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Paper Citation Record · LEDGER

Semantic Instance Segmentation with a Discriminative Loss Function

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:1708.02551.

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

pith.paper-citation-record.v1
1708.02551 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:41:08.612768Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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

External citation measurements

24
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 2fff6eee-28b9-44b6-89bc-604962ff092d · inbound

gen2seg: Generative Models Enable Generalizable Instance Segmentation cites this paper.

gen2seg: Generative Models Enable Generalizable Instance Segmentation Semantic Instance Segmentation with a Discriminative Loss Function

Reference 7

Resolution
metadata mismatch
local_arxiv, observed 2026-05-22T14:31:40.455449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-07-11T11:50:26.030339Z digest=sha256:95bda1a281f5a68bbf2c13de5ceaa17a6a5a0941ff4d4219a4ca64ce1a3f08c7

Observation 82c802e7-9622-4842-b82a-36072dfc7e1d · inbound

Open-Set LiDAR Panoptic Segmentation Guided by Uncertainty-Aware Learning cites this paper.

Open-Set LiDAR Panoptic Segmentation Guided by Uncertainty-Aware Learning Semantic Instance Segmentation with a Discriminative Loss Function

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-07T00:41:08.612768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:41:08.612768Z digest=sha256:065b6ad905f25198a94e5fbecd5d125bd842c65be0172e3db260480a0376b08c

Observation d1c76758-fbe5-4438-9adc-73293337a6be · inbound

GVCCS: A Dataset for Contrail Identification and Tracking on Visible Whole Sky Camera Sequences cites this paper.

GVCCS: A Dataset for Contrail Identification and Tracking on Visible Whole Sky Camera Sequences Semantic Instance Segmentation with a Discriminative Loss Function

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-06T14:37:32.071995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T14:37:32.071995Z digest=sha256:861e2cb6511209bb774f89f0c03e980ea611e23245d5d4b9618374d4cbddece9

Observation c0a6fe54-2524-4d25-b574-996860b33793 · inbound

MapRF: Weakly Supervised Online HD Map Construction via NeRF-Guided Self-Training cites this paper.

MapRF: Weakly Supervised Online HD Map Construction via NeRF-Guided Self-Training Semantic Instance Segmentation with a Discriminative Loss Function

Reference 46

Resolution
verified exact
local_arxiv, observed 2026-05-17T06:49:11.509078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-17T06:47:55.820535Z digest=sha256:b03cec1c56600777e48731f8c3e414f8f033c34983d0a10ac2284a4b571695e0

Observation 327e26c3-81f1-4bc0-8ef2-9132f67cfad0 · inbound

Evaluation of Embedding-Based and Generative Methods for LLM-Driven Document Classification: Opportunities and Challenges cites this paper.

Evaluation of Embedding-Based and Generative Methods for LLM-Driven Document Classification: Opportunities and Challenges Semantic Instance Segmentation with a Discriminative Loss Function

Reference 2

Resolution
unresolved
no resolver link, observed 2026-07-13T10:23:38.252227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T10:23:38.252227Z digest=sha256:1a76bad7a7daaa0a5fffeb467022bb0e7c6bc822d81503283a002bdfdf96099b

Observation 9fa213d5-884f-4513-8702-6cf69c026dee · inbound

Neural Collapse by Design: Learning Class Prototypes on the Hypersphere cites this paper.

Neural Collapse by Design: Learning Class Prototypes on the Hypersphere Semantic Instance Segmentation with a Discriminative Loss Function

Reference 69

Resolution
metadata mismatch
local_arxiv, observed 2026-05-21T07:34:48.042400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-21T07:34:23.657217Z digest=sha256:a867389c4d7dba32fadf2371b0075ebee7bc8e74d30aff474b89084c782260a7

Observation d108c9d6-40c9-456a-817a-2313b08b3b91 · inbound

Neural Collapse by Design: Learning Class Prototypes on the Hypersphere cites this paper.

Neural Collapse by Design: Learning Class Prototypes on the Hypersphere Semantic Instance Segmentation with a Discriminative Loss Function

Reference 69

Resolution
metadata mismatch
local_arxiv, observed 2026-05-22T09:51:21.829449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-22T09:48:55.074560Z digest=sha256:8eaa23c5bf9e3d1ce8bfc5bcdefaf8b66d1d030c7be95e438c1fa71163933f9e