Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection | Paper Citation Record

Observation 7d860b1d-a7d8-4fc1-b96b-5060628854db · outbound

This paper cites Small- object detection in remote sensing images with end-to-end edge- enhanced gan and object detector network,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Small- object detection in remote sensing images with end-to-end edge- enhanced gan and object detector network,

Reference 1

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Observation df8a8235-0f31-4512-9dcf-d90bda3486fe · outbound

This paper cites Cross-layer attention network for small object detection in remote sensing imagery,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Cross-layer attention network for small object detection in remote sensing imagery,

Reference 2

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Observation e356b4d1-a79d-408b-ad9e-bace7517493e · outbound

This paper cites Small object detection in remote sensing images with residual feature aggregation-based super-resolution and object detector network,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Small object detection in remote sensing images with residual feature aggregation-based super-resolution and object detector network,

Reference 3

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Observation 1e26ec11-e70a-4eb7-94f1-cafe9fbfff85 · outbound

This paper cites Exploring feature compensation and cross-level correlation for infrared small target de- tection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Exploring feature compensation and cross-level correlation for infrared small target de- tection,

Reference 4

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Observation c5cd58d2-4951-4396-bd04-e13e7540f73e · outbound

This paper cites Convolutional neural networks for object detection in aerial imagery for disaster response and recovery,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Convolutional neural networks for object detection in aerial imagery for disaster response and recovery,

Reference 5

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Observation 1eabfd35-2691-4e93-99aa-fb69455591b1 · outbound

This paper cites Self-mimic learning for small-scale pedestrian detection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Self-mimic learning for small-scale pedestrian detection,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:28:28.331552Z

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Observation 701df162-2817-418c-8fa7-38e65a1f3378 · outbound

This paper cites A survey and performance evaluation of deep learning methods for small object detection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection A survey and performance evaluation of deep learning methods for small object detection,

Reference 7

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Observation 6c99f851-8298-4c54-a1cc-24912d19e878 · outbound

This paper cites A survey of the four pillars for small object detection: Multiscale representation, contextual information, super-resolution, and region proposal,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection A survey of the four pillars for small object detection: Multiscale representation, contextual information, super-resolution, and region proposal,

Reference 8

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Observation 46b1a430-40bc-48bf-91a4-b83c50888b6d · outbound

This paper cites Small object detection via pixel level balancing with applications to blood cell detection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Small object detection via pixel level balancing with applications to blood cell detection,

Reference 9

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Observation d149009d-8ce9-4512-bcec-b61622ecbae2 · outbound

This paper cites Imaging based cervical cancer diagnostics using small object detection-generative adversarial networks,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Imaging based cervical cancer diagnostics using small object detection-generative adversarial networks,

Reference 10

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Observation 242a0ece-6b38-46cd-b7c6-b5244e80fdb9 · outbound

This paper cites an unresolved cited work.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Unresolved cited work

Reference 11

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Observation d0569d55-1387-41a9-9321-f30900cb8ed1 · outbound

This paper cites Feature pyramid networks for object detection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Feature pyramid networks for object detection,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:28:28.172275Z

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Observation b33ea280-969a-4045-ab65-09d08947aca4 · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Faster r-cnn: Towards real-time object detection with region proposal networks,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T05:28:25.007362Z

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Observation 5dbb83cb-6ba2-45db-891c-835fb0f16bde · outbound

This paper cites Focal loss for dense object detection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Focal loss for dense object detection,

Reference 14

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Observation d50fa3c1-d2c9-48e5-90a6-c952e0331868 · outbound

This paper cites Fcos: Fully convolutional one- stage object detection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Fcos: Fully convolutional one- stage object detection,

Reference 15

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Observation 405ec0a6-acc4-4531-8aa3-67751aa09162 · outbound

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

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection End-to-end object detection with transformers,

Reference 16

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Observation 8662d341-8444-4aca-9746-b3000d0ee1f3 · outbound

This paper cites Iou loss for 2d/3d object detection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Iou loss for 2d/3d object detection,

Reference 17

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Observation c7a4d824-3c2d-42f6-a949-54aba3ba9700 · outbound

This paper cites A jaccard base similarity measure to improve performance of cf based recom- mender systems,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection A jaccard base similarity measure to improve performance of cf based recom- mender systems,

Reference 18

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Observation e927a0ac-188e-4a02-82df-404e2da55b4a · outbound

This paper cites Rethinking classification and localization for object detection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Rethinking classification and localization for object detection,

Reference 19

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Observation efbe7175-a6f4-40c4-b1ff-089c0224e2e4 · outbound

This paper cites Towards large-scale small object detection: Survey and benchmarks,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Towards large-scale small object detection: Survey and benchmarks,

Reference 20

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Observation c0a8925a-5698-4b7b-b2a0-ecbdb3f0cba2 · outbound

This paper cites Tiny object detection in aerial images,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Tiny object detection in aerial images,

Reference 21

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Observation 1ebe02d1-c593-40a6-86db-1bd88a637acc · outbound

This paper cites Microsoft coco: Common objects in context,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Microsoft coco: Common objects in context,

Reference 22

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Observation 70cb5e29-d10d-4b69-a232-5afcb5d90c88 · outbound

This paper cites Dynamic head: Unifying object detection heads with attentions,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Dynamic head: Unifying object detection heads with attentions,

Reference 23

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Observation e2ef3d4f-c343-4f66-ada7-6fff2a8cc80a · outbound

This paper cites Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection,

Reference 24

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Observation 8939e27e-db4c-4295-8dae-d2260c75b683 · outbound

This paper cites Cornernet: Detecting objects as paired keypoints,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Cornernet: Detecting objects as paired keypoints,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T05:28:25.389002Z

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Observation 4fc2b7b4-88f0-4b64-91ac-12859883d833 · outbound

This paper cites Objects as Points.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Objects as Points

Reference 26

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Observation 92c7784f-ea85-443a-b424-69effe4dd774 · outbound

This paper cites Reppoints: Point set representation for object detection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Reppoints: Point set representation for object detection,

Reference 27

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Observation 97276a2f-e7bc-47ec-acd1-ae8fd98482ce · outbound

This paper cites Deformable DETR: Deformable Transformers for End-to-End Object Detection.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Deformable DETR: Deformable Transformers for End-to-End Object Detection

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T05:28:25.402917Z

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Observation 286a8507-0228-414b-9fe4-40b023e1faa5 · outbound

This paper cites Augmentation for small object detection.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Augmentation for small object detection

Reference 29

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Observation cffe7205-0927-4fa0-8671-630d4a1e4f63 · outbound

This paper cites Learning data augmentation strategies for object detection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Learning data augmentation strategies for object detection,

Reference 30

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Observation 37dfc9b7-e636-487e-945c-3eb9fcddad92 · outbound

This paper cites Scale match for tiny person detection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Scale match for tiny person detection,

Reference 31

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Observation 4a3ebeef-5c38-4db1-8912-b4ee5670d644 · outbound

This paper cites Sod-mtgan: Small object detection via multi-task generative adversarial network,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Sod-mtgan: Small object detection via multi-task generative adversarial network,

Reference 32

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Observation fd4b354b-0a61-4d0c-b2fb-437725b0fa67 · outbound

This paper cites Better to follow, follow to be better: Towards precise supervision of feature super-resolution for small object detection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Better to follow, follow to be better: Towards precise supervision of feature super-resolution for small object detection,

Reference 33

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Observation cf4b5168-cfeb-4065-a8d0-762e6899d3be · outbound

This paper cites Effective fusion factor in fpn for tiny object detection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Effective fusion factor in fpn for tiny object detection,

Reference 34

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Observation 039bdd82-3256-4895-9fda-ce8e6fb0fbcb · outbound

This paper cites Sspnet: Scale selection pyramid network for tiny person detection from uav images,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Sspnet: Scale selection pyramid network for tiny person detection from uav images,

Reference 35

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Observation 843077a8-ca22-494a-b1c0-dafebc8341db · outbound

This paper cites Rethinking rotated object detection with gaussian wasserstein distance loss,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Rethinking rotated object detection with gaussian wasserstein distance loss,

Reference 36

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Observation 52e5065f-f861-4cb0-8188-5dd7be75f9c5 · outbound

This paper cites A Normalized Gaussian Wasserstein Distance for Tiny Object Detection.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection A Normalized Gaussian Wasserstein Distance for Tiny Object Detection

Reference 37

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Observation 24a1336a-d0a6-47e4-8dc4-a90a91a25bc2 · outbound

This paper cites Rfla: Gaussian receptive field based label assignment for tiny object detection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Rfla: Gaussian receptive field based label assignment for tiny object detection,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-16T05:28:25.622777Z

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Observation 5a09765a-b72b-4d8f-b00f-2fc1b978425f · outbound

This paper cites Dynamic coarse-to-fine learning for oriented tiny object detection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Dynamic coarse-to-fine learning for oriented tiny object detection,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:28:27.373749Z

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Observation 1c0e60cb-62c0-4e43-b569-0d70fa02d5a9 · outbound

This paper cites Small object detection via coarse-to-fine proposal generation and imitation learning,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Small object detection via coarse-to-fine proposal generation and imitation learning,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:28:27.358635Z

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Observation 5d908967-dfab-4bc4-b84a-4af5e0e109f0 · outbound

This paper cites Feature selective anchor-free module for single-shot object detection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Feature selective anchor-free module for single-shot object detection,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:28:27.276738Z

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Observation c3319451-15c6-4936-a49a-8e9251c2fca7 · outbound

This paper cites Ota: Optimal transport assignment for object detection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Ota: Optimal transport assignment for object detection,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:28:27.191385Z

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Observation 50e53125-4176-45c7-83a7-8b5bde010773 · outbound

This paper cites Freeanchor: Learning to match anchors for visual object detection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Freeanchor: Learning to match anchors for visual object detection,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:28:27.177260Z

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Observation 00776557-94a0-408a-b29e-be7cf1a97d5d · outbound

This paper cites High-frequency component helps explain the generalization of convolutional neural networks,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection High-frequency component helps explain the generalization of convolutional neural networks,

Reference 44

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Observation 343dcf9e-5f8a-48b2-a164-2893a2ee5f02 · outbound

This paper cites Learning in the frequency domain,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Learning in the frequency domain,

Reference 45

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Observation e2981989-62d2-4c1e-9099-8b001595bc2f · outbound

This paper cites Invertible image rescaling,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Invertible image rescaling,

Reference 46

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Observation f19b0ba4-695e-4b4c-b1c0-4cc36a3adbcc · outbound

This paper cites Detect- ing camouflaged object in frequency domain,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Detect- ing camouflaged object in frequency domain,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:28:27.056808Z

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Observation 2a3a8a74-a264-46b6-9d7b-594c7ea47554 · outbound

This paper cites Deep residual learning for image recognition,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Deep residual learning for image recognition,

Reference 48

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Observation f1796168-8e80-46cb-9b2b-af5f5ade06c2 · outbound

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

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Aggregated residual transformations for deep neural networks,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-16T05:28:25.893468Z

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Observation b68453e8-910e-4bfa-94a2-1eacc068427a · outbound

This paper cites Sparse r-cnn: End-to-end object detection with learnable proposals,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Sparse r-cnn: End-to-end object detection with learnable proposals,

Reference 50

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Observation 8d45fd15-da5f-4b52-8181-906fc04ae0e4 · outbound

This paper cites Cascade rpn: Delving into high-quality region proposal network with adaptive convolution,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Cascade rpn: Delving into high-quality region proposal network with adaptive convolution,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:28:26.993222Z

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Observation 0365f0e8-099f-43e5-893c-e446bdac6386 · outbound

This paper cites YOLOX: Exceeding YOLO Series in 2021.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection YOLOX: Exceeding YOLO Series in 2021

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-16T05:28:25.909368Z

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Observation af838b3f-614e-471f-b4f7-63508b0305f1 · outbound

This paper cites AutoAssign: Differentiable Label Assignment for Dense Object Detection.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection AutoAssign: Differentiable Label Assignment for Dense Object Detection

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-16T05:28:25.913690Z

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Observation 53c7fcac-cce2-456d-b441-79dcc5ca890a · outbound

This paper cites Cascade r-cnn: Delving into high quality object detection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Cascade r-cnn: Delving into high quality object detection,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-16T05:28:25.918265Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb7a9d38-7af7-48b4-a8f0-d88a240f534c · outbound

This paper cites Dot distance for tiny object detection in aerial images,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Dot distance for tiny object detection in aerial images,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:28:26.969385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f0ef8d29-5e29-41f3-a132-8d39b9c2cd2a · outbound

This paper cites DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-16T05:28:26.045486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5e86919-0b58-412e-bf4c-588da56ef7e1 · outbound

This paper cites Detectors: Detecting objects with recursive feature pyramid and switchable atrous convolution,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Detectors: Detecting objects with recursive feature pyramid and switchable atrous convolution,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:28:26.763290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ca649474-22bc-4ecc-b0c3-1f0741e04ace · outbound

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

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection MMDetection: Open MMLab Detection Toolbox and Benchmark

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-16T05:28:26.054533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04d5f51f-4270-4ba1-a8ad-f0a93874a142 · outbound

This paper cites Mask r-cnn,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Mask r-cnn,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:28:26.701403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b11065c4-01ef-4c5d-a1b9-705d5b3ee4a7 · outbound

This paper cites Libra r-cnn: Towards balanced learning for object detection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Libra r-cnn: Towards balanced learning for object detection,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:28:26.680336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 19e3359c-6fb8-43dc-a48b-21a003118cc3 · outbound

This paper cites Region proposal by guided anchoring,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Region proposal by guided anchoring,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:28:26.666289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 29bb62a1-fc44-426b-b9e7-cd0ccbb49c47 · outbound

This paper cites R3det: Refined single-stage detector with feature refinement for rotating object,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection R3det: Refined single-stage detector with feature refinement for rotating object,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:28:26.612516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 174dc060-e3ee-4f67-93b4-0e6746fa524e · outbound

This paper cites Align deep features for oriented object detection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Align deep features for oriented object detection,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-16T05:28:26.157806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8163e992-73f2-4257-a784-9795ca6c4eb8 · outbound

This paper cites Redet: A rotation-equivariant detector for aerial object detection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Redet: A rotation-equivariant detector for aerial object detection,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:28:26.469500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 17065fd3-2247-4c59-a6ef-d9fd1fb4ced4 · outbound

This paper cites Gliding vertex on the horizontal bounding box for multi-oriented object detection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Gliding vertex on the horizontal bounding box for multi-oriented object detection,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:28:26.454360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3d07892a-c054-4a11-993d-36792c2ca296 · outbound

This paper cites Oriented r-cnn for object detection,.

Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection Oriented r-cnn for object detection,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:28:26.439416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers