Paper Citation Record · LEDGER

Separable Self-attention for Mobile Vision Transformers

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 21 inbound Pith citation observations for arXiv:2206.02680.

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

pith.paper-citation-record.v1
2206.02680 v1

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measured 0 of 0 reference resolution

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measured 21 of 21 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 21 of 21 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T20:52:32.830789Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T04:55:23.534879Z

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Outbound references

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Pith citing papers

Observation 4b7d7983-24ca-4ce7-8afd-52914d7b8201 · inbound

ContextFormer: Redefining Efficiency in Semantic Segmentation cites this paper.

ContextFormer: Redefining Efficiency in Semantic Segmentation Separable Self-attention for Mobile Vision Transformers

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-09T20:52:32.830789Z

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Unavailable: canonical work link unavailable.

Observation 4efe5668-7c42-4b1a-a5df-d2f1acd50a84 · inbound

Partial Channel Network: Compute Fewer, Perform Better cites this paper.

Partial Channel Network: Compute Fewer, Perform Better Separable Self-attention for Mobile Vision Transformers

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T15:49:53.522971Z

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Observation 89bc9b10-f41a-4a18-bc44-be5888c6bac9 · inbound

MicroViT: A Vision Transformer with Low Complexity Self Attention for Edge Device cites this paper.

MicroViT: A Vision Transformer with Low Complexity Self Attention for Edge Device Separable Self-attention for Mobile Vision Transformers

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-08T17:58:04.772771Z

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Observation 52a83080-a4c3-44d9-8c79-bbd2488af98c · inbound

MoENAS: Mixture-of-Expert based Neural Architecture Search for jointly Accurate, Fair, and Robust Edge Deep Neural Networks cites this paper.

MoENAS: Mixture-of-Expert based Neural Architecture Search for jointly Accurate, Fair, and Robust Edge Deep Neural Networks Separable Self-attention for Mobile Vision Transformers

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-08T12:54:26.985619Z

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Observation a08e2317-556b-433d-b4ad-0f93a07ecdb3 · inbound

RePaViT: Scalable Vision Transformer Acceleration via Structural Reparameterization on Feedforward Network Layers cites this paper.

RePaViT: Scalable Vision Transformer Acceleration via Structural Reparameterization on Feedforward Network Layers Separable Self-attention for Mobile Vision Transformers

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T13:27:49.390514Z

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Observation 913e3a36-7347-4f15-9c8e-efebe2327ba1 · inbound

MAC-Gaze: Motion-Aware Continual Calibration for Mobile Gaze Tracking cites this paper.

MAC-Gaze: Motion-Aware Continual Calibration for Mobile Gaze Tracking Separable Self-attention for Mobile Vision Transformers

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T13:06:50.091689Z

Source-reported events for the cited work

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Observation add36f08-bf81-4b26-85e5-b8f7e260b0b7 · inbound

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding cites this paper.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Separable Self-attention for Mobile Vision Transformers

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T04:39:08.980998Z

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Observation c0269a27-8f9e-45f0-a9ba-5511f777d021 · inbound

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices cites this paper.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices Separable Self-attention for Mobile Vision Transformers

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:08.801986Z

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Observation 0740fd8c-70f7-43da-a3b8-73aea374fe41 · inbound

LAID: Lightweight AI-Generated Image Detection in Spatial and Spectral Domains cites this paper.

LAID: Lightweight AI-Generated Image Detection in Spatial and Spectral Domains Separable Self-attention for Mobile Vision Transformers

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T19:36:15.874299Z

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Unavailable: canonical work link unavailable.

Observation 5fb90801-37e1-4387-97c3-8e1e40261856 · inbound

Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models cites this paper.

Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Separable Self-attention for Mobile Vision Transformers

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T15:28:08.967431Z

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Observation 153ebc47-14e2-4368-9189-86f6dcd7815a · inbound

Foundation Models and Transformers for Anomaly Detection: A Survey cites this paper.

Foundation Models and Transformers for Anomaly Detection: A Survey Separable Self-attention for Mobile Vision Transformers

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:52.190864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 220150ea-8be2-4c41-ad5e-e18e019d7a0b · inbound

Lightweight Backbone Networks Only Require Adaptive Lightweight Self-Attention Mechanisms cites this paper.

Lightweight Backbone Networks Only Require Adaptive Lightweight Self-Attention Mechanisms Separable Self-attention for Mobile Vision Transformers

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T05:44:19.808852Z

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Observation 79c8756f-1f73-4f5d-b01f-17d17222a623 · inbound

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation cites this paper.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Separable Self-attention for Mobile Vision Transformers

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T05:58:37.681478Z

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Unavailable: canonical work link unavailable.

Observation 3993bf6e-7adb-48ec-a860-de2171a9eb93 · inbound

CoAtNeXt:An Attention-Enhanced ConvNeXtV2-Transformer Hybrid Model for Gastric Tissue Classification cites this paper.

CoAtNeXt:An Attention-Enhanced ConvNeXtV2-Transformer Hybrid Model for Gastric Tissue Classification Separable Self-attention for Mobile Vision Transformers

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-04T19:28:48.487473Z

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Unavailable: canonical work link unavailable.

Observation bda9ebd6-818f-4387-b16b-baffd298ab42 · inbound

CNN-ViT Fusion with Adaptive Attention Gate for Brain Tumor MRI Classification: A Hybrid Deep Learning Model cites this paper.

CNN-ViT Fusion with Adaptive Attention Gate for Brain Tumor MRI Classification: A Hybrid Deep Learning Model Separable Self-attention for Mobile Vision Transformers

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:31:12.069344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a58d9429-2e27-485f-bde2-d927222ab0bc · inbound

MicroViTv2: Beyond the FLOPS for Edge Energy-Friendly Vision Transformers cites this paper.

MicroViTv2: Beyond the FLOPS for Edge Energy-Friendly Vision Transformers Separable Self-attention for Mobile Vision Transformers

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:21:23.762260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d66303f0-bcbb-4871-ae3d-073c2628ede4 · inbound

TCP-SSM: Efficient Vision State Space Models with Token-Conditioned Poles cites this paper.

TCP-SSM: Efficient Vision State Space Models with Token-Conditioned Poles Separable Self-attention for Mobile Vision Transformers

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-13T01:52:04.902463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b27fc5e1-5555-4388-baea-5ae22e9b7279 · inbound

MR2-ByteTrack: CNN and Transformer-based Video Object Detection for AI-augmented Embedded Vision Sensor Nodes cites this paper.

MR2-ByteTrack: CNN and Transformer-based Video Object Detection for AI-augmented Embedded Vision Sensor Nodes Separable Self-attention for Mobile Vision Transformers

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-19T15:27:38.925304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bdea8d65-ecd9-439f-8809-94ebf3f68882 · inbound

Do Synthetic Brain MRIs Reliably Improve Tumour Classification? A StyleGAN2-ADA Class-Plane Augmentation Study on BRISC 2025 cites this paper.

Do Synthetic Brain MRIs Reliably Improve Tumour Classification? A StyleGAN2-ADA Class-Plane Augmentation Study on BRISC 2025 Separable Self-attention for Mobile Vision Transformers

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-25T04:55:23.538490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation efdc97a7-c351-4f85-8dea-eaeb47bfb5e6 · inbound

UltraViT: Latency-Optimized On-device Vision Encoder for Large Vision-Language Models cites this paper.

UltraViT: Latency-Optimized On-device Vision Encoder for Large Vision-Language Models Separable Self-attention for Mobile Vision Transformers

Reference 36

Resolution: unresolved; no resolver link, observed 2026-07-31T00:01:35.791774Z

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Unavailable: canonical work link unavailable.

Observation c13f6a43-d919-4f4e-9238-778ef9ca297c · inbound

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model cites this paper.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Separable Self-attention for Mobile Vision Transformers

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T11:29:26.230024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.