Paper Citation Record · LEDGER

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

As of 13 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 100 inbound Pith citation observations for arXiv:1704.04861.

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

pith.paper-citation-record.v1
1704.04861 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-11T02:50:40.222229Z

measured 137 of 137 standing notices

One-hop event checks from named stored sources.

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

measured 100 of 468 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T20:31:53.455288Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

37 of 37 outbound references displayed

verified exact22
verified fuzzy12
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

9899
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 7ecbca0c-c9e7-408a-bc2b-555fe226ecf0 · outbound

This paper cites Abadi, A.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Abadi, A

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T02:50:41.026356Z

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Observation 18af8315-79b6-4cad-9a71-c446fcf65b03 · outbound

This paper cites Compressing Neural Networks with the Hashing Trick.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Compressing Neural Networks with the Hashing Trick

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:50:40.254493Z

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Observation 7dbf3583-66f0-430d-bd96-063ac2938f72 · outbound

This paper cites Xception: Deep Learning with Depthwise Separable Convolutions.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Xception: Deep Learning with Depthwise Separable Convolutions

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:50:40.260385Z

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Observation 90f1da44-f20d-460a-b1dc-7c44fc04e113 · outbound

This paper cites Training deep neural networks with low precision multiplications.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Training deep neural networks with low precision multiplications

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:50:40.391966Z

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Observation bece72cc-abbd-4cb8-b50e-3a2b2e73090d · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-12T15:59:28.637418Z

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Observation fcee2f61-7662-459b-98d8-89112e4a390a · outbound

This paper cites Hays and A.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Hays and A

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T02:50:40.474778Z

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Observation 93a1215c-eec8-4274-b61d-1cfaf636dffc · outbound

This paper cites Hays and A.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Hays and A

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T02:50:40.496633Z

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Observation b9bd5da3-9949-45a1-9bf8-3668c8dff5c1 · outbound

This paper cites Deep Residual Learning for Image Recognition.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Deep Residual Learning for Image Recognition

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:02:34.163591Z

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Observation c383d42e-bec8-4b4e-943a-f17110cc2082 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Distilling the Knowledge in a Neural Network

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-11T02:50:40.275952Z

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Observation 5cda4d62-f2d6-4247-acad-8aa8cc3e61b6 · outbound

This paper cites Speed/accuracy trade-offs for modern convolutional object detectors.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Speed/accuracy trade-offs for modern convolutional object detectors

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:50:40.289028Z

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Observation 2927e1ac-948d-4c86-b4c5-705189225048 · outbound

This paper cites Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:50:40.305343Z

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Observation 2a92ae9a-6cdb-4fb2-bc9a-93eae5084946 · outbound

This paper cites SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:50:40.329111Z

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Observation f8a11df2-4e17-4254-904c-3468f0c832b7 · outbound

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

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-13T17:19:17.426681Z

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Observation 2a8d11cf-ef3b-499e-826d-c2f0c25fa3b3 · outbound

This paper cites Speeding up Convolutional Neural Networks with Low Rank Expansions.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Speeding up Convolutional Neural Networks with Low Rank Expansions

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:50:40.353109Z

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Observation 4df2b56a-98bb-480b-8b64-782e233af629 · outbound

This paper cites Caffe: Convolutional Architecture for Fast Feature Embedding.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Caffe: Convolutional Architecture for Fast Feature Embedding

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:50:40.365229Z

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Observation 1aca3ecb-b0a8-4732-93b1-8a948462aaef · outbound

This paper cites Flattened Convolutional Neural Networks for Feedforward Acceleration.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Flattened Convolutional Neural Networks for Feedforward Acceleration

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:50:40.380448Z

Source-reported events for the cited work

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Observation 904040e5-33f6-4b5f-9171-433ad2b8d7e6 · outbound

This paper cites Khosla, N.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Khosla, N

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T02:50:40.516330Z

Source-reported events for the cited work

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Observation 39ee994b-8591-4c67-a92b-62a104c041b7 · outbound

This paper cites The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-04T21:28:38.269230Z

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Observation 8e975697-900a-420f-8eb5-c9b68f2d586f · outbound

This paper cites Krizhevsky, I.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Krizhevsky, I

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T02:50:40.616305Z

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Observation 38e43cc4-6e71-424b-a9d3-be378afebeb1 · outbound

This paper cites Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:50:40.419670Z

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Observation 6feb3c9e-9846-49c3-8576-20b36fcb3d9f · outbound

This paper cites SSD: Single Shot MultiBox Detector.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications SSD: Single Shot MultiBox Detector

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:50:40.427205Z

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Observation 8f1c232c-67fd-4093-a6b3-dce9f09d3c4d · outbound

This paper cites XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:50:40.440656Z

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Observation 4cbc7853-65ea-4689-b770-8e8672a5adb6 · outbound

This paper cites an unresolved cited work.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Unresolved cited work

Reference 23

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Observation 9ffef865-5cbb-4cc7-9eed-91e73871b7ef · outbound

This paper cites Russakovsky, J.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Russakovsky, J

Reference 24

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Observation 91d329e9-9745-44ca-8d06-ccd3b328ca7e · outbound

This paper cites Schroff, D.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Schroff, D

Reference 25

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Observation 1be532aa-a4c2-4c2a-ad0a-06526f7fd03c · outbound

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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Unresolved cited work

Reference 26

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Observation 7e57a924-2555-41c6-bb90-44d70da7b47b · outbound

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

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-05-11T02:50:40.281302Z

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Observation 13dc2327-3511-4801-9528-bb2bf5c070f0 · outbound

This paper cites Sindhwani, T.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Sindhwani, T

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T02:50:40.887350Z

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Observation 7e72f03b-2691-4353-be7f-c8b5dae3a0ba · outbound

This paper cites Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:50:40.294584Z

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Observation 1f1d7d65-1e6f-4e93-a351-a83c70e6a77b · outbound

This paper cites Szegedy, W.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Szegedy, W

Reference 30

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Observation 88d5e690-7c53-475d-bff1-e893a388a51f · outbound

This paper cites Rethinking the Inception Architecture for Computer Vision.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Rethinking the Inception Architecture for Computer Vision

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:50:40.317927Z

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Observation 1f06e032-ff44-43d9-ba18-14882b70a0f5 · outbound

This paper cites Thomee, D.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Thomee, D

Reference 32

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Observation 6470c69d-084e-4573-bd21-fd1ee149be65 · outbound

This paper cites Tieleman and G.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Tieleman and G

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T02:50:40.927029Z

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Observation 248b7ed6-50fe-4730-ad94-108c40d062e5 · outbound

This paper cites Design of Efficient Convolutional Layers using Single Intra-channel Convolution, Topological Subdivisioning and Spatial "Bottleneck" Structure.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Design of Efficient Convolutional Layers using Single Intra-channel Convolution, Topological Subdivisioning and Spatial "Bottleneck" Structure

Reference 34

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Observation abeee3ba-b332-4bb8-b891-dd63170da786 · outbound

This paper cites Weyand, I.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Weyand, I

Reference 35

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Observation 5d702df5-15ab-4ad6-8a4a-3ded6a5f0978 · outbound

This paper cites Quantized Convolutional Neural Networks for Mobile Devices.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Quantized Convolutional Neural Networks for Mobile Devices

Reference 36

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Observation cb5b09a5-3232-4c44-804f-d5d8497ae694 · outbound

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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Unresolved cited work

Reference 37

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Pith citing papers

Observation 5998147c-ea28-4e28-9daa-c5a5be0bdbcc · inbound

Searching for Activation Functions cites this paper.

Searching for Activation Functions MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 10

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Observation 836328ca-3039-431e-861b-58ff5e1fc260 · inbound

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks cites this paper.

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 13

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Observation b60727d2-09ef-4685-b5dd-fa57f8cf7a1b · inbound

SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures cites this paper.

SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 11

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Observation ffbfa876-98d4-4798-89ba-519b953a9f10 · inbound

Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles cites this paper.

Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 9

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Observation 8171d9cb-bf6b-49ed-bee9-3f617cc63972 · inbound

Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases cites this paper.

Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-25T17:51:06.172811Z

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Observation 53cc54ca-605e-48bd-b5f0-731999b80a64 · inbound

ESNet: An Efficient Symmetric Network for Real-time Semantic Segmentation cites this paper.

ESNet: An Efficient Symmetric Network for Real-time Semantic Segmentation MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 29

Resolution: metadata mismatch; local_arxiv, observed 2026-05-25T17:51:05.984148Z

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SkyNet: A Champion Model for DAC-SDC on Low Power Object Detection cites this paper.

SkyNet: A Champion Model for DAC-SDC on Low Power Object Detection MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 11

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Observation 7731a200-64be-419f-8770-206c69be94ec · inbound

COP: Customized Deep Model Compression via Regularized Correlation-Based Filter-Level Pruning cites this paper.

COP: Customized Deep Model Compression via Regularized Correlation-Based Filter-Level Pruning MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-25T17:07:05.372960Z

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Observation 8e7acfd7-b59e-4ae9-9e48-1b88bfe62f43 · inbound

New pointwise convolution in Deep Neural Networks through Extremely Fast and Non Parametric Transforms cites this paper.

New pointwise convolution in Deep Neural Networks through Extremely Fast and Non Parametric Transforms MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 11

Resolution: metadata mismatch; local_arxiv, observed 2026-05-25T16:56:04.187269Z

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Observation 6b36d4af-4d23-43c3-9078-66ab2f27f86b · inbound

Weight Normalization based Quantization for Deep Neural Network Compression cites this paper.

Weight Normalization based Quantization for Deep Neural Network Compression MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-25T11:45:45.049598Z

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Observation aa4ba6c6-812f-4f0f-99ba-b0cbc165e273 · inbound

A Unified Optimization Approach for CNN Model Inference on Integrated GPUs cites this paper.

A Unified Optimization Approach for CNN Model Inference on Integrated GPUs MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-05-25T09:25:35.326768Z

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Observation 8c678bf6-5c60-40ad-81e7-b3d6f060cb0b · inbound

Slim-CNN: A Light-Weight CNN for Face Attribute Prediction cites this paper.

Slim-CNN: A Light-Weight CNN for Face Attribute Prediction MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 8

Resolution: metadata mismatch; local_arxiv, observed 2026-05-25T09:56:51.327263Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b017ad4f-b67e-4c20-b0e4-34516947119b · inbound

Genetic Network Architecture Search cites this paper.

Genetic Network Architecture Search MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 14

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Reference 18

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Introduction to Camera Pose Estimation with Deep Learning MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 58

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Reference 14

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Separable Convolutional LSTMs for Faster Video Segmentation cites this paper.

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Reference 26

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Reference 21

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Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images cites this paper.

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Reference 34

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Open DNN Box by Power Side-Channel Attack MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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Co-Evolutionary Compression for Unpaired Image Translation cites this paper.

Co-Evolutionary Compression for Unpaired Image Translation MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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Adapted Center and Scale Prediction: More Stable and More Accurate cites this paper.

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YOLOv4: Optimal Speed and Accuracy of Object Detection cites this paper.

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Reference 28

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Observation 78b03927-696a-4620-9c1e-f25763e70980 · inbound

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MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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Observation 6f64018f-87b3-4fe7-b052-a8839f271943 · inbound

BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View cites this paper.

BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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Observation a7766356-5dfb-4980-916d-0a1e98a30eb4 · inbound

RECALL: Rehearsal-free Continual Learning for Object Classification cites this paper.

RECALL: Rehearsal-free Continual Learning for Object Classification MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 28

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Observation f2ca50cc-183c-4d2e-8534-577e79bed2a6 · inbound

Faster Segment Anything: Towards Lightweight SAM for Mobile Applications cites this paper.

Faster Segment Anything: Towards Lightweight SAM for Mobile Applications MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 13

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Observation f91eb4c9-3957-43d5-880d-aa1b891f9f96 · inbound

VMamba: Visual State Space Model cites this paper.

VMamba: Visual State Space Model MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 28

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Observation c85258d3-820e-4647-9668-77ec07d07193 · inbound

Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive cites this paper.

Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 233

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Observation 1c097429-92c2-43be-863f-a0d44b764e8a · inbound

Deep Privacy Funnel Model: From a Discriminative to a Generative Approach with an Application to Face Recognition cites this paper.

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Reference 88

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Observation 8f47db31-e482-4b72-b96d-20fa1d31e29d · inbound

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge cites this paper.

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Reference 63

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Source-reported events for the cited work

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SleepNet and DreamNet: Enriching and Reconstructing Representations for Consolidated Visual Classification cites this paper.

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Reference 18

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Observation a52e5b17-1fa4-4c43-b85b-255494ec786d · inbound

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Reference 16

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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 ac8b09d4-3a45-4199-94d8-c15d5d52d843 · inbound

Local-Global Attention: An Adaptive Mechanism for Multi-Scale Feature Integration cites this paper.

Local-Global Attention: An Adaptive Mechanism for Multi-Scale Feature Integration MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 11

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OneNet: A Channel-Wise 1D Convolutional U-Net cites this paper.

OneNet: A Channel-Wise 1D Convolutional U-Net MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 12

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Real-Time AI-Driven People Tracking and Counting Using Overhead Cameras cites this paper.

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Reference 11

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BiDense: Binarization for Dense Prediction MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 21

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Structure Tensor Representation for Robust Oriented Object Detection cites this paper.

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Multi-perspective Contrastive Logit Distillation cites this paper.

Multi-perspective Contrastive Logit Distillation MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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Lung Disease Detection with Vision Transformers: A Comparative Study of Machine Learning Methods cites this paper.

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Reference 29

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LightFFDNets: Lightweight Convolutional Neural Networks for Rapid Facial Forgery Detection cites this paper.

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Ultra-Sparse Memory Network MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning cites this paper.

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Deep Feature Response Discriminative Calibration cites this paper.

Deep Feature Response Discriminative Calibration MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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Towards Scalable Insect Monitoring: Ultra-Lightweight CNNs as On-Device Triggers for Insect Camera Traps cites this paper.

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Reference 33

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Noise-Aware Ensemble Learning for Efficient Radar Modulation Recognition cites this paper.

Noise-Aware Ensemble Learning for Efficient Radar Modulation Recognition MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space Duality cites this paper.

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Reference 23

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Comparative Analysis of Resource-Efficient CNN Architectures for Brain Tumor Classification cites this paper.

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LRSAA: Large-scale Remote Sensing Image Target Recognition and Automatic Annotation cites this paper.

LRSAA: Large-scale Remote Sensing Image Target Recognition and Automatic Annotation MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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MobileMamba: Lightweight Multi-Receptive Visual Mamba Network cites this paper.

MobileMamba: Lightweight Multi-Receptive Visual Mamba Network MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 27

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TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-Resolution cites this paper.

TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-Resolution MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 18

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Preserving Deep Representations In One-Shot Pruning: A Hessian-Free Second-Order Optimization Framework cites this paper.

Preserving Deep Representations In One-Shot Pruning: A Hessian-Free Second-Order Optimization Framework MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 29

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Unavailable: canonical work link unavailable.

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Pruning Deep Convolutional Neural Network Using Conditional Mutual Information cites this paper.

Pruning Deep Convolutional Neural Network Using Conditional Mutual Information MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 2017

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DuetML: Human-LLM Collaborative Machine Learning Framework for Non-Expert Users cites this paper.

DuetML: Human-LLM Collaborative Machine Learning Framework for Non-Expert Users MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 52

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Unavailable: canonical work link unavailable.

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LDA-AQU: Adaptive Query-guided Upsampling via Local Deformable Attention cites this paper.

LDA-AQU: Adaptive Query-guided Upsampling via Local Deformable Attention MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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On the Performance Analysis of Momentum Method: A Frequency Domain Perspective cites this paper.

On the Performance Analysis of Momentum Method: A Frequency Domain Perspective MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 12

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A Visual-inertial Localization Algorithm using Opportunistic Visual Beacons and Dead-Reckoning for GNSS-Denied Large-scale Applications cites this paper.

A Visual-inertial Localization Algorithm using Opportunistic Visual Beacons and Dead-Reckoning for GNSS-Denied Large-scale Applications MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 2017

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Source-reported events for the cited work

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Enhancing Skin Cancer Diagnosis (SCD) Using Late Discrete Wavelet Transform (DWT) and New Swarm-Based Optimizers cites this paper.

Enhancing Skin Cancer Diagnosis (SCD) Using Late Discrete Wavelet Transform (DWT) and New Swarm-Based Optimizers MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 36

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d486159-7bd9-4c47-a84c-5c2b691f2c04 · inbound

Visual Modality Prompt for Adapting Vision-Language Object Detectors cites this paper.

Visual Modality Prompt for Adapting Vision-Language Object Detectors MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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Unavailable: canonical work link unavailable.

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AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices cites this paper.

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 30

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Unavailable: canonical work link unavailable.

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Hiding Faces in Plain Sight: Defending DeepFakes by Disrupting Face Detection cites this paper.

Hiding Faces in Plain Sight: Defending DeepFakes by Disrupting Face Detection MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-12T04:45:38.840675Z

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Unavailable: canonical work link unavailable.

Observation b8a7a8e9-9531-43c1-98e8-5b2609317b68 · inbound

Quantum Pointwise Convolution: A Flexible and Scalable Approach for Neural Network Enhancement cites this paper.

Quantum Pointwise Convolution: A Flexible and Scalable Approach for Neural Network Enhancement MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 11

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ILASH: A Predictive Neural Architecture Search Framework for Multi-Task Applications cites this paper.

ILASH: A Predictive Neural Architecture Search Framework for Multi-Task Applications MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 42

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CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution cites this paper.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 13

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OODFace: Benchmarking Robustness of Face Recognition under Common Corruptions and Appearance Variations cites this paper.

OODFace: Benchmarking Robustness of Face Recognition under Common Corruptions and Appearance Variations MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 23

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Lightweight Stochastic Video Prediction via Hybrid Warping cites this paper.

Lightweight Stochastic Video Prediction via Hybrid Warping MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 13

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Ternary Stochastic Neuron -- Implemented with a Single Strained Magnetostrictive Nanomagnet cites this paper.

Ternary Stochastic Neuron -- Implemented with a Single Strained Magnetostrictive Nanomagnet MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 8

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Observation 73e4a6de-346e-4bfb-a830-f5b2a5b66d40 · inbound

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning cites this paper.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 46

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Power Plant Detection for Energy Estimation using GIS with Remote Sensing, CNN & Vision Transformers cites this paper.

Power Plant Detection for Energy Estimation using GIS with Remote Sensing, CNN & Vision Transformers MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T21:07:56.274991Z

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[CLS] Token Tells Everything Needed for Training-free Efficient MLLMs cites this paper.

[CLS] Token Tells Everything Needed for Training-free Efficient MLLMs MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 26

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LVS-Net: A Lightweight Vessels Segmentation Network for Retinal Image Analysis cites this paper.

LVS-Net: A Lightweight Vessels Segmentation Network for Retinal Image Analysis MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 66

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Concerning the Use of Turbulent Flow Data for Machine Learning cites this paper.

Concerning the Use of Turbulent Flow Data for Machine Learning MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 19

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Observation 61c6ce91-55ca-4c2b-b2eb-6419568aba48 · inbound

Enhanced Multi-Object Tracking Using Pose-based Virtual Markers in 3x3 Basketball cites this paper.

Enhanced Multi-Object Tracking Using Pose-based Virtual Markers in 3x3 Basketball MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T19:55:53.000554Z

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A Flexible Template for Edge Generative AI with High-Accuracy Accelerated Softmax & GELU cites this paper.

A Flexible Template for Edge Generative AI with High-Accuracy Accelerated Softmax & GELU MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 14

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EMOv2: Pushing 5M Vision Model Frontier cites this paper.

EMOv2: Pushing 5M Vision Model Frontier MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 8

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Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions cites this paper.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 11

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EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision cites this paper.

EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 51

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A Generative Victim Model for Segmentation cites this paper.

A Generative Victim Model for Segmentation MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 72

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KneeXNeT: An Ensemble-Based Approach for Knee Radiographic Evaluation cites this paper.

KneeXNeT: An Ensemble-Based Approach for Knee Radiographic Evaluation MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T18:47:54.705945Z

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NeRF-NQA: No-Reference Quality Assessment for Scenes Generated by NeRF and Neural View Synthesis Methods cites this paper.

NeRF-NQA: No-Reference Quality Assessment for Scenes Generated by NeRF and Neural View Synthesis Methods MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T18:21:46.149271Z

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Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation cites this paper.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-11T18:13:40.306129Z

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Local Features Meet Stochastic Anonymization: Revolutionizing Privacy-Preserving Face Recognition for Black-Box Models cites this paper.

Local Features Meet Stochastic Anonymization: Revolutionizing Privacy-Preserving Face Recognition for Black-Box Models MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T18:08:12.443114Z

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SnapGen: Taming High-Resolution Text-to-Image Models for Mobile Devices with Efficient Architectures and Training cites this paper.

SnapGen: Taming High-Resolution Text-to-Image Models for Mobile Devices with Efficient Architectures and Training MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T16:56:01.646512Z

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DQA: An Efficient Method for Deep Quantization of Deep Neural Network Activations cites this paper.

DQA: An Efficient Method for Deep Quantization of Deep Neural Network Activations MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T16:55:46.356281Z

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RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone cites this paper.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T15:28:55.451760Z

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Light-T2M: A Lightweight and Fast Model for Text-to-motion Generation cites this paper.

Light-T2M: A Lightweight and Fast Model for Text-to-motion Generation MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T15:16:46.443765Z

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Online Writer Retrieval with Chinese Handwritten Phrases: A Synergistic Temporal-Frequency Representation Learning Approach cites this paper.

Online Writer Retrieval with Chinese Handwritten Phrases: A Synergistic Temporal-Frequency Representation Learning Approach MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T14:46:19.654882Z

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Classification of Spontaneous and Scripted Speech for Multilingual Audio cites this paper.

Classification of Spontaneous and Scripted Speech for Multilingual Audio MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T14:31:49.812169Z

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Observation 49e9c8f5-79c9-4fd0-ad91-1732b2a8982d · inbound

CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices cites this paper.

CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 18

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Observation 8119a866-5f04-4cb1-840e-66c206730ad2 · inbound

Color Enhancement for V-PCC Compressed Point Cloud via 2D Attribute Map Optimization cites this paper.

Color Enhancement for V-PCC Compressed Point Cloud via 2D Attribute Map Optimization MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:42.097284Z

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CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality Detection cites this paper.

CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality Detection MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T12:12:52.379268Z

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Observation 6c36e86d-c948-42dd-86aa-c484c4888d37 · inbound

Till the Layers Collapse: Compressing a Deep Neural Network through the Lenses of Batch Normalization Layers cites this paper.

Till the Layers Collapse: Compressing a Deep Neural Network through the Lenses of Batch Normalization Layers MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T11:44:13.346800Z

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Observation 53f0f577-54cd-434a-a24c-5072de1f8e16 · inbound

PreNeT: Leveraging Computational Features to Predict Deep Neural Network Training Time cites this paper.

PreNeT: Leveraging Computational Features to Predict Deep Neural Network Training Time MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T11:24:37.676576Z

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TopView: Vectorising road users in a bird's eye view from uncalibrated street-level imagery with deep learning cites this paper.

TopView: Vectorising road users in a bird's eye view from uncalibrated street-level imagery with deep learning MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 55

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Observation 70e7b5bc-c542-4b82-ad7b-f577a8324bcc · inbound

Cross-View Consistency Regularisation for Knowledge Distillation cites this paper.

Cross-View Consistency Regularisation for Knowledge Distillation MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 34

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Observation 60804581-a64e-487d-9057-73f53f6e3569 · inbound

Automated Bleeding Detection and Classification in Wireless Capsule Endoscopy with YOLOv8-X cites this paper.

Automated Bleeding Detection and Classification in Wireless Capsule Endoscopy with YOLOv8-X MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T10:25:57.816325Z

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