Paper Citation Record · LEDGER

FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2111.13824.

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

pith.paper-citation-record.v1
2111.13824 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:59:29.255532Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T08:09:41.095910Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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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 ada87dbf-4213-4e22-ae10-1355d7f12584 · inbound

AHCQ-SAM: Toward Accurate and Hardware-Compatible Post-Training Segment Anything Model Quantization cites this paper.

AHCQ-SAM: Toward Accurate and Hardware-Compatible Post-Training Segment Anything Model Quantization FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:52:23.026421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 915de67a-f83b-483f-b78d-610083b4c6dd · inbound

QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models cites this paper.

QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T17:59:29.255532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3dda8ae6-edee-4443-8ece-d505f9f41c0c · inbound

DFQ-ViT: Data-Free Quantization for Vision Transformers without Fine-tuning cites this paper.

DFQ-ViT: Data-Free Quantization for Vision Transformers without Fine-tuning FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T16:09:08.506328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9c4783a-5ba4-473f-a25e-ee9b7db85337 · inbound

eMamba: Efficient Acceleration Framework for Mamba Models in Edge Computing cites this paper.

eMamba: Efficient Acceleration Framework for Mamba Models in Edge Computing FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-05T20:34:27.268158Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31176dff-1fb9-4324-8672-a5e09ff4f651 · inbound

QFlash: Bridging Quantization and Memory Efficiency in Vision Transformer Attention cites this paper.

QFlash: Bridging Quantization and Memory Efficiency in Vision Transformer Attention FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:31:13.479624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2e38025b-8abc-4a46-8408-e83e06d95f3d · inbound

Colinearity Decay: Training Quantization-Friendly ViTs with Outlier Decay cites this paper.

Colinearity Decay: Training Quantization-Friendly ViTs with Outlier Decay FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:56:07.893011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0f5789ce-35ae-4d94-a9ee-fb87dae9e3f3 · inbound

CAR-SAM: Cross-Attention Reconstruction for Post-Training Quantization of the Segment Anything Model cites this paper.

CAR-SAM: Cross-Attention Reconstruction for Post-Training Quantization of the Segment Anything Model FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-19T21:32:47.899827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f3757210-5281-41e8-826c-9eb63eeab1ab · inbound

Selective Coupling of Decoupled Informative Regions: Masked Attention Alignment for Data-Free Quantization of Vision Transformers cites this paper.

Selective Coupling of Decoupled Informative Regions: Masked Attention Alignment for Data-Free Quantization of Vision Transformers FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-02T07:16:44.937175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0ceff618-9cbb-4b9b-a9a9-5e3045c750e3 · inbound

ScalePredictor: Instance-aware Scale Learning for Accurate Quantization of Vision Transformers cites this paper.

ScalePredictor: Instance-aware Scale Learning for Accurate Quantization of Vision Transformers FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T08:09:41.097523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6b620890-16d1-4c0b-9aec-493f4066a0df · inbound

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference cites this paper.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-03T04:17:36.630458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2b593858-c1d8-46fa-a10a-f53efdfa4377 · inbound

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference cites this paper.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.