Paper Citation Record · LEDGER

iFormer: Integrating ConvNet and Transformer for Mobile Application

As of 11 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 3 inbound Pith citation observations for arXiv:2501.15369.

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

pith.paper-citation-record.v1
2501.15369 v2

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:24:25.877527Z

measured 37 of 37 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T04:27:04.776920Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T23:23:15.964658Z

Reference resolution

34 of 34 outbound references displayed

verified exact4
verified fuzzy9
unresolved19
parse uncertain0
malformed identifier2
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External citation measurements

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Outbound references

Observation d9190fcc-6032-4f4f-88fa-9302b6c922f6 · outbound

This paper cites Output Size(Downs.

iFormer: Integrating ConvNet and Transformer for Mobile Application Output Size(Downs

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:24:26.420195Z

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Observation 7f3c098c-800b-4983-95c1-104a0790f215 · outbound

This paper cites As summarized in Table 14, split and concatenate operations introduce additional runtime.

iFormer: Integrating ConvNet and Transformer for Mobile Application As summarized in Table 14, split and concatenate operations introduce additional runtime

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:24:26.435576Z

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Observation 73295e50-bd7b-44eb-b709-5d74ba03182c · outbound

This paper cites Learning Efficient Vision Transformers via Fine-Grained Manifold Distillation.

iFormer: Integrating ConvNet and Transformer for Mobile Application Learning Efficient Vision Transformers via Fine-Grained Manifold Distillation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T14:24:25.750290Z

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Observation 7a3b7a70-7319-4ecc-a751-9d8bd79b3ba0 · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

iFormer: Integrating ConvNet and Transformer for Mobile Application MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T14:24:25.760161Z

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Observation 6cc8cb61-63e8-45d7-9780-854cf91be753 · outbound

This paper cites GhostNetV3: Exploring the Training Strategies for Compact Models.

iFormer: Integrating ConvNet and Transformer for Mobile Application GhostNetV3: Exploring the Training Strategies for Compact Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T14:24:25.771228Z

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Observation 53bfef61-5a38-4b72-8f53-9cd9dcc763f3 · outbound

This paper cites MoCoViT: Mobile Convolutional Vision Transformer.

iFormer: Integrating ConvNet and Transformer for Mobile Application MoCoViT: Mobile Convolutional Vision Transformer

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:24:26.233526Z

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Observation 4cddf970-c1f2-44b6-bf17-4e5d2527574a · outbound

This paper cites MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer.

iFormer: Integrating ConvNet and Transformer for Mobile Application MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T14:24:25.785453Z

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Observation c0fdd873-3d36-4c96-b64a-1e81d10f7bd8 · outbound

This paper cites Separable Self-attention for Mobile Vision Transformers.

iFormer: Integrating ConvNet and Transformer for Mobile Application Separable Self-attention for Mobile Vision Transformers

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T14:24:25.789994Z

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Observation 63e6987e-b72d-4c5b-88b5-16ecd4df25de · outbound

This paper cites LowFormer: Hardware Efficient Design for Convolutional Transformer Backbones.

iFormer: Integrating ConvNet and Transformer for Mobile Application LowFormer: Hardware Efficient Design for Convolutional Transformer Backbones

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:24:26.166457Z

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Observation 2f06eaa5-dab3-4594-b0eb-7045037725cd · outbound

This paper cites MobileNetV4 -- Universal Models for the Mobile Ecosystem.

iFormer: Integrating ConvNet and Transformer for Mobile Application MobileNetV4 -- Universal Models for the Mobile Ecosystem

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T14:24:25.799146Z

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Observation c745476a-4025-4fa0-9376-8401bad46c8c · outbound

This paper cites GLU Variants Improve Transformer.

iFormer: Integrating ConvNet and Transformer for Mobile Application GLU Variants Improve Transformer

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T14:24:25.803619Z

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Observation 71663fe0-a633-4f04-b4ab-0f60da80557e · outbound

This paper cites Ghostnetv2: Enhance cheap operation with long-range attention.

iFormer: Integrating ConvNet and Transformer for Mobile Application Ghostnetv2: Enhance cheap operation with long-range attention

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:24:26.541431Z

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Observation 0f186b2d-b4b8-4a15-bcef-c9f390d1149f · outbound

This paper cites Attention Is All You Need.

iFormer: Integrating ConvNet and Transformer for Mobile Application Attention Is All You Need

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T14:24:25.812958Z

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Observation 0a17cbb7-aec3-42a5-8271-f88ba864f4c4 · outbound

This paper cites SeaFormer++: Squeeze-enhanced Axial Transformer for Mobile Visual Recognition.

iFormer: Integrating ConvNet and Transformer for Mobile Application SeaFormer++: Squeeze-enhanced Axial Transformer for Mobile Visual Recognition

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T14:24:25.817540Z

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Observation c844e49d-22ac-4ed3-a09e-930b5f1000b2 · outbound

This paper cites Focal modulation networks.

iFormer: Integrating ConvNet and Transformer for Mobile Application Focal modulation networks

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:24:26.526772Z

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Observation c246c064-af1e-483f-870b-0cbe3c62d975 · outbound

This paper cites ParC-Net: Position Aware Circular Convolution with Merits from ConvNets and Transformer.

iFormer: Integrating ConvNet and Transformer for Mobile Application ParC-Net: Position Aware Circular Convolution with Merits from ConvNets and Transformer

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:24:25.976570Z

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Observation 795abad9-8884-44ec-958e-30af093881fe · outbound

This paper cites Rethinking mobile block for efficient attention-based models.

iFormer: Integrating ConvNet and Transformer for Mobile Application Rethinking mobile block for efficient attention-based models

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:24:26.512617Z

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Observation 77bdea01-bb04-4242-9ce3-cd24fa52e2ee · outbound

This paper cites CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications.

iFormer: Integrating ConvNet and Transformer for Mobile Application CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T14:24:25.835655Z

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Observation 8c5290ac-db1f-4e98-8388-26e1bddd47c1 · outbound

This paper cites RepNeXt: A Fast Multi-Scale CNN using Structural Reparameterization.

iFormer: Integrating ConvNet and Transformer for Mobile Application RepNeXt: A Fast Multi-Scale CNN using Structural Reparameterization

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:24:25.937495Z

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 89668b24-70ff-4a2a-90ea-e270b573b731 · outbound

This paper cites Lightweight Vision Transformer with Cross Feature Attention.

iFormer: Integrating ConvNet and Transformer for Mobile Application Lightweight Vision Transformer with Cross Feature Attention

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T14:24:25.844988Z

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Observation e32bfcb0-3efa-4e85-8b7a-f1d2cd046961 · outbound

This paper cites training config iFormer-T/S/M/L/H resolution 2242 weight init trunc.

iFormer: Integrating ConvNet and Transformer for Mobile Application training config iFormer-T/S/M/L/H resolution 2242 weight init trunc

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:24:26.497833Z

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 4488822f-27f6-4572-a599-a7890b132daa · outbound

This paper cites We hypothesize that implementing more effective spatial mixing before the FFN diminishes its significance.

iFormer: Integrating ConvNet and Transformer for Mobile Application We hypothesize that implementing more effective spatial mixing before the FFN diminishes its significance

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:24:26.466290Z

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 56003c13-3b83-42a6-a255-53505e453a0c · outbound

This paper cites Table 13: Object detection & Semantic segmentation results using backbone pretrained for 450 epochs.

iFormer: Integrating ConvNet and Transformer for Mobile Application Table 13: Object detection & Semantic segmentation results using backbone pretrained for 450 epochs

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:24:26.450582Z

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

Observation 45435208-475e-4761-9f79-53af2839b018 · outbound

This paper cites Channel Chunking.

iFormer: Integrating ConvNet and Transformer for Mobile Application Channel Chunking

Reference 34

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T14:24:26.404399Z

Source-reported events for the cited work

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Observation 5ce762c7-d284-4a90-bc61-6bbf7e0fdd05 · outbound

This paper cites Token Merging: Your ViT But Faster.

iFormer: Integrating ConvNet and Transformer for Mobile Application Token Merging: Your ViT But Faster

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-10T14:24:25.725000Z

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Observation acee2e1e-a389-45ff-a941-c1a7ba5386f2 · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

iFormer: Integrating ConvNet and Transformer for Mobile Application Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-10T14:24:25.765485Z

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Observation 6d2e531d-9dec-42c3-bb3b-85c903ab4906 · outbound

This paper cites Efficient Modulation for Vision Networks.

iFormer: Integrating ConvNet and Transformer for Mobile Application Efficient Modulation for Vision Networks

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-10T14:24:25.780813Z

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Observation 6d2d82fb-c37b-488b-b7c5-7bc20ddac949 · outbound

This paper cites Conditional Positional Encodings for Vision Transformers.

iFormer: Integrating ConvNet and Transformer for Mobile Application Conditional Positional Encodings for Vision Transformers

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-10T14:24:25.735397Z

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Observation d0ac8a56-b136-4a20-838c-6627d1354dc6 · outbound

This paper cites In https://github.com/apple/coremltools.

iFormer: Integrating ConvNet and Transformer for Mobile Application In https://github.com/apple/coremltools

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:24:26.556111Z

Source-reported events for the cited work

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Observation 6c52a8c8-07ca-4843-ac6c-761b8f359fae · outbound

This paper cites FasterViT: Fast Vision Transformers with Hierarchical Attention.

iFormer: Integrating ConvNet and Transformer for Mobile Application FasterViT: Fast Vision Transformers with Hierarchical Attention

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-10T14:24:25.755297Z

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Observation 437d2063-a54b-45e8-815a-150dd8036cad · outbound

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

iFormer: Integrating ConvNet and Transformer for Mobile Application An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-10T14:24:25.745285Z

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Observation 3026d7bf-2d61-4498-b2f7-b9c589e2b2d1 · outbound

This paper cites MMDetection: Open MMLab Detection Toolbox and Benchmark.

iFormer: Integrating ConvNet and Transformer for Mobile Application MMDetection: Open MMLab Detection Toolbox and Benchmark

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-10T14:24:25.730063Z

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Observation 80efaf35-15d9-41a1-85c2-5f8822c79535 · outbound

This paper cites Layer Normalization.

iFormer: Integrating ConvNet and Transformer for Mobile Application Layer Normalization

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-10T14:24:25.719446Z

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Observation ed4b637f-606e-4314-8717-717759189b8a · outbound

This paper cites It is possible to further improve performance by adjusting the learning rates for different model variants, which we will explore in the future.

iFormer: Integrating ConvNet and Transformer for Mobile Application It is possible to further improve performance by adjusting the learning rates for different model variants, which we will explore in the future

Reference 4096

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T14:24:26.482130Z

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

Pith citing papers

Observation 0e5cecb1-0c79-4215-894e-d4d26fb69b6b · inbound

GasTwinFormer: A Hybrid Vision Transformer for Livestock Methane Emission Segmentation and Dietary Classification in Optical Gas Imaging cites this paper.

GasTwinFormer: A Hybrid Vision Transformer for Livestock Methane Emission Segmentation and Dietary Classification in Optical Gas Imaging iFormer: Integrating ConvNet and Transformer for Mobile Application

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-05T18:15:19.607391Z

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Observation 783770f6-731f-486d-a7df-e92b17d11d27 · inbound

TRACE: Thermal Recognition Attentive-Framework for CO2 Emissions from Livestock cites this paper.

TRACE: Thermal Recognition Attentive-Framework for CO2 Emissions from Livestock iFormer: Integrating ConvNet and Transformer for Mobile Application

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-14T23:23:15.971581Z

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

Observation a96d29e5-84d0-42fb-8768-a1e51d642c57 · inbound

URNet: A Unified Reparameterized Network for Efficient RGB-D Semantic Segmentation cites this paper.

URNet: A Unified Reparameterized Network for Efficient RGB-D Semantic Segmentation iFormer: Integrating ConvNet and Transformer for Mobile Application

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-08T04:27:04.776920Z

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