mlsanityData to 14 Oct 2026

Contrastive Monotonic Pixel-Level Modulation

arXiv 2207.11517 ↗First posted 23 Jul 2022cs.CVcited by 2

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Abstract (arXiv)

Continuous one-to-many mapping is a less investigated yet important task in both low-level visions and neural image translation. In this paper, we present a new formulation called MonoPix, an unsupervised and contrastive continuous modulation model, and take a step further to enable a pixel-level spatial control which is critical but can not be properly handled previously. The key feature of this work is to model the monotonicity between controlling signals and the domain discriminator with a novel contrastive modulation framework and corresponding monotonicity constraints. We have also introduced a selective inference strategy with logarithmic approximation complexity and support fast domain adaptations. The state-of-the-art performance is validated on a variety of continuous mapping tasks, including AFHQ cat-dog and Yosemite summer-winter translation. The introduced approach also helps to provide a new solution for many low-level tasks like low-light enhancement and natural noise generation, which is beyond the long-established practice of one-to-one training and inference. Code is available at https://github.com/lukun199/MonoPix.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 68 of its 85 references that are in the map. First posted on arXiv 23 Jul 2022.

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68 of 85 references.

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Citing papers per year: 2022: 1; 2023: 0; 2024: 1.

2 papers.

  1. 2022Semantics-Empowered Communication: A Tutorial-cum-Surveycited by 8
  2. 2024Generative Quanta Color Imagingcited by 1