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Paper Citation Record · LEDGER

Combining the Best of Convolutional Layers and Recurrent Layers: A Hybrid Network for Semantic Segmentation

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1603.04871.

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

pith.paper-citation-record.v1
1603.04871 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:48:34.991090Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T00:28:45.937859Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

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 e1a9a8a7-cd64-401f-8f6b-4701820cd37e · inbound

Rethinking Atrous Convolution for Semantic Image Segmentation cites this paper.

Rethinking Atrous Convolution for Semantic Image Segmentation Combining the Best of Convolutional Layers and Recurrent Layers: A Hybrid Network for Semantic Segmentation

Reference 88

Resolution
verified exact
arxiv_id, observed 2026-07-04T20:53:41.283200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-12T00:28:45.260361Z digest=sha256:8e84e113a1a9f3fd5e4a47d583a67de3c36a2b3a493a726129a05d559feb8f69

Observation 4ae8b2ab-3d11-4ccc-af4f-d299a93e015b · inbound

Semantic Correlation Promoted Shape-Variant Context for Segmentation cites this paper.

Semantic Correlation Promoted Shape-Variant Context for Segmentation Combining the Best of Convolutional Layers and Recurrent Layers: A Hybrid Network for Semantic Segmentation

Reference 69

Resolution
unresolved
no resolver link, observed 2026-08-14T04:48:34.991090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T04:48:34.991090Z digest=sha256:22115e8944a8e775d66d8906a39c56cb4a9db93bb3d46193fba5b557bbe3e991