Pith. sign in

Paper Citation Record · LEDGER

Text Descriptions are Compressive and Invariant Representations for Visual Learning

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2307.04317.

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

pith.paper-citation-record.v1
2307.04317 v2

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 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T17:09:44.730681Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T14:31:37.433474Z

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 1a57e65d-9790-43f2-9b4a-596f74502a85 · inbound

MultiEYE: Dataset and Benchmark for OCT-Enhanced Retinal Disease Recognition from Fundus Images cites this paper.

MultiEYE: Dataset and Benchmark for OCT-Enhanced Retinal Disease Recognition from Fundus Images Text Descriptions are Compressive and Invariant Representations for Visual Learning

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-11T17:09:44.730681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:09:44.730681Z digest=sha256:a7f79489f792b042e6615fb4f855f74d19c6ff32ab202218e07166db093ee56c

Observation c160910e-7fd4-4758-a399-a59b400fb036 · inbound

Does VLM Classification Benefit from LLM Description Semantics? cites this paper.

Does VLM Classification Benefit from LLM Description Semantics? Text Descriptions are Compressive and Invariant Representations for Visual Learning

Reference 13

Resolution
verified exact
local_arxiv, observed 2026-08-11T14:31:37.437843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T14:31:37.208435Z digest=sha256:1fa20c73bda646709ae0922612d4fa146d90d042c01cb3935656b571214e4e59