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arXiv preprint arXiv:2203.14211 , year=

DepthFormer: Exploiting Long-Range Correlation, Local Information for Accurate Monocular Depth Estimation , author= · 2022 · arXiv 2203.14211

2 Pith papers cite this work. Polarity classification is still indexing.

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cs.CV 2

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2026 1 2023 1

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UNVERDICTED 2

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Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation

cs.CV · 2026-05-11 · unverdicted · novelty 6.0

GraphDepth integrates multi-scale GraphSAGE layers into a ResNet-101 U-Net with attention-gated skips and uncertainty estimation to deliver competitive monocular depth accuracy at lower computational cost than transformers.

Vision Transformers Need Registers

cs.CV · 2023-09-28 · unverdicted · novelty 6.0

Adding register tokens to Vision Transformers eliminates high-norm background artifacts and raises state-of-the-art performance on dense visual prediction tasks.

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Showing 2 of 2 citing papers.

  • Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation cs.CV · 2026-05-11 · unverdicted · none · ref 7

    GraphDepth integrates multi-scale GraphSAGE layers into a ResNet-101 U-Net with attention-gated skips and uncertainty estimation to deliver competitive monocular depth accuracy at lower computational cost than transformers.

  • Vision Transformers Need Registers cs.CV · 2023-09-28 · unverdicted · none · ref 277

    Adding register tokens to Vision Transformers eliminates high-norm background artifacts and raises state-of-the-art performance on dense visual prediction tasks.