Paper Citation Record · LEDGER

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment

As of 22 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:1908.06391.

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

pith.paper-citation-record.v1
1908.06391 v2

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:50:33.347244Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

verified exact2
verified fuzzy14
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 33e0144b-fbd1-488c-88c7-245f0af3e044 · outbound

This paper cites Segnet: A deep convolutional encoder-decoder architecture for image segmentation.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Segnet: A deep convolutional encoder-decoder architecture for image segmentation

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:50:33.852161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b6870d29-102f-40de-9650-fd44e1549797 · outbound

This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-14T12:50:33.189689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8c2ad64-11a2-4060-8ee6-71d3185eba94 · outbound

This paper cites Boxsup: Exploit- ing bounding boxes to supervise convolutional networks for semantic segmentation.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Boxsup: Exploit- ing bounding boxes to supervise convolutional networks for semantic segmentation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:50:33.825106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 559bebe1-4563-41ed-a692-63f5834b61c0 · outbound

This paper cites Few-shot semantic segmen- tation with prototype learning.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Few-shot semantic segmen- tation with prototype learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:50:33.808215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 46e72250-dc51-4513-89c5-1059a68c18b9 · outbound

This paper cites The pascal visual object classes (voc) challenge.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment The pascal visual object classes (voc) challenge

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-14T12:50:33.210138Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f26607a4-b7af-4d60-bf2a-07c2bb740652 · outbound

This paper cites Model- agnostic meta-learning for fast adaptation of deep networks.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Model- agnostic meta-learning for fast adaptation of deep networks

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:50:33.777269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b74f2cf8-fc05-46fc-bd51-b59c9a2faac5 · outbound

This paper cites Semantic contours from inverse detectors.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Semantic contours from inverse detectors

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:50:33.760046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d493f01f-f1cd-48b4-8df2-494875d881cb · outbound

This paper cites an unresolved cited work.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-14T12:50:33.739251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 27992f77-aa3a-4e85-8ffb-a24ccb4e0acf · outbound

This paper cites Scribblesup: Scribble-supervised convolutional networks for semantic segmentation.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Scribblesup: Scribble-supervised convolutional networks for semantic segmentation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:50:33.723284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c4fe29ff-218e-468b-a94e-3fa352e12582 · outbound

This paper cites Reﬁnenet: Multi-path reﬁnement networks for high- resolution semantic segmentation.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Reﬁnenet: Multi-path reﬁnement networks for high- resolution semantic segmentation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T12:50:33.242503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01c6ebb1-31b5-458e-b0e1-adc6450c52cf · outbound

This paper cites Microsoft coco: Common objects in context.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Microsoft coco: Common objects in context

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-14T12:50:33.248985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec303c50-4ba8-4027-819b-4a97a9b6d5bc · outbound

This paper cites Learning to propagate labels: Transductive propagation network for few-shot learn- ing.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Learning to propagate labels: Transductive propagation network for few-shot learn- ing

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:50:33.680515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 64d7ada7-0bec-4322-9213-a7f0148b99da · outbound

This paper cites Fully convolutional networks for semantic segmentation.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Fully convolutional networks for semantic segmentation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-14T12:50:33.260717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7711f37c-0ebb-451a-b50e-95282f3d9c54 · outbound

This paper cites Tadam: Task dependent adaptive metric for improved few-shot learning.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Tadam: Task dependent adaptive metric for improved few-shot learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:50:33.656023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 198f9d9c-dac7-4f4f-855b-f7e49bc59558 · outbound

This paper cites Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmenta- tion.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmenta- tion

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:50:33.638865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5d6708f2-08d2-4909-9669-d9940efefaef · outbound

This paper cites Conditional networks for few-shot semantic segmentation.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Conditional networks for few-shot semantic segmentation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:50:33.621606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0bdc9bbf-990b-4e16-a526-75dbaf5538a6 · outbound

This paper cites Few-Shot Segmentation Propagation with Guided Networks.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Few-Shot Segmentation Propagation with Guided Networks

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-14T12:50:33.478613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e624a18e-5fb1-4e2f-869a-3f2edf055045 · outbound

This paper cites Optimization as a model for few-shot learning.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Optimization as a model for few-shot learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-14T12:50:33.288795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49094711-ef92-45ae-8e7b-65ae247213f0 · outbound

This paper cites Berg, and Li Fei-Fei.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Berg, and Li Fei-Fei

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-14T12:50:33.293959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 616d5e89-4c4a-4112-93fa-67040f14552a · outbound

This paper cites Few-shot learning with graph neural networks.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Few-shot learning with graph neural networks

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:50:33.582836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation affdcbf3-e8a2-4a03-adf7-9e307894afe7 · outbound

This paper cites One-Shot Learning for Semantic Segmentation.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment One-Shot Learning for Semantic Segmentation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-14T12:50:33.306275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac8d43d7-c5d7-4055-a4d4-3e1d93c25106 · outbound

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

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-14T12:50:33.311633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f05e688c-f727-453b-b236-64944bf4ee28 · outbound

This paper cites Prototypi- cal networks for few-shot learning.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Prototypi- cal networks for few-shot learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:50:33.566096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 722bbe4b-e77f-4e30-ba9f-e94b3e051b8f · outbound

This paper cites Learning to compare: Re- lation network for few-shot learning.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Learning to compare: Re- lation network for few-shot learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:50:33.548967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4875a911-4767-4f4d-b927-47768ac76927 · outbound

This paper cites Matching networks for one shot learning.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Matching networks for one shot learning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-14T12:50:33.327931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a27c5a8-afff-4b9c-a2c7-d254b9600ded · outbound

This paper cites Object region mining with adversarial erasing: A simple classiﬁcation to semantic segmentation approach.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Object region mining with adversarial erasing: A simple classiﬁcation to semantic segmentation approach

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:50:33.519320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1933497a-e090-43d3-8070-3afebac04cd2 · outbound

This paper cites Multi-Scale Context Aggregation by Dilated Convolutions.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Multi-Scale Context Aggregation by Dilated Convolutions

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-14T12:50:33.337066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c378d53-6244-47d3-90c8-6d2ec1c7dee4 · outbound

This paper cites SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-14T12:50:33.398306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 84d22b4a-568d-4d93-b505-f91e1b33b334 · outbound

This paper cites Pyramid scene parsing network.

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment Pyramid scene parsing network

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-14T12:50:33.347244Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.