Paper Citation Record · LEDGER

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction

As of 7 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2607.16012.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.16012 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T21:39:24.422182Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

45 of 45 outbound references displayed

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verified fuzzy0
unresolved45
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a42aba28-ecac-478d-8e0c-665b7e4395bf · outbound

This paper cites Bev- former: learning bird’s-eye-view representation from lidar-camera via spatiotemporal transformers,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Bev- former: learning bird’s-eye-view representation from lidar-camera via spatiotemporal transformers,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:20.087838Z

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Observation 2dbe5741-4ef0-4c4b-ba92-b27935d75f60 · outbound

This paper cites VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning

Reference 2

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Observation 1eac1d79-3684-45f9-8fca-a19ded0192e2 · outbound

This paper cites Scene as occupancy,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Scene as occupancy,

Reference 3

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Observation 75f94149-85c6-419e-a4d5-87f72161a241 · outbound

This paper cites Sam 2: Segment anything in images and videos,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Sam 2: Segment anything in images and videos,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:20.517576Z

Source-reported events for the cited work

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Observation 6d8e5135-e9d6-4746-9533-a5cd310102ec · outbound

This paper cites Depth anything v2,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Depth anything v2,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:20.690201Z

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Observation bb445614-6e72-4c16-8d9f-36e61a252dd4 · outbound

This paper cites Multi-task learning for dense prediction tasks: A survey,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Multi-task learning for dense prediction tasks: A survey,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:20.801836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37a61f88-1401-487d-88c6-e49d7680e98b · outbound

This paper cites Multitask learning,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Multitask learning,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:20.908002Z

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Unavailable: canonical work link unavailable.

Observation f4984b3a-8f60-469b-beb2-e0416cf34a4c · outbound

This paper cites Cross-stitch net- works for multi-task learning,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Cross-stitch net- works for multi-task learning,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:20.998305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1bdc3b10-fa49-48f8-bfcf-a460e08589d6 · outbound

This paper cites Vision transformers for dense prediction,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Vision transformers for dense prediction,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:21.092492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 503fba60-17cf-442a-be50-ef23b0f82750 · outbound

This paper cites Invpt++: Inverted pyramid multi-task transformer for visual scene understanding,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Invpt++: Inverted pyramid multi-task transformer for visual scene understanding,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:21.184379Z

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Unavailable: canonical work link unavailable.

Observation 6a65a397-4813-4430-bdd2-6905732cf5f8 · outbound

This paper cites Taskprompter: Spatial-channel multi-task prompting for dense scene understanding,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Taskprompter: Spatial-channel multi-task prompting for dense scene understanding,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:21.292999Z

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Observation 36c726f3-f20a-4859-a6d8-617709dc29d2 · outbound

This paper cites Mtmamba: Enhancing multi-task dense scene understanding by mamba-based decoders,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Mtmamba: Enhancing multi-task dense scene understanding by mamba-based decoders,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:21.450868Z

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Observation fd184f49-2ecc-4c73-8e87-7433931b03af · outbound

This paper cites Multi-task dense prediction via mixture of low-rank experts,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Multi-task dense prediction via mixture of low-rank experts,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:21.580316Z

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Observation 7f043a43-c489-497b-b699-fdc89adaffd9 · outbound

This paper cites Swinmtl: A shared architecture for simultaneous depth estimation and semantic segmentation from monocular camera images,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Swinmtl: A shared architecture for simultaneous depth estimation and semantic segmentation from monocular camera images,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:21.676394Z

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Unavailable: canonical work link unavailable.

Observation dd833e50-027d-470d-b8df-ec8ce9a11f4d · outbound

This paper cites M2h: Multi-task learning with efficient window-based cross-task attention for monocular spatial perception,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction M2h: Multi-task learning with efficient window-based cross-task attention for monocular spatial perception,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:21.780619Z

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Unavailable: canonical work link unavailable.

Observation 6bb956cc-76e5-479c-be18-4792975e63f4 · outbound

This paper cites Deep residual learning for image recognition,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Deep residual learning for image recognition,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:21.890664Z

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Observation 70ccb75b-937a-48a6-8b1a-a28df5b97b28 · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:21.993772Z

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Unavailable: canonical work link unavailable.

Observation 248cb5d4-6c4f-4ee7-ae7e-d78ded7cebd7 · outbound

This paper cites Mo- bilenetv2: Inverted residuals and linear bottlenecks,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Mo- bilenetv2: Inverted residuals and linear bottlenecks,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:22.136360Z

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Observation 40b8a7dd-e098-4d5b-857c-6b1f88155400 · outbound

This paper cites Efficientnet: Rethinking model scaling for con- volutional neural networks,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Efficientnet: Rethinking model scaling for con- volutional neural networks,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:22.242353Z

Source-reported events for the cited work

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Observation cbbb8472-413e-4abf-ba58-961310a09e6d · outbound

This paper cites A convnet for the 2020s,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction A convnet for the 2020s,

Reference 20

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Source-reported events for the cited work

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Observation 18404710-5adb-44ef-886d-5d5a44453edd · outbound

This paper cites Refinenet: Multi-path refinement networks for high-resolution semantic segmentation,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Refinenet: Multi-path refinement networks for high-resolution semantic segmentation,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:22.378408Z

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Observation b10f0a08-cddc-4eec-a845-f0e9799ba0c6 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:22.477867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8afa474c-fde5-406f-93f5-c778ab78de4f · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:22.586658Z

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Observation ea50270a-1cd9-4585-801e-351553bd6b9d · outbound

This paper cites Emerging properties in self-supervised vision trans- formers,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Emerging properties in self-supervised vision trans- formers,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:22.649676Z

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Observation 6d02121e-637b-4880-afa6-009adfabb212 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction DINOv2: Learning Robust Visual Features without Supervision

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:22.684139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd3e65d7-b33c-42cd-a7af-848fea24c4bf · outbound

This paper cites Vision transform- ers need registers,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Vision transform- ers need registers,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:22.719880Z

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Observation d2b61879-2ea6-4fe1-b5e0-b92cb0f8caf1 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction U-net: Convolutional networks for biomedical image segmentation,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:22.727720Z

Source-reported events for the cited work

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Observation c350b8ef-684f-44fb-8932-e32636b6b3e0 · outbound

This paper cites Feature pyramid networks for object detection,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Feature pyramid networks for object detection,

Reference 28

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Observation 664cdfd0-40eb-4ce2-b769-fdc983c61fc4 · outbound

This paper cites Eff-unet: A novel architecture for semantic segmentation in unstructured environment,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Eff-unet: A novel architecture for semantic segmentation in unstructured environment,

Reference 29

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Source-reported events for the cited work

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Observation 06d8129e-6684-41b3-abbb-cb6aebed2fde · outbound

This paper cites Swin transformer v2: Scaling up capacity and resolution,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Swin transformer v2: Scaling up capacity and resolution,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:22.966049Z

Source-reported events for the cited work

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Observation 44afdaec-8ba7-48d4-9230-fb9b06287773 · outbound

This paper cites Depth anything: Unleashing the power of large-scale unlabeled data,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Depth anything: Unleashing the power of large-scale unlabeled data,

Reference 31

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Observation 372b8235-a792-4f7a-9076-996b7e6192fd · outbound

This paper cites Dino- foresight: Looking into the future with dino,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Dino- foresight: Looking into the future with dino,

Reference 32

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Observation 4661902d-3a49-4d4d-85b2-5ba07fee187c · outbound

This paper cites Pidnet: A real-time semantic segmentation network inspired by pid controllers,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Pidnet: A real-time semantic segmentation network inspired by pid controllers,

Reference 33

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Observation 1ba41f3d-9f56-46be-b7cd-11f23b614b68 · outbound

This paper cites Boundary-aware multitask learning for remote sensing imagery,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Boundary-aware multitask learning for remote sensing imagery,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:23.223239Z

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Observation 77ea184a-c799-49d9-830e-1bda3833646e · outbound

This paper cites Training region-based object detectors with online hard example mining,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Training region-based object detectors with online hard example mining,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:23.257861Z

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Observation 72d42e67-451f-4c09-9a8e-3ab1dba1cd3f · outbound

This paper cites Depth map prediction from a single image using a multi-scale deep network,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Depth map prediction from a single image using a multi-scale deep network,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:23.345745Z

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Observation 4988c1ad-a291-4828-a680-1bef7ff3d1f6 · outbound

This paper cites Multi-task learning using uncertainty to weigh losses for scene geometry and semantics,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Multi-task learning using uncertainty to weigh losses for scene geometry and semantics,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:23.483569Z

Source-reported events for the cited work

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Observation a9d016c2-3bf7-434e-b40d-2be14d22bd92 · outbound

This paper cites Pad-net: Multi-tasks guided prediction-and-distillation network for simultaneous depth es- timation and scene parsing,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Pad-net: Multi-tasks guided prediction-and-distillation network for simultaneous depth es- timation and scene parsing,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:23.563214Z

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Unavailable: canonical work link unavailable.

Observation c876027e-ed34-4501-aedc-0f60558f8db5 · outbound

This paper cites Cross-task attention mecha- nism for dense multi-task learning,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Cross-task attention mecha- nism for dense multi-task learning,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:23.627476Z

Source-reported events for the cited work

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Observation 729a26a2-475c-4bdb-92e4-3c3a4c5e1e60 · outbound

This paper cites Three ways to improve semantic segmentation with self-supervised depth estimation,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Three ways to improve semantic segmentation with self-supervised depth estimation,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:23.682895Z

Source-reported events for the cited work

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Observation fa268218-5fd9-492e-9582-d850a25f5f32 · outbound

This paper cites Inverted pyramid multi-task transformer for dense scene understanding,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Inverted pyramid multi-task transformer for dense scene understanding,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:23.760682Z

Source-reported events for the cited work

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Observation aa2bc522-0c5b-489b-9924-4ad2b676b3ef · outbound

This paper cites Mtmamba++: Enhancing multi-task dense scene understanding via mamba-based decoders,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Mtmamba++: Enhancing multi-task dense scene understanding via mamba-based decoders,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:23.939186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85f56974-1f29-4d44-81e6-8c10352f6806 · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction The cityscapes dataset for semantic urban scene understanding,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:24.096978Z

Source-reported events for the cited work

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Observation 74341a56-c0ca-4106-b5f4-79bd47007f7b · outbound

This paper cites Practical stereo matching via cascaded recurrent network with adaptive correlation,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Practical stereo matching via cascaded recurrent network with adaptive correlation,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:24.275077Z

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Observation c70b12dd-9c79-4656-84bf-64ac0fbf718f · outbound

This paper cites Indoor segmenta- tion and support inference from rgbd images,.

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction Indoor segmenta- tion and support inference from rgbd images,

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:24.422182Z

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Pith citing papers

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