Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T12:35:52.341950Z
Paper Citation Record · LEDGER
Dynamic Graph Message Passing Networks
As of 16 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 0 inbound Pith citation observations for arXiv:1908.06955.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
63 of 63 outbound references displayed
- verified exact0
- verified fuzzy56
- unresolved7
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 21f0af4c-5a98-4fc4-8474-8f425acba16b · outbound
This paper cites Higher order conditional random fields in deep neural networks.
Dynamic Graph Message Passing Networks Higher order conditional random fields in deep neural networks
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:53.372937Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 626f1b15-9f18-432c-84a8-933b57be8e83 · outbound
This paper cites Conditional random fields meet deep neural networks for semantic segmentation: Combining probabilistic graphical models with deep learning for structured prediction.
Dynamic Graph Message Passing Networks Conditional random fields meet deep neural networks for semantic segmentation: Combining probabilistic graphical models with deep learning for structured prediction
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:53.359900Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 2b9dc712-1184-4df2-86a6-a201ca13ccc1 · outbound
This paper cites Geodesic matting: A frame- work for fast interactive image and video segmentation and matting.
Dynamic Graph Message Passing Networks Geodesic matting: A frame- work for fast interactive image and video segmentation and matting
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:53.346591Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d673bee7-7ade-497d-b2fe-160e24897600 · outbound
This paper cites Gcnet: Non-local networks meet squeeze-excitation networks and beyond.
Dynamic Graph Message Passing Networks Gcnet: Non-local networks meet squeeze-excitation networks and beyond
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:53.334141Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d35d649d-7b71-464f-bc8b-2bd4a545e5cf · outbound
This paper cites an unresolved cited work.
Dynamic Graph Message Passing Networks Unresolved cited work
Reference 5
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T12:35:53.321856Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 7430f2a8-082b-4f67-871e-130eec3d74c4 · outbound
This paper cites Rethinking atrous convolution for semantic image segmentation.
Dynamic Graph Message Passing Networks Rethinking atrous convolution for semantic image segmentation
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:53.308150Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 49f14da5-1048-4a2a-a03d-4a96511b45e8 · outbound
This paper cites Encoder-decoder with atrous separable convolution for semantic image segmentation.
Dynamic Graph Message Passing Networks Encoder-decoder with atrous separable convolution for semantic image segmentation
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:53.292978Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 426c6399-2661-4ae4-b3d8-458d8f8a2af8 · outbound
This paper cites Graph-based global reasoning networks.
Dynamic Graph Message Passing Networks Graph-based global reasoning networks
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:53.276919Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation a09bfd07-0618-4e69-8095-175137e60203 · outbound
This paper cites Xception: Deep learning with depthwise separable convolutions.
Dynamic Graph Message Passing Networks Xception: Deep learning with depthwise separable convolutions
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:53.264689Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 4c050332-fdef-4bad-be63-ca5413816b3d · outbound
This paper cites The cityscapes dataset for semantic urban scene understanding.
Dynamic Graph Message Passing Networks The cityscapes dataset for semantic urban scene understanding
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:53.251037Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 565c79d6-2ec8-4b15-afa5-b8c1b96e41f9 · outbound
This paper cites Boxsup: Exploit- ing bounding boxes to supervise convolutional networks for semantic segmentation.
Dynamic Graph Message Passing Networks Boxsup: Exploit- ing bounding boxes to supervise convolutional networks for semantic segmentation
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:53.238126Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 40b6a3e4-1a79-47a9-a80e-595319d69e74 · outbound
This paper cites R-fcn: Object detection via region-based fully convolutional networks.
Dynamic Graph Message Passing Networks R-fcn: Object detection via region-based fully convolutional networks
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:53.222365Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 387f870c-a833-416a-8f64-7da599276977 · outbound
This paper cites Deformable convolutional networks.
Dynamic Graph Message Passing Networks Deformable convolutional networks
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:53.208543Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation df435838-e3a6-4a23-a421-2a2d7db5e087 · outbound
This paper cites Dssd: Deconvolutional single shot detector.
Dynamic Graph Message Passing Networks Dssd: Deconvolutional single shot detector
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:53.193704Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 30ec0082-cbdc-4cc3-898b-18af44261a98 · outbound
This paper cites Dual attention network for scene segmentation.
Dynamic Graph Message Passing Networks Dual attention network for scene segmentation
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:53.178877Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 1964a974-f077-4be3-b7b1-52a123ab95be · outbound
This paper cites Neural message passing for quantum chemistry.
Dynamic Graph Message Passing Networks Neural message passing for quantum chemistry
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:53.163802Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 08769c02-b43e-40c9-9673-2d28a49655b2 · outbound
This paper cites Accurate, large minibatch sgd: Training imagenet in 1 hour.
Dynamic Graph Message Passing Networks Accurate, large minibatch sgd: Training imagenet in 1 hour
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:53.147901Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation a91a89f8-2a15-4c30-be0b-5ac52847304d · outbound
This paper cites Inductive representation learning on large graphs.
Dynamic Graph Message Passing Networks Inductive representation learning on large graphs
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:53.124146Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 98b27014-7130-4ec7-a70b-865ac4b93635 · outbound
This paper cites Mask r-cnn.
Dynamic Graph Message Passing Networks Mask r-cnn
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:53.094970Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 31164432-89a6-4227-a3db-360144a728bd · outbound
This paper cites Deep residual learning for image recognition.
Dynamic Graph Message Passing Networks Deep residual learning for image recognition
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:53.076137Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation b16c5b38-f71a-497f-a4e1-520b7e184603 · outbound
This paper cites Strip pooling: Rethinking spatial pooling for scene parsing.
Dynamic Graph Message Passing Networks Strip pooling: Rethinking spatial pooling for scene parsing
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:53.060561Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f5e78bbe-9fab-4c8a-a3ba-8f8bea1e9dce · outbound
This paper cites Ccnet: Criss-cross attention for semantic segmentation.
Dynamic Graph Message Passing Networks Ccnet: Criss-cross attention for semantic segmentation
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:53.044442Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 36ed41fe-c8fd-4804-b575-bf3d90ba86ba · outbound
This paper cites Spatial transformer networks.
Dynamic Graph Message Passing Networks Spatial transformer networks
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:53.028880Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 52c3478c-f92c-4f5f-b2b3-de466184411b · outbound
This paper cites Dynamic filter networks.
Dynamic Graph Message Passing Networks Dynamic filter networks
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T12:35:52.111849Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8b0f0574-1d99-48be-a649-d91a3482a33d · outbound
This paper cites Semi-supervised classifi- cation with graph convolutional networks.
Dynamic Graph Message Passing Networks Semi-supervised classifi- cation with graph convolutional networks
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.991747Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 6b5c4224-9c59-420e-835b-56fbe6797bfd · outbound
This paper cites an unresolved cited work.
Dynamic Graph Message Passing Networks Unresolved cited work
Reference 26
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T12:35:52.978754Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 19d6ab59-707f-4806-98bd-0b8a9bb352ba · outbound
This paper cites Efficient inference in fully connected crfs with gaussian edge potentials.
Dynamic Graph Message Passing Networks Efficient inference in fully connected crfs with gaussian edge potentials
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.963203Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation fb6641b4-1051-4e1f-9a39-016a89d6ecb2 · outbound
This paper cites Cornernet: Detecting objects as paired keypoints.
Dynamic Graph Message Passing Networks Cornernet: Detecting objects as paired keypoints
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.948239Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation c9fddb6b-6055-4cfc-9678-e784560ff80e · outbound
This paper cites Sampling from large graphs.
Dynamic Graph Message Passing Networks Sampling from large graphs
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.933108Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 74d5d45b-d70b-4ade-8676-cc7998c59194 · outbound
This paper cites Holistic, instance-level human parsing.
Dynamic Graph Message Passing Networks Holistic, instance-level human parsing
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.916524Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 16ab6992-6f41-4dee-a5f6-b5abacc1ee4b · outbound
This paper cites Global aggregation then local dis- tribution in fully convolutional networks.
Dynamic Graph Message Passing Networks Global aggregation then local dis- tribution in fully convolutional networks
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.903057Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 6e357284-6b22-4b7d-9dbd-e65553c0299d · outbound
This paper cites Gated graph sequence neural networks.
Dynamic Graph Message Passing Networks Gated graph sequence neural networks
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.887668Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation eb3c3850-e826-4a3c-aa02-9cb6b5e08cc0 · outbound
This paper cites Deeply learning the messages in message passing inference.
Dynamic Graph Message Passing Networks Deeply learning the messages in message passing inference
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.872316Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation dd951b77-001b-4f9c-b7d1-d65d7a120acd · outbound
This paper cites Girshick, Kaiming He, Bharath Hariharan, and Serge J.
Dynamic Graph Message Passing Networks Girshick, Kaiming He, Bharath Hariharan, and Serge J
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.857814Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 7c6a8b38-873e-473b-a2a9-c8204aba2ed6 · outbound
This paper cites Focal loss for dense object detection.
Dynamic Graph Message Passing Networks Focal loss for dense object detection
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T12:35:52.185039Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bdd3d209-2f95-46ba-860c-ed047607b8c9 · outbound
This paper cites Microsoft coco: Common objects in context.
Dynamic Graph Message Passing Networks Microsoft coco: Common objects in context
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.834247Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 0cb154ec-7e9b-49f4-b3b6-333ff6eafcf7 · outbound
This paper cites Microsoft coco: Common objects in context.
Dynamic Graph Message Passing Networks Microsoft coco: Common objects in context
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.818775Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8143ad34-11e7-4ff7-86fc-a0c6284aba6c · outbound
This paper cites Ssd: Single shot multibox detector.
Dynamic Graph Message Passing Networks Ssd: Single shot multibox detector
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.801141Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 80500e34-58d4-409f-91e9-e047790baeda · outbound
This paper cites maskrcnn-benchmark: Fast, modular reference implementation of Instance Seg- mentation and Object Detection algorithms in PyTorch.
Dynamic Graph Message Passing Networks maskrcnn-benchmark: Fast, modular reference implementation of Instance Seg- mentation and Object Detection algorithms in PyTorch
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.783861Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 53bc379f-70ef-4b01-b4ae-f76efd853c78 · outbound
This paper cites The role of context in object recognition.
Dynamic Graph Message Passing Networks The role of context in object recognition
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.765964Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 349c5978-e0bf-44e8-ac52-06127350d247 · outbound
This paper cites Libra r-cnn: Towards balanced learning for object detection.
Dynamic Graph Message Passing Networks Libra r-cnn: Towards balanced learning for object detection
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.750401Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation aef63ceb-9483-41c5-80bf-4200142061fc · outbound
This paper cites Full-resolution residual networks for semantic segmentation in street scenes.
Dynamic Graph Message Passing Networks Full-resolution residual networks for semantic segmentation in street scenes
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.731217Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 442dd5a6-b607-478f-9220-8cdb11eace77 · outbound
This paper cites Objects in context.
Dynamic Graph Message Passing Networks Objects in context
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.717459Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d6fadbbb-e2e5-4e4f-8dee-df4dccfc53ae · outbound
This paper cites Yolov3: An incremental improvement.
Dynamic Graph Message Passing Networks Yolov3: An incremental improvement
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.704625Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 37b7d79c-bcd4-4e35-b9b4-6c4cf95fac79 · outbound
This paper cites In-place activated batchnorm for memory-optimized training of dnns.
Dynamic Graph Message Passing Networks In-place activated batchnorm for memory-optimized training of dnns
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.688610Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 54d51745-d1ae-4bf1-8ca9-0229a70eeeba · outbound
This paper cites Training region-based object detectors with online hard ex- ample mining.
Dynamic Graph Message Passing Networks Training region-based object detectors with online hard ex- ample mining
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.671776Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 144bf887-3d21-4563-8450-fb74650ba1dc · outbound
This paper cites Very deep convolu- tional networks for large-scale image recognition.
Dynamic Graph Message Passing Networks Very deep convolu- tional networks for large-scale image recognition
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T12:35:52.261015Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4937dd4c-a1be-4319-a329-4632c524214c · outbound
This paper cites Attention is all you need.
Dynamic Graph Message Passing Networks Attention is all you need
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T12:35:52.268865Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a29411b1-41c5-4052-a412-eb48356d6921 · outbound
This paper cites Graph attention networks.
Dynamic Graph Message Passing Networks Graph attention networks
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.624420Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation bae2db8d-8156-4baf-a5a4-efbf8985aed7 · outbound
This paper cites Understanding convolu- tion for semantic segmentation.
Dynamic Graph Message Passing Networks Understanding convolu- tion for semantic segmentation
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.609432Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation b5c1e88a-08cb-425c-baee-877d2f145c70 · outbound
This paper cites Non-local neural networks.
Dynamic Graph Message Passing Networks Non-local neural networks
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.593788Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation c36afff9-d4e1-4288-badd-bf8f497c02fa · outbound
This paper cites Pay less attention with lightweight and dynamic convolutions.
Dynamic Graph Message Passing Networks Pay less attention with lightweight and dynamic convolutions
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.578525Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 00535288-82f9-4340-8c06-824141a77340 · outbound
This paper cites Aggregated residual transformations for deep neural networks.
Dynamic Graph Message Passing Networks Aggregated residual transformations for deep neural networks
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.563750Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ba217a3c-28b3-454a-9621-2c8e3a047c87 · outbound
This paper cites Learning deep structured multi-scale features using attention-gated crfs for contour prediction.
Dynamic Graph Message Passing Networks Learning deep structured multi-scale features using attention-gated crfs for contour prediction
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.549406Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 277ec891-c9e9-4c23-b07a-114c484fe548 · outbound
This paper cites Denseaspp for semantic segmentation in street scenes.
Dynamic Graph Message Passing Networks Denseaspp for semantic segmentation in street scenes
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.536449Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 25fef9ee-817e-4cc7-9400-7441974bbb4e · outbound
This paper cites Bisenet: Bilateral segmentation network for real-time semantic segmentation.
Dynamic Graph Message Passing Networks Bisenet: Bilateral segmentation network for real-time semantic segmentation
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.521463Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 019092b2-c6d3-4594-b4b4-95aa79715248 · outbound
This paper cites Multi-scale context aggrega- tion by dilated convolutions.
Dynamic Graph Message Passing Networks Multi-scale context aggrega- tion by dilated convolutions
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.505429Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 3df83839-978f-4580-aea3-e50e2983e7b9 · outbound
This paper cites Ocnet: Object context network for scene parsing.
Dynamic Graph Message Passing Networks Ocnet: Object context network for scene parsing
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.487763Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 505eb6ca-c228-4ee1-bde9-389a0b4ba86c · outbound
This paper cites Dual graph convolutional network for semantic segmentation.
Dynamic Graph Message Passing Networks Dual graph convolutional network for semantic segmentation
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.467383Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 29da8e33-0cfc-420f-ae02-06063ff52c38 · outbound
This paper cites Pyramid scene parsing network.
Dynamic Graph Message Passing Networks Pyramid scene parsing network
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.451847Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 9bb963e5-db95-4d58-9cc6-9ebb07cc59e3 · outbound
This paper cites Psanet: Point-wise spatial attention network for scene parsing.
Dynamic Graph Message Passing Networks Psanet: Point-wise spatial attention network for scene parsing
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.435087Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 05361c54-556a-46f5-afe6-f03ceb025ca5 · outbound
This paper cites an unresolved cited work.
Dynamic Graph Message Passing Networks Unresolved cited work
Reference 62
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T12:35:52.416800Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5cd8ae6e-afd3-4397-954c-2ec558690896 · outbound
This paper cites De- formable convnets v2: More deformable, better results.
Dynamic Graph Message Passing Networks De- formable convnets v2: More deformable, better results
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:35:52.398236Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
No inbound Pith citation observations are available.