RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations | Paper Citation Record

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This paper cites https : / / github.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations https : / / github

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This paper cites Layer Normalization.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Layer Normalization

Reference 2

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This paper cites End-to- end object detection with transformers.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations End-to- end object detection with transformers

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This paper cites PeLK: Parameter-efficient Large Kernel ConvNets with Peripheral Convolution.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations PeLK: Parameter-efficient Large Kernel ConvNets with Peripheral Convolution

Reference 4

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This paper cites Run, don’t walk: Chasing higher flops for faster neural networks.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Run, don’t walk: Chasing higher flops for faster neural networks

Reference 5

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This paper cites Drop an octave: Reducing spatial redundancy in con- volutional neural networks with octave convolution.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Drop an octave: Reducing spatial redundancy in con- volutional neural networks with octave convolution

Reference 6

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This paper cites Mobile- former: Bridging mobilenet and transformer.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Mobile- former: Bridging mobilenet and transformer

Reference 7

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This paper cites Largekernel3d: Scaling up kernels in 3d sparse cnns.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Largekernel3d: Scaling up kernels in 3d sparse cnns

Reference 8

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

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Imagenet: A large-scale hierarchical image database

Reference 9

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This paper cites Scaling up your kernels to 31x31: Revisiting large kernel design in cnns.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Scaling up your kernels to 31x31: Revisiting large kernel design in cnns

Reference 10

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This paper cites UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image Recognition.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image Recognition

Reference 11

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This paper cites ModernTCN: A modern pure convolution structure for general time series analysis.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations ModernTCN: A modern pure convolution structure for general time series analysis

Reference 12

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

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 13

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Observation ce7d8ff2-55f8-43b6-8bdb-2382eaf8c559 · outbound

This paper cites Torchcam: class activation explorer.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Torchcam: class activation explorer

Reference 14

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This paper cites Wavelet convolutions for large receptive fields.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Wavelet convolutions for large receptive fields

Reference 15

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Observation a0eb568b-f21b-42da-a44c-2b4d3ccb7e4a · outbound

This paper cites Partial success in closing the gap between human and machine vision.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Partial success in closing the gap between human and machine vision

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Observation 8f3e90b4-407e-49d4-bf50-ed753123d74b · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Mamba: Linear-Time Sequence Modeling with Selective State Spaces

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This paper cites Efficiently mod- eling long sequences with structured state spaces.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Efficiently mod- eling long sequences with structured state spaces

Reference 18

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This paper cites SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

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This paper cites Flatten transformer: Vision transformer using focused linear attention.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Flatten transformer: Vision transformer using focused linear attention

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Observation 151ec777-3f45-424a-a3a1-3c847026f1d9 · outbound

This paper cites Demystify mamba in vision: A linear attention perspective.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Demystify mamba in vision: A linear attention perspective

Reference 21

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Observation edc39ce0-e17a-4ce5-8e3c-2bb2c39e91e5 · outbound

This paper cites Ghostnet: More features from cheap opera- tions.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Ghostnet: More features from cheap opera- tions

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

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations MobileMamba: Lightweight Multi-Receptive Visual Mamba Network

Reference 23

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Observation dac17103-a628-4311-8f5a-6a6a1de10fa5 · outbound

This paper cites Deep residual learning for image recognition.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Deep residual learning for image recognition

Reference 24

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Observation 6b4c8831-483f-4735-861d-db137717e4fe · outbound

This paper cites Mask r-cnn.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Mask r-cnn

Reference 25

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This paper cites Gaussian Error Linear Units (GELUs).

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Gaussian Error Linear Units (GELUs)

Reference 26

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This paper cites Searching for mo- bilenetv3.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Searching for mo- bilenetv3

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Observation 710dc1a1-baf4-4c0d-a631-05ba7f7e9261 · outbound

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

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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This paper cites LightViT: Towards Light-Weight Convolution-Free Vision Transformers.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations LightViT: Towards Light-Weight Convolution-Free Vision Transformers

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This paper cites Are large kernels better teachers than transformers for convnets?,.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Are large kernels better teachers than transformers for convnets?,

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This paper cites Batch normalization: Accelerating deep network training by reducing internal co- variate shift.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Batch normalization: Accelerating deep network training by reducing internal co- variate shift

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This paper cites Wavemix: A resource-efficient neural network for im- age analysis, 2023.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Wavemix: A resource-efficient neural network for im- age analysis, 2023

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This paper cites Transformers are rnns: Fast autoregressive transformers with linear attention.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Transformers are rnns: Fast autoregressive transformers with linear attention

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This paper cites Panoptic feature pyramid networks.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Panoptic feature pyramid networks

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Observation 35138178-1e73-4b43-b4c0-5aed6f3595d9 · outbound

This paper cites Imagenet classification with deep convolutional neural net- works.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Imagenet classification with deep convolutional neural net- works

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This paper cites FractalNet: Ultra-Deep Neural Networks without Residuals.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations FractalNet: Ultra-Deep Neural Networks without Residuals

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This paper cites Backpropagation applied to handwrit- ten zip code recognition.Neural computation, 1(4):541–551,.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Backpropagation applied to handwrit- ten zip code recognition.Neural computation, 1(4):541–551,

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This paper cites Visualizing the loss landscape of neural nets.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Visualizing the loss landscape of neural nets

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RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Unresolved cited work

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This paper cites Selec- tive kernel networks.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Selec- tive kernel networks

Reference 40

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This paper cites Efficientformer: Vision transformers at mobilenet speed.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Efficientformer: Vision transformers at mobilenet speed

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Observation 65c9635d-b5ff-4e87-b5ce-5f879a10276a · outbound

This paper cites Large selective kernel network for remote sensing object detection.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Large selective kernel network for remote sensing object detection

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This paper cites Re- thinking vision transformers for mobilenet size and speed.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Re- thinking vision transformers for mobilenet size and speed

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Observation 7cb63205-f611-4a48-8744-dd93b7860c8f · outbound

This paper cites Microsoft coco: Common objects in context.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Microsoft coco: Common objects in context

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This paper cites More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity

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

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Swin transformer: Hierarchical vision transformer using shifted windows

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:15:12.895412Z

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This paper cites A convnet for the 2020s.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations A convnet for the 2020s

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This paper cites Rewrite the stars.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Rewrite the stars

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This paper cites Efficient modulation for vision net- works.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Efficient modulation for vision net- works

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This paper cites Edgenext: Efficiently amalgamated cnn-transformer architecture for mobile vision applications.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Edgenext: Efficiently amalgamated cnn-transformer architecture for mobile vision applications

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

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer

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Observation 8d7d4c3d-022c-4020-a784-e360d785b23e · outbound

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

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Separable Self-attention for Mobile Vision Transformers

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This paper cites Mo- bilevig: Graph-based sparse attention for mobile vision ap- plications.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Mo- bilevig: Graph-based sparse attention for mobile vision ap- plications

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Observation 2c27b4c7-8872-4b9f-ae38-6d4f32d67122 · outbound

This paper cites Edgevits: Competing light-weight cnns on mobile devices with vision transformers.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Edgevits: Competing light-weight cnns on mobile devices with vision transformers

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Observation 6a9900b4-dbd4-49a8-bedc-34d37cba32da · outbound

This paper cites Large kernel matters–improve semantic segmen- tation by global convolutional network.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Large kernel matters–improve semantic segmen- tation by global convolutional network

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:15:12.794355Z

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Observation 25fe3df9-affe-443c-8d08-d75d97eb5ae1 · outbound

This paper cites Designing network design spaces.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Designing network design spaces

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:15:12.778865Z

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Observation c7d80500-5e3e-4893-81c0-9a76347f235e · outbound

This paper cites Global filter networks for image classification.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Global filter networks for image classification

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:15:12.763090Z

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Observation 4d067c96-3c56-4311-b704-ebd68fb68f58 · outbound

This paper cites Hornet: Efficient high-order spatial interactions with recursive gated convolutions.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Hornet: Efficient high-order spatial interactions with recursive gated convolutions

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:15:12.747962Z

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Observation 0476806e-6cc0-4199-a69d-801af68c21e4 · outbound

This paper cites You only look once: Unified, real-time object de- tection.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations You only look once: Unified, real-time object de- tection

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:15:12.731351Z

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Observation a76b06ce-6f4b-4bb5-b7fc-adbd38c7904f · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 60

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Observation 42344248-27a0-441b-afe5-b2c17bd13f5a · outbound

This paper cites Schuster and K.K.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Schuster and K.K

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:15:12.706031Z

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Observation e5112bca-b6af-4b39-82ff-0898b2fe241d · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:15:12.690257Z

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Observation 9cfee071-18bb-4d77-aca9-7e60f32ca734 · outbound

This paper cites Swiftformer: Efficient additive attention for transformer- based real-time mobile vision applications.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Swiftformer: Efficient additive attention for transformer- based real-time mobile vision applications

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:15:12.674786Z

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Observation 2f61033e-f9fd-49a0-9674-1bdf34b78f00 · outbound

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

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 64

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Observation 17c3f433-1db5-4bba-bffd-60b57d234fe0 · outbound

This paper cites Going deeper with convolutions.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Going deeper with convolutions

Reference 65

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Observation b8ce0958-8202-4c4e-9654-149f4951c45b · outbound

This paper cites Rethinking the inception archi- tecture for computer vision.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Rethinking the inception archi- tecture for computer vision

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:15:12.648876Z

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Observation 6cd04796-16f9-434c-a22e-715d351be4ae · outbound

This paper cites Inception-v4, inception-resnet and the impact of residual connections on learning.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Inception-v4, inception-resnet and the impact of residual connections on learning

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:15:12.633180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 47b30a16-f7b2-4eff-98d9-aa3ef3ae0402 · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-11T00:15:11.848531Z

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Observation 5df25abc-e910-4ff9-b3ec-c03fe9493019 · outbound

This paper cites Mnas- net: Platform-aware neural architecture search for mobile.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Mnas- net: Platform-aware neural architecture search for mobile

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:15:12.606396Z

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Observation 03a491d0-fdb0-4e89-9e8f-aa29dff04439 · outbound

This paper cites MLP-Mixer: An all-MLP Architecture for Vision.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations MLP-Mixer: An all-MLP Architecture for Vision

Reference 70

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This paper cites Patches Are All You Need?.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Patches Are All You Need?

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-11T00:15:11.862905Z

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Observation d19c5602-797d-4264-8f35-b8faf5f7a887 · outbound

This paper cites FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-11T00:15:11.867792Z

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Observation 755afaf6-c7eb-41fe-ac5b-0646e06db64a · outbound

This paper cites Mobileone: An im- proved one millisecond mobile backbone.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Mobileone: An im- proved one millisecond mobile backbone

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:15:12.590863Z

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Observation 1fa697ed-e05a-422e-bc2b-569aea321fd0 · outbound

This paper cites Attention is all you need.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Attention is all you need

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-11T00:15:11.877285Z

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Observation 9df00cef-8095-4934-ad78-3b27f3a4fdfe · outbound

This paper cites MobileViTv3: Mobile-Friendly Vision Transformer with Simple and Effective Fusion of Local, Global and Input Features.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations MobileViTv3: Mobile-Friendly Vision Transformer with Simple and Effective Fusion of Local, Global and Input Features

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-11T00:15:11.882090Z

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Observation 23762d59-4fb9-4707-ae6c-844b283a7a1e · outbound

This paper cites RepViT: Revisiting Mobile CNN From ViT Perspective.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations RepViT: Revisiting Mobile CNN From ViT Perspective

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-11T00:15:11.886964Z

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Observation 8a513cde-00b2-4c47-b4a2-631ae80900bf · outbound

This paper cites YOLOv10: Real-Time End-to-End Object Detection.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations YOLOv10: Real-Time End-to-End Object Detection

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-11T00:15:11.891896Z

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Observation 76e7dad2-ef35-4bf5-a054-f24714832b4e · outbound

This paper cites Lsnet: See large, focus small, 2025.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Lsnet: See large, focus small, 2025

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:15:12.565605Z

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Observation 51e9cb59-e9f2-45c9-8e66-d80de41c57ff · outbound

This paper cites Pyramid vision transformer: A versatile backbone for dense prediction without convolutions.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Pyramid vision transformer: A versatile backbone for dense prediction without convolutions

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:15:12.550142Z

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Observation 5e99c2e0-84fb-4c81-b476-45a8312c7aa0 · outbound

This paper cites Early convolutions help trans- formers see better.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Early convolutions help trans- formers see better

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:15:12.533500Z

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Observation ab14644c-78b5-4cbc-a1a3-4fc9b2e41aa6 · outbound

This paper cites Metaformer is actually what you need for vision.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Metaformer is actually what you need for vision

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:15:12.518336Z

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Observation dfbc776a-2522-4928-bc5e-497c293dffe7 · outbound

This paper cites InceptionNeXt: When Inception Meets ConvNeXt.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations InceptionNeXt: When Inception Meets ConvNeXt

Reference 82

Resolution: verified exact; local_arxiv, observed 2026-08-11T00:15:12.033104Z

Source-reported events for the cited work

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Observation 77e0cecc-fa2f-4033-8daf-ec13db1e4716 · outbound

This paper cites SHViT: Single-Head Vision Transformer with Memory Efficient Macro Design.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations SHViT: Single-Head Vision Transformer with Memory Efficient Macro Design

Reference 83

Resolution: verified exact; local_arxiv, observed 2026-08-11T00:15:12.009456Z

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Observation 457d03ea-290a-4acb-9832-218f0fb9074c · outbound

This paper cites Parc-net: Po- sition aware circular convolution with merits from convnets and transformer.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Parc-net: Po- sition aware circular convolution with merits from convnets and transformer

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:15:12.503594Z

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Observation dfe53f0b-ed49-438f-b8f6-7995d8efdb59 · outbound

This paper cites Repnext: A fast multi-scale cnn using structural reparameterization, 2024.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Repnext: A fast multi-scale cnn using structural reparameterization, 2024

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:15:12.488574Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1da1cfa2-efad-4d76-8b2c-52f307630258 · outbound

This paper cites Torr, and Li Zhang.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Torr, and Li Zhang

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:15:12.473694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c1208d0d-5c1e-45c5-a36a-604ee3b83e4a · outbound

This paper cites Scene parsing through ade20k dataset.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations Scene parsing through ade20k dataset

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-11T00:15:11.943886Z

Source-reported events for the cited work

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Observation e9b2eac1-f3eb-4ce4-b4cd-70c1edf5de31 · outbound

This paper cites this approach treats the sequence of downsampled feature maps from each decomposition level as the input to a recurrent model.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations this approach treats the sequence of downsampled feature maps from each decomposition level as the input to a recurrent model

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:15:12.432987Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8c516be3-e840-4ef9-a30d-1068e89a4f69 · outbound

This paper cites T”, “S”, “B.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations T”, “S”, “B

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:15:12.417612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e915fa30-5397-4f1f-abb6-781d8c8a1232 · outbound

This paper cites M” and “A.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations M” and “A

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:15:12.402130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3a00d546-6149-41cb-a8fb-efb68bc57456 · outbound

This paper cites h = None for i, o in reversed(zip(fs[1:], fs[:-1])): h = self.a(h) + self.b(i) if h else self.b(i) h = interpolate(h, size=o.shape[2:]) return self.c(h) + self.d(x).

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations h = None for i, o in reversed(zip(fs[1:], fs[:-1])): h = self.a(h) + self.b(i) if h else self.b(i) h = interpolate(h, size=o.shape[2:]) return self.c(h) + self.d(x)

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:15:12.449235Z

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