Neural Representations with Bias Modulationcited by 2
  4. 2025Cross-Frequency Implicit Neural Representation with Self-Evolving Parameterscited by 2
  5. 2026Calibrated Harmonic Overlaid Implicit Neural Representations for Multi-Dimensional Datacited by 2
  6. 2026Neural Operator-Grounded Continuous Tensor Function Representation and Its Applicationscited by 0
  7. 2026H-SPAM: Hierarchical Superpixel Anything Modelcited by 0