Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
FETV: A Benchmark for Fine-Grained Evaluation of Open-Domain Text-to-Video Generation
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2311.01813.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T23:40:21.561264Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-29T22:54:00.764516Z
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 016ad169-e761-4ded-a56e-f41c735fe44b · inbound
Content-Rich AIGC Video Quality Assessment via Intricate Text Alignment and Motion-Aware Consistency cites this paper.
Content-Rich AIGC Video Quality Assessment via Intricate Text Alignment and Motion-Aware Consistency FETV: A Benchmark for Fine-Grained Evaluation of Open-Domain Text-to-Video Generation
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T23:40:21.561264Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 99fd2706-140c-47f0-81f2-4bff03c18ae7 · inbound
We'll Fix it in Post: Improving Text-to-Video Generation with Neuro-Symbolic Feedback cites this paper.
We'll Fix it in Post: Improving Text-to-Video Generation with Neuro-Symbolic Feedback FETV: A Benchmark for Fine-Grained Evaluation of Open-Domain Text-to-Video Generation
Reference 57
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T18:56:58.159507Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 216da338-cbcc-4efc-955b-3425461459dd · inbound
How Far Are Video Models from True Multimodal Reasoning? cites this paper.
How Far Are Video Models from True Multimodal Reasoning? FETV: A Benchmark for Fine-Grained Evaluation of Open-Domain Text-to-Video Generation
Reference 46
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T12:51:03.923156Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b2078459-455c-4a9a-b2c6-dff2f5ce1dbc · inbound
LongAV-Compass: Towards Unified Evaluation of Minute-Scale Audio-Visual Generation Across T2AV, I2AV, and V2AV cites this paper.
LongAV-Compass: Towards Unified Evaluation of Minute-Scale Audio-Visual Generation Across T2AV, I2AV, and V2AV FETV: A Benchmark for Fine-Grained Evaluation of Open-Domain Text-to-Video Generation
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T22:54:00.766237Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation a73ea158-4690-4752-b760-97aa19c20fbf · inbound
Are Video Models Zero-Shot Learners and Reasoners in Education? EduVideoBench, A Knowledge-Skills-Attitude Benchmark for Educational Video Generation cites this paper.
Are Video Models Zero-Shot Learners and Reasoners in Education? EduVideoBench, A Knowledge-Skills-Attitude Benchmark for Educational Video Generation FETV: A Benchmark for Fine-Grained Evaluation of Open-Domain Text-to-Video Generation
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T18:23:50.889184Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation eb651483-7cde-4a54-89d2-2109ec93c665 · inbound
DSTAR: Accelerating Diffusion Transformers via Spatial and Temporal Redundancy Reduction cites this paper.
DSTAR: Accelerating Diffusion Transformers via Spatial and Temporal Redundancy Reduction FETV: A Benchmark for Fine-Grained Evaluation of Open-Domain Text-to-Video Generation
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T22:14:35.617067Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.