Pixel-Adaptive Convolutional Neural Networks
Open accessAbstract
Convolutions are the fundamental building blocks of CNNs. The fact that their weights are spatially shared is one of the main reasons for their widespread use, but it is also a major limitation, as it makes convolutions content-agnostic. We propose a pixel-adaptive convolution (PAC) operation, a simple yet effective modification of standard convolutions, in which the filter weights are multiplied with a spatially varying kernel that depends on learnable, local pixel features. PAC is a generalization of several popular filtering techniques and thus can be used for a wide range of use cases. Specifically, we demonstrate state-of-the-art performance when PAC is used for deep joint image upsampling. PAC also offers an effective alternative to fully-connected CRF (Full-CRF), called PAC-CRF, which performs competitively compared to Full-CRF, while being considerably faster. In addition, we also demonstrate that PAC can be used as a drop-in replacement for convolution layers in pre-trained networks, resulting in consistent performance improvements.
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Mao et al. illustrated that multi-task learning improves adversarial robustness, which is critical for safety applications.
“Mao et al. [4] illustrated that multi-task learning improves adversarial robustness, which is critical for safety applications.”
From OmniDet: Surround View Cameras Based Multi-Task Visual Perception Network for Autonomous Driving (2021) · cites this paper as [4]
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CARAFE++: Unified Content-Aware ReAssembly of FEatures
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Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution
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MetaGait: Learning to Learn an Omni Sample Adaptive Representation for Gait Recognition
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HyperE2VID: Improving Event-Based Video Reconstruction via Hypernetworks
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Panoptic-Based Image Synthesis
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- Authors
- Su, H., Jampani, V., Sun, D., Gallo, O., Learned-Miller, E., & Kautz, J.
- Affiliations
- University of Massachusetts Amherst (US)
- Nvidia (United Kingdom) (GB)
- Nvidia (United States) (US)
- Univ. of Massachusetts, Amherst
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 11158-11167
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963870605
- Semantic Scholar
- dabc815780ab0aa0c8136b58c0cf1abec1336884
- Cited by
- 348
- References
- 54
- Citations by year
- 2026: 16 · 2025: 41 · 2024: 56 · 2023: 66 · 2022: 54 · 2021: 75 · 2020: 32 · 2019: 7
- FWCI
- 13.70
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Defense Advanced Research Projects Agency (FA8750)
- Air Force Research Laboratory
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- convolutional neural networks, image upscaling, conditional random fields
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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