FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks | Paper Citation Record

Observation 59d01e80-35cf-4b89-adb8-1ae33a72ffa9 · outbound

This paper cites Deep cnn-based visual defect detection: Survey of current literature,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Deep cnn-based visual defect detection: Survey of current literature,

Reference 1

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This paper cites Few-shot learning for structural health diagnosis of civil infrastructure,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Few-shot learning for structural health diagnosis of civil infrastructure,

Reference 2

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This paper cites Defect detection in civil structure using deep learning method,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Defect detection in civil structure using deep learning method,

Reference 3

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Observation 4e1f9e52-d614-437a-9bfd-3a3f45c09109 · outbound

This paper cites Fusion of thermal and rgb images for automated deep learning based crack detection in civil infrastructure,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Fusion of thermal and rgb images for automated deep learning based crack detection in civil infrastructure,

Reference 4

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Observation 366dd4d3-ce34-4cb2-9134-8a48b3a7bd66 · outbound

This paper cites Learning monoc- ular depth estimation for defect measurement from civil rgb-d dataset,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Learning monoc- ular depth estimation for defect measurement from civil rgb-d dataset,

Reference 5

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Observation 7a244167-110f-44ca-a591-1c5b4f9cdc15 · outbound

This paper cites Deep learning-based concrete defects classification and detection using semantic segmentation,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Deep learning-based concrete defects classification and detection using semantic segmentation,

Reference 6

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Observation 37d64711-2e3e-4c65-899d-7fcc37ad584d · outbound

This paper cites Lightweight pixel-level semantic segmentation and analysis for sewer defects using deep learning,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Lightweight pixel-level semantic segmentation and analysis for sewer defects using deep learning,

Reference 7

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Observation 381b0114-e5cd-4a4a-a74c-7df8e8552fc2 · outbound

This paper cites Co-cracksegment: A new collaborative deep learning framework for pixel-level semantic segmentation of concrete cracks,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Co-cracksegment: A new collaborative deep learning framework for pixel-level semantic segmentation of concrete cracks,

Reference 8

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Observation a1626be9-de2d-40e6-a69e-5c1c337643e1 · outbound

This paper cites An automated visual defect segmentation for flat steel surface using deep neural networks,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks An automated visual defect segmentation for flat steel surface using deep neural networks,

Reference 9

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This paper cites Iter- lunet: deep learning architecture for pixel-wise crack detection in levee systems,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Iter- lunet: deep learning architecture for pixel-wise crack detection in levee systems,

Reference 10

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Observation 7520f0cf-8749-46a1-ada4-96feadb10f6c · outbound

This paper cites Addressing class imbalance in micro-ct image segmentation: A modified u-net model with pixel-level class weighting,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Addressing class imbalance in micro-ct image segmentation: A modified u-net model with pixel-level class weighting,

Reference 11

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Observation e17029fb-a85d-4c96-8ee5-5f90ec7120b4 · outbound

This paper cites Kolmogorov-arnold network autoencoders,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Kolmogorov-arnold network autoencoders,

Reference 12

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This paper cites Mof-kan: Kolmogorov- arnold networks for digital discovery of metal-organic frameworks,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Mof-kan: Kolmogorov- arnold networks for digital discovery of metal-organic frameworks,

Reference 13

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Observation f068b8e2-bec8-4fd7-85fa-ed37d2ee47ca · outbound

This paper cites Kanice: Kolmogorov-arnold networks with interactive convolutional elements,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Kanice: Kolmogorov-arnold networks with interactive convolutional elements,

Reference 14

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Observation d1e7f41a-3022-4441-b40c-b895c8dc8068 · outbound

This paper cites Kolmogorov–arnold network for hyperspectral change detection,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Kolmogorov–arnold network for hyperspectral change detection,

Reference 15

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Observation 315c8408-ee3a-43c3-8aa7-e3fec20e2892 · outbound

This paper cites A white-box deep-learning method for electrical energy system modeling based on kolmogorov-arnold network,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks A white-box deep-learning method for electrical energy system modeling based on kolmogorov-arnold network,

Reference 16

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Observation 5b6bfa45-50fa-4f80-9fd9-59d98fbe250a · outbound

This paper cites Kolmogorov-arnold networks in trans- former attention for low-light image enhancement,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Kolmogorov-arnold networks in trans- former attention for low-light image enhancement,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:13.623386Z

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Observation 620b56c6-acb4-40f3-acd5-5745d7aa0972 · outbound

This paper cites Stand-alone composite attention network for concrete structural defect classification,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Stand-alone composite attention network for concrete structural defect classification,

Reference 18

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Observation e6a0cd8e-7c96-409f-8779-823793c006f7 · outbound

This paper cites Vibration-based rf-svm for pc structural defect detection and assessment,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Vibration-based rf-svm for pc structural defect detection and assessment,

Reference 19

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Observation 39e4087e-1d34-4fa5-862d-b3567307434f · outbound

This paper cites Localizing structural damage based on auto-regressive with exogenous input model parameters and residuals using a support vector machine based learning approach,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Localizing structural damage based on auto-regressive with exogenous input model parameters and residuals using a support vector machine based learning approach,

Reference 20

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Observation da507ad8-9e5f-48a4-85d9-4f8526e8a19e · outbound

This paper cites Predictive modeling of structural perfor- mance using machine learning: A comprehensive review,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Predictive modeling of structural perfor- mance using machine learning: A comprehensive review,

Reference 21

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Observation 77b4d29a-a8cd-4c17-9ebf-5e6ede7dd1f1 · outbound

This paper cites Machine learning-assisted improved anomaly detection for structural health monitoring,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Machine learning-assisted improved anomaly detection for structural health monitoring,

Reference 22

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This paper cites Assessing the impact of deep learning on grey urban infrastructure systems: A comprehensive review,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Assessing the impact of deep learning on grey urban infrastructure systems: A comprehensive review,

Reference 23

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This paper cites Fully convolutional networks for semantic segmentation,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Fully convolutional networks for semantic segmentation,

Reference 24

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Observation f0adac89-0bd2-44fa-b7ef-5eae65f20ac3 · outbound

This paper cites Comparison of fully convolutional networks and u-net for optic disc and optic cup segmentation,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Comparison of fully convolutional networks and u-net for optic disc and optic cup segmentation,

Reference 25

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This paper cites Development of semantic segmentation based on deep learn- ing,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Development of semantic segmentation based on deep learn- ing,

Reference 26

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Observation cb8bbe86-de41-4d48-a0f9-d6fa155e0ab7 · outbound

This paper cites Dual Attention U-Net with Feature Infusion: Pushing the Boundaries of Multiclass Defect Segmentation.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Dual Attention U-Net with Feature Infusion: Pushing the Boundaries of Multiclass Defect Segmentation

Reference 27

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Observation 9084026d-da59-4cfc-84e0-eb823eed1081 · outbound

This paper cites Textile defect detection based on multi-proportion spa- tial attention mechanism and channel memory feature fusion network,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Textile defect detection based on multi-proportion spa- tial attention mechanism and channel memory feature fusion network,

Reference 28

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Observation 1572aa08-2cf6-4604-99fc-f17a93396dd4 · outbound

This paper cites Pddd-net: Defect detection network based on parallel attention mechanism and dual-channel spatial pyramid pooling,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Pddd-net: Defect detection network based on parallel attention mechanism and dual-channel spatial pyramid pooling,

Reference 29

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Observation feab0947-7b0a-48fc-85f1-7c09c016515a · outbound

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FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Progressive attention guided recurrent network for salient object detection,

Reference 30

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Observation 00ce24f0-b0b4-4b39-9336-9e4591cb82e5 · outbound

This paper cites Wgs yolo dual: A detection model for strip steel surface defects based on attention mechanism and spatial pyramid pooling structure,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Wgs yolo dual: A detection model for strip steel surface defects based on attention mechanism and spatial pyramid pooling structure,

Reference 31

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Observation 708f3b56-5484-44bd-9bff-4d519753422f · outbound

This paper cites Adaptive dual attention fusion network for rgb-d surface defect detection,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Adaptive dual attention fusion network for rgb-d surface defect detection,

Reference 32

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Observation c4416c5d-2a27-4599-b8aa-98981ad0e142 · outbound

This paper cites KAT to KANs: A Review of Kolmogorov-Arnold Networks and the Neural Leap Forward.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks KAT to KANs: A Review of Kolmogorov-Arnold Networks and the Neural Leap Forward

Reference 33

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FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks A Survey on Kolmogorov-Arnold Network

Reference 34

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This paper cites KAN: Kolmogorov-Arnold Networks.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks KAN: Kolmogorov-Arnold Networks

Reference 35

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Observation 4e24532a-e524-4352-8641-2918b7898770 · outbound

This paper cites Can kan work? exploring the potential of kolmogorov-arnold networks in computer vision,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Can kan work? exploring the potential of kolmogorov-arnold networks in computer vision,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:09.564033Z

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Observation b02a371f-9d96-41f7-b8c8-b4c90a935e2d · outbound

This paper cites Medkaformer: When kolmogorov-arnold theorem meets vision transformer for medical image representation,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Medkaformer: When kolmogorov-arnold theorem meets vision transformer for medical image representation,

Reference 37

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Observation 7166401e-c8d4-4b69-a1d5-c7ae3d2d0bb7 · outbound

This paper cites Multilevel feature fusion and kan integration for brain tumor segmentation,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Multilevel feature fusion and kan integration for brain tumor segmentation,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:09.101291Z

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Observation 81c2f4c5-f248-4d3e-bd87-e2e298476991 · outbound

This paper cites Kolmogorov-arnold networks for metal surface defect classification,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Kolmogorov-arnold networks for metal surface defect classification,

Reference 39

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Observation 102a8351-bdea-4244-ae4c-e0dee96dff06 · outbound

This paper cites Sa- unet: Spatial attention u-net for retinal vessel segmentation,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Sa- unet: Spatial attention u-net for retinal vessel segmentation,

Reference 40

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Observation 6c4dd7b0-8f13-40b7-9e4e-34a5e4b99d2d · outbound

This paper cites Unet segmentation network of covid-19 ct images with multi-scale attention,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Unet segmentation network of covid-19 ct images with multi-scale attention,

Reference 41

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Observation c06b0850-3560-4f55-96c3-685417ab9c55 · outbound

This paper cites GAEI-UNet: Global Attention and Elastic Interaction U-Net for Vessel Image Segmentation.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks GAEI-UNet: Global Attention and Elastic Interaction U-Net for Vessel Image Segmentation

Reference 42

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Observation 28817dfd-5457-4195-bbec-6ec91755326b · outbound

This paper cites U-net: Convolutional net- works for biomedical image segmentation,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks U-net: Convolutional net- works for biomedical image segmentation,

Reference 43

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Observation 8b72934a-70d6-47b7-bfb7-af98b04a542d · outbound

This paper cites Ege-unet: an efficient group enhanced unet for skin lesion segmentation,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Ege-unet: an efficient group enhanced unet for skin lesion segmentation,

Reference 44

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Observation 7a4cd8c8-0218-4a16-b1ef-2cd3f0e14592 · outbound

This paper cites U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation

Reference 45

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Observation de316b5e-1c8c-437f-9d78-1dbee0b32729 · outbound

This paper cites Image-based detection of structural defects using hierarchical multi-scale attention,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Image-based detection of structural defects using hierarchical multi-scale attention,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:07.792264Z

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Observation b45d87ce-92c1-4164-8e60-df0a6bd442bd · outbound

This paper cites Dynamic label injection for imbalanced industrial defect segmentation,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Dynamic label injection for imbalanced industrial defect segmentation,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:07.558959Z

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Observation 1cb11490-7c35-424e-88b9-70241bb57186 · outbound

This paper cites Feature pyramid networks for object detection,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Feature pyramid networks for object detection,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:07.364367Z

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Observation e5f02ba1-623e-4ecc-8a9f-f829c920dedd · outbound

This paper cites Attention u-net: Learning where to look for the pancreas,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Attention u-net: Learning where to look for the pancreas,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:07.097957Z

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Observation f51eec3d-e907-4f98-b083-1fed5d168c95 · outbound

This paper cites Unet++: A nested u-net architecture for medical image segmentation,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Unet++: A nested u-net architecture for medical image segmentation,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:06.814475Z

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Observation 78466819-135a-45de-a549-5f8ff6f5368f · outbound

This paper cites Efficientdet: Scalable and efficient object detection,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Efficientdet: Scalable and efficient object detection,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:06.568093Z

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Observation 6dd7f578-7345-434c-9184-b73d868a04a7 · outbound

This paper cites Unet 3+: A full-scale connected unet for medical im- age segmentation,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Unet 3+: A full-scale connected unet for medical im- age segmentation,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:06.301297Z

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Observation 40996a4e-7f0b-4719-873c-22c36ea2dde4 · outbound

This paper cites Unext: Mlp-based rapid medical image segmentation network,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Unext: Mlp-based rapid medical image segmentation network,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:06.054111Z

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Observation 26060559-e123-4662-abda-661f06da730f · outbound

This paper cites Rolling-unet: Revi- talizing mlp’s ability to efficiently extract long-distance dependencies for medical image segmentation,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Rolling-unet: Revi- talizing mlp’s ability to efficiently extract long-distance dependencies for medical image segmentation,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:05.866192Z

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Observation 908ddefa-b8af-48e4-849f-e04326c2dfb9 · outbound

This paper cites H-vit: A hierarchical vision transformer for deformable image registration,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks H-vit: A hierarchical vision transformer for deformable image registration,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:05.674976Z

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Observation 502d0564-4a39-4790-a899-baaba6e5d19e · outbound

This paper cites Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T16:49:03.924807Z

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Observation 383089f2-871c-49f9-b9bf-aabb5b89addb · outbound

This paper cites MobileUNETR: A Lightweight End-To-End Hybrid Vision Transformer For Efficient Medical Image Segmentation.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks MobileUNETR: A Lightweight End-To-End Hybrid Vision Transformer For Efficient Medical Image Segmentation

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T16:49:04.024976Z

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Observation dcb43250-a231-4a41-8ddc-85212bbb58d9 · outbound

This paper cites Segformer: Simple and efficient design for semantic segmentation with transformers,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Segformer: Simple and efficient design for semantic segmentation with transformers,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:05.479453Z

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Observation 3a407d1c-2c30-447f-affc-cd5d898ce573 · outbound

This paper cites Fastervit: Fast vision transformers with hierarchical attention,.

FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks Fastervit: Fast vision transformers with hierarchical attention,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:49:05.169098Z

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