Paper Citation Record · LEDGER

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis

As of 11 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2412.18178.

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

pith.paper-citation-record.v1
2412.18178 v2

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:01:51.129920Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

verified exact2
verified fuzzy26
unresolved15
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9733f64f-5744-44ae-be18-d14126885a46 · outbound

This paper cites Deep learning,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Deep learning,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:52.506567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5c684306-c734-4358-9b56-01a8eeb157ea · outbound

This paper cites Cross-modal causal relational reasoning for event-level visual question answering,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Cross-modal causal relational reasoning for event-level visual question answering,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:52.485152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a2587e0c-d7b8-4790-8072-b9011176c849 · outbound

This paper cites Visual causal scene refinement for video question answering,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Visual causal scene refinement for video question answering,

Reference 3

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Source-reported events for the cited work

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Observation 8be91b8d-8bc9-4178-8ee8-7757570903cc · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Imagenet classification with deep convolutional neural networks,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:52.443684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d7dbf352-130a-46fd-afd1-0526b5872f36 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 5

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Source-reported events for the cited work

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Observation 7b5e4a79-2af3-4789-80ff-cea96b5ad96e · outbound

This paper cites Transformers in vision: A survey,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Transformers in vision: A survey,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:52.422552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0b79c0c8-8748-4457-9c6e-8384cad04039 · outbound

This paper cites Attention is all you need,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Attention is all you need,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:52.399663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation eeeb9e82-af74-43df-a15d-25f3b9ee0a81 · outbound

This paper cites MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-11T05:01:51.556459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 07e5d5cf-2eeb-4d46-83d8-2e0ebf8db99f · outbound

This paper cites Mamba: A scalable state-space model for sequence modeling,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Mamba: A scalable state-space model for sequence modeling,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:52.375091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 788a99e9-4d2f-4fae-84a7-eec4c54e6f9c · outbound

This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 10

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Source-reported events for the cited work

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Observation 2276e3f5-e7c3-4507-8bbe-3beaa3095555 · outbound

This paper cites Were rnns all we needed?,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Were rnns all we needed?,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:52.353929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 75b47fcb-e7d5-45a6-aeac-c570085267cf · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T05:01:50.828594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ac93c7d-4c78-4be1-a1d8-ef75a3202804 · outbound

This paper cites Deep residual learning for image recognition,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Deep residual learning for image recognition,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:52.327470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 495f7b9f-9f39-4406-9af3-5fc98b78271e · outbound

This paper cites Densely connected convolutional networks,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Densely connected convolutional networks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:52.300103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 30b470bd-3b5f-43cd-932e-0f2734815d4d · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:52.260300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0ab436b2-b8cb-438d-9193-ea4c6c8d747e · outbound

This paper cites Efficiently modeling long sequences with structured state spaces,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Efficiently modeling long sequences with structured state spaces,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:52.221188Z

Source-reported events for the cited work

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Observation dde98408-d494-4c6a-8b2b-3f1085eb1cf9 · outbound

This paper cites Trans4mer: State-space models for vision,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Trans4mer: State-space models for vision,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:52.187324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 38cb0dd1-e9bb-420e-8f04-903fa2c48617 · outbound

This paper cites U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

Reference 18

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Source-reported events for the cited work

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Observation 1e21440d-301c-4aeb-a894-b0ad396eb474 · outbound

This paper cites Vision mamba: Efficient visual representation learning with bidirectional state space model,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Vision mamba: Efficient visual representation learning with bidirectional state space model,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:52.156468Z

Source-reported events for the cited work

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Observation 4eef11fb-9f1b-4a10-bdf2-afc5a6450253 · outbound

This paper cites AmbieGen: A Search-based Framework for Autonomous Systems Testing.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis AmbieGen: A Search-based Framework for Autonomous Systems Testing

Reference 20

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Source-reported events for the cited work

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Observation 947ffc0a-735c-418f-85aa-698f72213363 · outbound

This paper cites Finding structure in time,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Finding structure in time,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:52.121734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 873293aa-31e3-4ea5-9fc4-be70ad4ceea5 · outbound

This paper cites Learning long-term dependencies with gradient descent is difficult,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Learning long-term dependencies with gradient descent is difficult,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:52.093615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fd802d41-30d3-45d8-8c0b-f03a8761b1f9 · outbound

This paper cites Long short-term memory,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Long short-term memory,

Reference 23

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Source-reported events for the cited work

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Observation b524a44d-b908-4629-9803-aab37ea7e7f2 · outbound

This paper cites Log-concavity and log-convexity of series containing multiple Pochhammer symbols.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Log-concavity and log-convexity of series containing multiple Pochhammer symbols

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2e4108e2-5fd7-4db0-b9c6-2268b9d6d8fc · outbound

This paper cites Complex diagonal recurrence and exponential gating for rnns,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Complex diagonal recurrence and exponential gating for rnns,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:52.030486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 13d78d51-2073-4f56-8427-3a2aeb896d55 · outbound

This paper cites Minrnns: Simplified recurrent models for efficient sequence modeling,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Minrnns: Simplified recurrent models for efficient sequence modeling,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:51.991384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3e349440-627c-48e8-aede-5ca06111b79c · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Imagenet: A large-scale hierarchical image database,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:51.963635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6c646715-58ca-495e-966e-624831cc9755 · outbound

This paper cites Training data-efficient image transformers & distillation through attention.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Training data-efficient image transformers & distillation through attention

Reference 28

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Source-reported events for the cited work

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Observation 0cefa0bc-4ad4-42fa-ac5b-a34be5ac0e72 · outbound

This paper cites Decoupled Weight Decay Regularization.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Decoupled Weight Decay Regularization

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T05:01:50.984867Z

Source-reported events for the cited work

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Observation 18b73420-54b1-4875-b2f3-f21e56e3d160 · outbound

This paper cites Acceleration of stochastic approximation by averaging,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Acceleration of stochastic approximation by averaging,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:51.923109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1b80910a-3952-46de-ade5-c8e6dbc1a7fb · outbound

This paper cites Aggregated residual transformations for deep neural networks,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Aggregated residual transformations for deep neural networks,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:51.884750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fd8693fd-c58a-4d73-842f-f9c7546f17b6 · outbound

This paper cites Designing network design spaces,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Designing network design spaces,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:51.856503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b92df596-aa1e-4ebf-8c19-9578456936cf · outbound

This paper cites Upernet: Unified perceptual parsing for scene understanding,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Upernet: Unified perceptual parsing for scene understanding,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:51.817371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b52d23e7-1963-42e4-b081-5803ff554a7f · outbound

This paper cites Semantics-aware adaptive knowledge distillation for sensor-to-vision action recognition,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Semantics-aware adaptive knowledge distillation for sensor-to-vision action recognition,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:51.785613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8aeb352c-6164-4dad-b2c9-32abb4bb9050 · outbound

This paper cites Tcgl: Temporal contrastive graph for self-supervised video representation learning,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Tcgl: Temporal contrastive graph for self-supervised video representation learning,

Reference 35

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Source-reported events for the cited work

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Observation aa344d18-f5e1-42cf-a086-63614c582120 · outbound

This paper cites Enhanced soft label for semi-supervised semantic segmentation,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Enhanced soft label for semi-supervised semantic segmentation,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:51.743746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0f70af99-89a1-4ac6-a606-46baf55d4e9b · outbound

This paper cites Causal reasoning meets visual representation learning: A prospective study,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Causal reasoning meets visual representation learning: A prospective study,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:51.706889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation abd95ae7-18a7-4752-a50e-7cc2b2316d5a · outbound

This paper cites Skeletonmae: graph-based masked autoencoder for skeleton sequence pre-training,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Skeletonmae: graph-based masked autoencoder for skeleton sequence pre-training,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-11T05:01:51.064512Z

Source-reported events for the cited work

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Observation fda35e08-fd05-4dc1-b6c5-cda643f0afa0 · outbound

This paper cites Hybrid-order representation learning for electricity theft detection,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Hybrid-order representation learning for electricity theft detection,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T05:01:51.081062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a6a0050-3fdf-4fe2-bfae-367725de82c9 · outbound

This paper cites Cross-Modal Causal Intervention for Medical Report Generation.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Cross-Modal Causal Intervention for Medical Report Generation

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T05:01:51.089377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56bfea99-499b-43b3-93d0-4bac88f49791 · outbound

This paper cites Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-11T05:01:51.106468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61cd60ed-b02e-434d-bfaa-b35568a0a1fc · outbound

This paper cites Towards Long-Horizon Vision-Language Navigation: Platform, Benchmark and Method.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Towards Long-Horizon Vision-Language Navigation: Platform, Benchmark and Method

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T05:01:51.113626Z

Source-reported events for the cited work

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Observation 3a182843-6dd1-40b9-b35b-b35a5e1a7391 · outbound

This paper cites ODMixer: Fine-grained Spatial-temporal MLP for Metro Origin-Destination Prediction.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis ODMixer: Fine-grained Spatial-temporal MLP for Metro Origin-Destination Prediction

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-11T05:01:51.222208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1c4862db-4f17-470d-96a9-c40681087b38 · outbound

This paper cites Diversity matters: User-centric multi-interest learning for conversational movie recommendation,.

VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis Diversity matters: User-centric multi-interest learning for conversational movie recommendation,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:01:51.628481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.