Pith. sign in

Paper Citation Record · LEDGER

Enhancing Vision-Language Models Generalization via Diversity-Driven Novel Feature Synthesis

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2405.02586.

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

pith.paper-citation-record.v1
2405.02586 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 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:54:30.671892Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T23:21:09.424138Z

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 e8eafccc-dc9a-47f2-8218-b6298fcf276c · inbound

CLIP-Powered Domain Generalization and Domain Adaptation: A Comprehensive Survey cites this paper.

CLIP-Powered Domain Generalization and Domain Adaptation: A Comprehensive Survey Enhancing Vision-Language Models Generalization via Diversity-Driven Novel Feature Synthesis

Reference 174

Resolution
unresolved
no resolver link, observed 2026-08-16T11:54:30.671892Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:54:30.671892Z digest=sha256:4f1ea8c3c2637c7610f5885cba9af0b49853406bf3132f66d4aea2142cdb6c28

Observation 0b38bfb6-ca77-4eab-9ba1-c165d11b0cc4 · inbound

Generalizing vision-language models to novel domains: A comprehensive survey cites this paper.

Generalizing vision-language models to novel domains: A comprehensive survey Enhancing Vision-Language Models Generalization via Diversity-Driven Novel Feature Synthesis

Reference 101

Resolution
verified exact
local_arxiv, observed 2026-08-06T23:21:09.514216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T23:20:46.315371Z digest=sha256:272ab6fc2b0c00db92fdbb0e8ee21eee506b27c5ae3178d876d6d4f9e4ee674f