Papers › Learning to Upsample by Learning to Sample

Learning to Upsample by Learning to Sample

29 Aug 2023ICCV 2023 1arXiv:2308.15085archive 2025-07-28

Wenze Liu, Hao Lu, Hongtao Fu, Zhiguo Cao

We present DySample, an ultra-lightweight and effective dynamic upsampler. While impressive performance gains have been witnessed from recent kernel-based dynamic upsamplers such as CARAFE, FADE, and SAPA, they introduce much workload, mostly due to the time-consuming dynamic convolution and the additional sub-network used to generate dynamic kernels. Further, the need for high-res feature guidance of FADE and SAPA somehow limits their application scenarios. To address these concerns, we bypass dynamic convolution and formulate upsampling from the perspective of point sampling, which is more resource-efficient and can be easily implemented with the standard built-in function in PyTorch. We first showcase a naive design, and then demonstrate how to strengthen its upsampling behavior step by step towards our new upsampler, DySample. Compared with former kernel-based dynamic upsamplers, DySample requires no customized CUDA package and has much fewer parameters, FLOPs, GPU memory, and latency. Besides the light-weight characteristics, DySample outperforms other upsamplers across five dense prediction tasks, including semantic segmentation, object detection, instance segmentation, panoptic segmentation, and monocular depth estimation. Code is available at https://github.com/tiny-smart/dysample.

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Tasks

Depth EstimationFeature UpsamplingInstance SegmentationMonocular Depth EstimationObject DetectionPanoptic SegmentationSegmentationSemantic Segmentationobject-detection

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Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Feature Upsampling ImageNet Dysample ADCC 61.6 #4 of 8 Archive leaderboard report
Feature Upsampling ImageNet Dysample Average Drop 17.8 #4 of 8 Archive leaderboard report
Feature Upsampling ImageNet Dysample Average Increase 20.0 #4 of 8 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

CARAFEConvolution

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