Paper Citation Record · LEDGER

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

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 22 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

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measured 22 of 22 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 22 of 22 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T16:20:03.015840Z

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

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Outbound references

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Pith citing papers

Observation 8b84504f-67ac-4341-814b-58b247eef34d · inbound

ElastiFormer: Learned Redundancy Reduction in Transformer via Self-Distillation cites this paper.

ElastiFormer: Learned Redundancy Reduction in Transformer via Self-Distillation FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-12T14:38:51.889606Z

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Observation 625db241-1661-40fc-a0c5-98b2bd369dfa · inbound

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices cites this paper.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-12T16:20:03.015840Z

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Observation dfed0eda-b151-4b48-bcd7-971143a5a032 · inbound

Behavior Backdoor for Deep Learning Models cites this paper.

Behavior Backdoor for Deep Learning Models FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:20.031345Z

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Observation bc1042d6-1c5c-4d8e-8aee-c62d5716e659 · inbound

Efficiency Meets Fidelity: A Novel Quantization Framework for Stable Diffusion cites this paper.

Efficiency Meets Fidelity: A Novel Quantization Framework for Stable Diffusion FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T19:30:02.692722Z

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Observation 56572403-d4ca-4df8-b540-72bce5f2cbe6 · inbound

TTAQ: Towards Stable Post-training Quantization in Continuous Domain Adaptation cites this paper.

TTAQ: Towards Stable Post-training Quantization in Continuous Domain Adaptation FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:15.811044Z

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Observation 87709b98-df1d-47bd-af8c-e31829129ee5 · inbound

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks cites this paper.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T13:04:27.589120Z

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Observation 94859a6e-7bba-4383-a848-72fcf3b48daa · inbound

V"Mean"ba: Visual State Space Models only need 1 hidden dimension cites this paper.

V"Mean"ba: Visual State Space Models only need 1 hidden dimension FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T10:29:37.013956Z

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Observation 64e1295e-7d39-4963-bf94-e888a74929cb · inbound

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models cites this paper.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T22:04:24.249726Z

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Observation bee3ca99-c36f-4f52-aa38-66f380365cb5 · inbound

UAV-Assisted Real-Time Disaster Detection Using Optimized Transformer Model cites this paper.

UAV-Assisted Real-Time Disaster Detection Using Optimized Transformer Model FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T17:34:37.551465Z

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Observation aef689ca-d152-491d-801d-80513d939892 · inbound

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods cites this paper.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.431120Z

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Observation ea4c122b-7808-4fde-a789-dda26a000e9b · inbound

Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization cites this paper.

Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T11:08:55.303914Z

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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 2026-08-13T06:32:02.005865+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

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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

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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

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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 2026-08-13T06:32:02.005865+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 2026-08-13T06:32:02.005865+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 2026-08-13T06:32:02.005865+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 2026-08-13T06:32:02.005865+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 2026-08-13T06:32:02.005865+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

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+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

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Unavailable: canonical work link unavailable.