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Paper Citation Record · LEDGER

Vision Transformers for Dense Prediction

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2103.13413.

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

pith.paper-citation-record.v1
2103.13413 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:31:50.728421Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T12:43:26.001843Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation e6651243-8f39-466d-994f-220ff61fa59b · inbound

MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer cites this paper.

MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer Vision Transformers for Dense Prediction

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-20T20:46:35.154007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-20T20:46:35.073600Z digest=sha256:7e1dd0110907a6565adf0e160ea745962ac1b54b6615d2e362281984b31a5d19

Observation 284ca38a-4deb-435b-9f32-7e98e6e5ea29 · inbound

Rig3R: Rig-Aware Conditioning for Learned 3D Reconstruction cites this paper.

Rig3R: Rig-Aware Conditioning for Learned 3D Reconstruction Vision Transformers for Dense Prediction

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-07T11:31:50.728421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:31:50.728421Z digest=sha256:32afb557eda5c84f68385542eac60e5777534c45485cf26a32935cdbd1ed4eb5

Observation 1d0f3e76-f382-463c-ba08-cd1f3836659b · inbound

THIRDEYE: Cue-Aware Monocular Depth Estimation via Brain-Inspired Multi-Stage Fusion cites this paper.

THIRDEYE: Cue-Aware Monocular Depth Estimation via Brain-Inspired Multi-Stage Fusion Vision Transformers for Dense Prediction

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-06T22:45:09.780724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:45:09.780724Z digest=sha256:33c61b1bdcbf28db92bb033303a5ca05a8d35718d1a53185a5fb7970674855a5

Observation 5f7fa42c-ae88-4433-9e6e-54433e416803 · inbound

FF3R: Feedforward Feature 3D Reconstruction from Unconstrained views cites this paper.

FF3R: Feedforward Feature 3D Reconstruction from Unconstrained views Vision Transformers for Dense Prediction

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-05-11T06:05:59.675114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T17:47:52.901901Z digest=sha256:c3cf187b89ccc24f5426952ef13ff044a9590b7e7ccb3a34b0c9b5227beffc2a

Observation 7211ffd7-cb75-4e4d-a5bd-c459cd5a642f · inbound

No Pose, No Problem in 4D: Feed-Forward Dynamic Gaussians from Unposed Multi-View Videos cites this paper.

No Pose, No Problem in 4D: Feed-Forward Dynamic Gaussians from Unposed Multi-View Videos Vision Transformers for Dense Prediction

Reference 47

Resolution
verified exact
arxiv_id, observed 2026-05-22T06:31:10.103190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-22T06:27:21.559658Z digest=sha256:f3e7f11cec2588b27035731109058aff57c951d5927abfb5a962078cc9776ea6

Observation 51b265c5-fa69-4eba-b51f-87517ec3bb18 · inbound

Turning Video Models into Generalist Robot Policies cites this paper.

Turning Video Models into Generalist Robot Policies Vision Transformers for Dense Prediction

Reference 52

Resolution
verified exact
arxiv_id, observed 2026-06-29T12:43:26.003382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-29T12:34:55.440907Z digest=sha256:d424eaf377155cb425e43ff49198e890a1b2f422136ab7983253073f176be26d

Observation 89697aa5-fdf0-430c-a0b5-bf4e7428ecf4 · inbound

The Multipath Blind Spot: $K$-Agnostic Robust Calibration for Sparse-Anchor Metric Depth from Frozen Foundations cites this paper.

The Multipath Blind Spot: $K$-Agnostic Robust Calibration for Sparse-Anchor Metric Depth from Frozen Foundations Vision Transformers for Dense Prediction

Reference 2

Resolution
unresolved
no resolver link, observed 2026-07-11T21:42:00.898441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T21:42:00.898441Z digest=sha256:863047aa21c7ed43757c646baaa72567f13b77ba524bf10028c1ff466d5df858

Observation 47985a1b-cc58-40a0-9f44-8c90c46e32e5 · inbound

RayOcc: Occlusion-Aware Ray Occupancy Estimation via Gaussian Mixture Intensity cites this paper.

RayOcc: Occlusion-Aware Ray Occupancy Estimation via Gaussian Mixture Intensity Vision Transformers for Dense Prediction

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-01T17:26:13.404733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T17:26:13.404733Z digest=sha256:12a3e9f07f65a45d3a730905b568046e3273b50229d487958f5eb7f840710d1e

Observation 2b9cb9cd-0d6e-4143-9098-b7ef5edcb7ee · inbound

MoGe-3: Fine-Detail Monocular Geometry Estimation with Self-Guided Sparse Volumetric Refinement cites this paper.

MoGe-3: Fine-Detail Monocular Geometry Estimation with Self-Guided Sparse Volumetric Refinement Vision Transformers for Dense Prediction

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-01T16:34:23.661246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T16:34:23.661246Z digest=sha256:313f0b6a7c468b06eace6cab9a101ddc9d6218f5e5fa60096244ea30a4628ee6