Dense optical flow can be estimated accurately in one forward pass by combining DINO-v2 semantic priors and monocular depth geometric cues via global matching, reaching 2.81 EPE on Sintel Final without any refinement.
Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
UniISP unifies ISP processing with a Hybrid Attention Module and Feature Adapter to produce images that are both visually pleasing for humans and informative for computer vision models.
citing papers explorer
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Rethinking Dense Optical Flow without Test-Time Scaling
Dense optical flow can be estimated accurately in one forward pass by combining DINO-v2 semantic priors and monocular depth geometric cues via global matching, reaching 2.81 EPE on Sintel Final without any refinement.
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UniISP: A Unified ISP Framework for Both Human and Machine Vision
UniISP unifies ISP processing with a Hybrid Attention Module and Feature Adapter to produce images that are both visually pleasing for humans and informative for computer vision models.