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Paper Citation Record · LEDGER

Unsupervised Instance Segmentation with Superpixels

As of 11 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2509.05352.

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

pith.paper-citation-record.v1
2509.05352 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T11:21:00.950583Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

50 of 50 outbound references displayed

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  • verified fuzzy37
  • unresolved12
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 377c2980-73b8-4d68-a854-bef80ce98253 · outbound

This paper cites Rama: A rapid multicut algorithm on gpu, in: Proceedings of the IEEE /CVF Conference on Computer Vision and Pattern Recognition, pp.

Unsupervised Instance Segmentation with Superpixels Rama: A rapid multicut algorithm on gpu, in: Proceedings of the IEEE /CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 1

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Observation 6235aeec-8b53-4258-97e0-4773ba991da1 · outbound

This paper cites Superpixels and polygons using sim- ple non-iterative clustering, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp.

Unsupervised Instance Segmentation with Superpixels Superpixels and polygons using sim- ple non-iterative clustering, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp

Reference 2

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Observation 5b5e92e3-ec2c-4892-8c3d-a357b4a084c4 · outbound

This paper cites an unresolved cited work.

Unsupervised Instance Segmentation with Superpixels Unresolved cited work

Reference 3

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Observation e8deaae9-4293-4c73-8b5c-91ffcc199955 · outbound

This paper cites an unresolved cited work.

Unsupervised Instance Segmentation with Superpixels Unresolved cited work

Reference 4

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation beaf41ce-d1b3-407e-9bdf-9486a12a00d6 · outbound

This paper cites Superpixel segmentation using gaussian mixture model.

Unsupervised Instance Segmentation with Superpixels Superpixel segmentation using gaussian mixture model

Reference 5

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 66bd92d4-d653-4f5a-8adb-af9dad3106fa · outbound

This paper cites Emerging properties in self-supervised vision trans- formers, in: Proceedings of the IEEE /CVF international conference on computer vision, pp.

Unsupervised Instance Segmentation with Superpixels Emerging properties in self-supervised vision trans- formers, in: Proceedings of the IEEE /CVF international conference on computer vision, pp

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 2d0538dd-4c0b-4aa4-bc52-ebb22fa9c284 · outbound

This paper cites Masked-attention mask transformer for universal image segmentation, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.

Unsupervised Instance Segmentation with Superpixels Masked-attention mask transformer for universal image segmentation, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp

Reference 7

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Observation 592bbd5d-60d2-423d-bcca-3fe04a8b28ce · outbound

This paper cites Ima- genet: A large-scale hierarchical image database, in: 2009 IEEE confer- ence on computer vision and pattern recognition, Ieee.

Unsupervised Instance Segmentation with Superpixels Ima- genet: A large-scale hierarchical image database, in: 2009 IEEE confer- ence on computer vision and pattern recognition, Ieee

Reference 8

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Observation 6f2ca9f2-48a2-41da-9cf8-523e75e70bba · outbound

This paper cites an unresolved cited work.

Unsupervised Instance Segmentation with Superpixels Unresolved cited work

Reference 9

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Observation d86b5e8f-318d-479e-b28a-c0688d63c1f8 · outbound

This paper cites an unresolved cited work.

Unsupervised Instance Segmentation with Superpixels Unresolved cited work

Reference 10

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Observation 6fcb0e9d-23de-48a9-8593-630bf692952d · outbound

This paper cites an unresolved cited work.

Unsupervised Instance Segmentation with Superpixels Unresolved cited work

Reference 11

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Observation 263f1e6e-3045-4b3d-ab33-6c8871565c02 · outbound

This paper cites Are we ready for autonomous driving? the kitti vision benchmark suite, in: 2012 IEEE conference on computer vision and pattern recognition, IEEE.

Unsupervised Instance Segmentation with Superpixels Are we ready for autonomous driving? the kitti vision benchmark suite, in: 2012 IEEE conference on computer vision and pattern recognition, IEEE

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 3a07e46b-39df-43ae-92ef-2b3356bf35a6 · outbound

This paper cites Unsupervised semantic segmentation by distilling feature correspondences, in: International Confer- ence on Learning Representations, pp.

Unsupervised Instance Segmentation with Superpixels Unsupervised semantic segmentation by distilling feature correspondences, in: International Confer- ence on Learning Representations, pp

Reference 13

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 5ec11aa5-9400-4cfa-93dd-95a5378d61d9 · outbound

This paper cites Mask r-cnn, in: Proceedings of the IEEE international conference on computer vision, pp.

Unsupervised Instance Segmentation with Superpixels Mask r-cnn, in: Proceedings of the IEEE international conference on computer vision, pp

Reference 14

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 0e7f0754-0bd8-4908-b1eb-3ce8e2ad33a1 · outbound

This paper cites Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp.

Unsupervised Instance Segmentation with Superpixels Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp

Reference 15

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Source-reported events for the cited work

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Observation f93db7a8-08c7-4e84-82dd-2cff415c47d9 · outbound

This paper cites Dynamic random walk for superpixel segmentation.

Unsupervised Instance Segmentation with Superpixels Dynamic random walk for superpixel segmentation

Reference 16

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Observation cc689b7c-a099-413e-a502-d8c37a748699 · outbound

This paper cites Gcbanet: A global context boundary- aware network for sar ship instance segmentation.

Unsupervised Instance Segmentation with Superpixels Gcbanet: A global context boundary- aware network for sar ship instance segmentation

Reference 17

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Observation c0802441-5bcc-4983-9e48-782cfb7e70d8 · outbound

This paper cites an unresolved cited work.

Unsupervised Instance Segmentation with Superpixels Unresolved cited work

Reference 18

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Observation cdc02c8a-073d-49ee-ab18-80b118bb958d · outbound

This paper cites Microsoft coco: Common objects in context, in: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, Springer.

Unsupervised Instance Segmentation with Superpixels Microsoft coco: Common objects in context, in: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, Springer

Reference 19

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation b812a59a-e835-40aa-9fc2-b94adcfe8258 · outbound

This paper cites an unresolved cited work.

Unsupervised Instance Segmentation with Superpixels Unresolved cited work

Reference 20

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Observation 44f3506f-2ee6-40c7-8cd3-12e87ee20dbf · outbound

This paper cites an unresolved cited work.

Unsupervised Instance Segmentation with Superpixels Unresolved cited work

Reference 21

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Observation 398333e6-a8c5-4922-a807-50ff54242b86 · outbound

This paper cites Unsu- pervised universal image segmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Unsupervised Instance Segmentation with Superpixels Unsu- pervised universal image segmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 22

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 4e88f1be-1799-4429-9be0-6d64495a1914 · outbound

This paper cites Coarse-to- fine video instance segmentation with factorized conditional appearance flows.

Unsupervised Instance Segmentation with Superpixels Coarse-to- fine video instance segmentation with factorized conditional appearance flows

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 42b8c605-f9a7-48e3-ae4f-a67e9ab8e438 · outbound

This paper cites Learning hierarchical embedding for video instance segmentation, in: Proceedings of the 29th ACM international conference on multimedia, pp.

Unsupervised Instance Segmentation with Superpixels Learning hierarchical embedding for video instance segmentation, in: Proceedings of the 29th ACM international conference on multimedia, pp

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation d48589f4-13ec-4a14-ac79-02d312fc9907 · outbound

This paper cites Cuts3d: Cutting semantics in 3d for 2d unsupervised instance segmen- tation.

Unsupervised Instance Segmentation with Superpixels Cuts3d: Cutting semantics in 3d for 2d unsupervised instance segmen- tation

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 71a2cf97-d3e3-4143-8495-36091b6117b5 · outbound

This paper cites Localizing objects with self-supervised transformers and no labels, in: Proceedings of the British Machine Vision Conference (BMVC), pp.

Unsupervised Instance Segmentation with Superpixels Localizing objects with self-supervised transformers and no labels, in: Proceedings of the British Machine Vision Conference (BMVC), pp

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation bc38ec17-7c02-4b58-86ad-0ce4cd8c90f9 · outbound

This paper cites Learn- able tree filter for structure-preserving feature transform.

Unsupervised Instance Segmentation with Superpixels Learn- able tree filter for structure-preserving feature transform

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation bc92a405-69de-4b34-9584-2d7e5c1454d7 · outbound

This paper cites Yfcc100m: The new data in multimedia research.

Unsupervised Instance Segmentation with Superpixels Yfcc100m: The new data in multimedia research

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 543322dd-0fc9-4565-862b-fdeca6f778d9 · outbound

This paper cites Discovering Object Masks with Transformers for Unsupervised Semantic Segmentation.

Unsupervised Instance Segmentation with Superpixels Discovering Object Masks with Transformers for Unsupervised Semantic Segmentation

Reference 29

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 2ef97b47-766a-460f-ac1d-16b0eeaf888e · outbound

This paper cites Attention is all you need.

Unsupervised Instance Segmentation with Superpixels Attention is all you need

Reference 30

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T11:20:59.282101Z digest=sha256:44763307ab8f6bd80cf7088758289ac347e6d5a3f0dd70d3189e992e3df3bbd8

Observation 37767f45-6828-4728-973e-cdd0c315058f · outbound

This paper cites Unidentified video objects: A benchmark for dense, open-world segmentation, in: Proceed- ings of the IEEE /CVF international conference on computer vision, pp.

Unsupervised Instance Segmentation with Superpixels Unidentified video objects: A benchmark for dense, open-world segmentation, in: Proceed- ings of the IEEE /CVF international conference on computer vision, pp

Reference 31

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verified fuzzy
raw_fallback, observed 2026-08-05T11:21:03.051934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 3dba9c6b-634c-462a-a9f2-c6f9e2d761ff · outbound

This paper cites Cut and learn for unsupervised object detection and instance segmentation, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.

Unsupervised Instance Segmentation with Superpixels Cut and learn for unsupervised object detection and instance segmentation, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp

Reference 32

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raw_fallback, observed 2026-08-05T11:21:03.038304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 9dd804ce-2885-4dd7-8bf3-e751cd845da1 · outbound

This paper cites Freesolo: Learning to segment objects without annotations, in: Proceedings of the IEEE /CVF conference on computer vision and pattern recognition, pp.

Unsupervised Instance Segmentation with Superpixels Freesolo: Learning to segment objects without annotations, in: Proceedings of the IEEE /CVF conference on computer vision and pattern recognition, pp

Reference 33

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raw_fallback, observed 2026-08-05T11:21:03.022024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation ea44a726-5604-419b-be0e-545a5c9e542d · outbound

This paper cites Solov2: Dynamic 16 and fast instance segmentation.

Unsupervised Instance Segmentation with Superpixels Solov2: Dynamic 16 and fast instance segmentation

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:21:03.006809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T11:20:59.733596Z digest=sha256:f2b29aa1d5ac67661c66cdd3005dc16bdde93a373ee318afe9e3df6b7f86dea2

Observation 8732b0a7-a47e-4327-a1c9-d1f7ad2bcc71 · outbound

This paper cites Tokencut: Segmenting objects in images and videos with self-supervised transformer and normalized cut.

Unsupervised Instance Segmentation with Superpixels Tokencut: Segmenting objects in images and videos with self-supervised transformer and normalized cut

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:21:02.991035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T11:20:59.878038Z digest=sha256:af0f20dbdff1bb2d064cfb0c6b30922e2f1ab15ba51edd5c144ede59399ab1e1

Observation 66b706e3-ca01-4258-954f-ae5ca9a7dd02 · outbound

This paper cites Noisy boundaries: Lemon or lemon- ade for semi-supervised instance segmentation?, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Unsupervised Instance Segmentation with Superpixels Noisy boundaries: Lemon or lemon- ade for semi-supervised instance segmentation?, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:21:02.974549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T11:21:00.038283Z digest=sha256:49fb2182739eaa88772ccae39e3c98476a6823fa9250cfc14a51338e3e464b00

Observation 5bd0b01e-ef99-4770-aafa-c83141349eac · outbound

This paper cites St ++: Make self- training work better for semi-supervised semantic segmentation, in: Pro- ceedings of the IEEE /CVF conference on computer vision and pattern recognition, pp.

Unsupervised Instance Segmentation with Superpixels St ++: Make self- training work better for semi-supervised semantic segmentation, in: Pro- ceedings of the IEEE /CVF conference on computer vision and pattern recognition, pp

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:21:02.959274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T11:21:00.156880Z digest=sha256:e8fcc0858530f66e6385f6b595278c258d0ca261ce6ea7f7f61a958c2dfc4f4c

Observation 074c585b-a510-4343-bd3f-fb2688a92bfb · outbound

This paper cites unmore: Unsupervised multi-object segmentation via center-boundary reasoning.

Unsupervised Instance Segmentation with Superpixels unmore: Unsupervised multi-object segmentation via center-boundary reasoning

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:21:02.944186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T11:21:00.262927Z digest=sha256:02fde1ee680f04cee8cb85ea2d3b3b9f2c780e9205da1572e35e6a2f7ddb7d14

Observation fb364dda-cd87-46da-96fa-0a6d107acc83 · outbound

This paper cites High-speed ship detection in sar images based on a grid convolutional neural network.

Unsupervised Instance Segmentation with Superpixels High-speed ship detection in sar images based on a grid convolutional neural network

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:21:02.877675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T11:21:00.375785Z digest=sha256:6a6133e2482370e82622851a05808c9d0fc5b78911223373de0c1e95b3ac9a78

Observation 43d4f62c-9907-460e-8ee0-9b00cb452e10 · outbound

This paper cites A full-level context squeeze-and-excitation roi extractor for sar ship instance segmentation.

Unsupervised Instance Segmentation with Superpixels A full-level context squeeze-and-excitation roi extractor for sar ship instance segmentation

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:21:02.738432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T11:21:00.481929Z digest=sha256:28c0891febcf9e344a4ae19005af6c025762609dd85baebcf1aeec50ff324f74

Observation ce971c53-6efe-4493-860b-288d90a4ad20 · outbound

This paper cites Htc + for sar ship instance segmentation.

Unsupervised Instance Segmentation with Superpixels Htc + for sar ship instance segmentation

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:21:02.591033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T11:21:00.581513Z digest=sha256:30ceeb56c6ff5d5cf4e3d170ca7e864c4da25529ae6e323330eb122969204c3f

Observation 373f6fb6-f4f6-45d3-b72d-c83285242056 · outbound

This paper cites A mask attention interaction and scale en- hancement network for sar ship instance segmentation.

Unsupervised Instance Segmentation with Superpixels A mask attention interaction and scale en- hancement network for sar ship instance segmentation

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:21:02.332869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T11:21:00.703363Z digest=sha256:f45e22dd25fd462a3b6d6258431103d3d7e4152f1d1e26a1d7c50af2787c0e6d

Observation 84bdaf0e-eee8-4f96-ba97-8914163936ac · outbound

This paper cites A polarization fusion network with geo- metric feature embedding for sar ship classification.

Unsupervised Instance Segmentation with Superpixels A polarization fusion network with geo- metric feature embedding for sar ship classification

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:21:02.067198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T11:21:00.783056Z digest=sha256:36a782db7fb25af8eebead13dd37a56395015d5bfbc49c0dcf6d829ab4842f76

Observation 41798d62-cde7-463a-a8a9-cf36624c77b3 · outbound

This paper cites Sar ship detection dataset (ssdd): O fficial release and comprehensive data analysis.

Unsupervised Instance Segmentation with Superpixels Sar ship detection dataset (ssdd): O fficial release and comprehensive data analysis

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:21:01.713697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T11:21:00.864378Z digest=sha256:6f17b62438e1d6559b4b2a38acdf71580f3cc02f2c5849e2961cbc638657a1f8

Observation 144235c4-61c1-41c4-9e36-4a868a9b97df · outbound

This paper cites Depthwise separable convo- lution neural network for high-speed sar ship detection.

Unsupervised Instance Segmentation with Superpixels Depthwise separable convo- lution neural network for high-speed sar ship detection

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:21:01.445920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T11:21:00.950583Z digest=sha256:541142e9104b1b1f32006317d31deb6931f3f93765b69751b7421f4685e646bc

Observation 115d8053-6b7d-4954-866e-32a04421bf1e · outbound

This paper cites International journal of computer vision 88, 303–338.

Unsupervised Instance Segmentation with Superpixels International journal of computer vision 88, 303–338

Reference 2010

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:21:03.380067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T11:20:57.638052Z digest=sha256:37763ec462bd837aaec5768b9c871b73ab760eea6644ab1832eea6d3905834d6

Observation 20a36e4a-9601-4dcc-a03e-e1b07266e96d · outbound

This paper cites an unresolved cited work.

Unsupervised Instance Segmentation with Superpixels Unresolved cited work

Reference 2014

Resolution
verified exact
doi, observed 2026-08-05T11:21:01.169282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T11:20:56.958885Z digest=sha256:b0238225293d0c6b4c50c3d6fcd9bee763c7847b2c50c56619959ab2e75651e9

Observation a4aa9d49-28b2-4d2c-82eb-af41c66b5cd6 · outbound

This paper cites an unresolved cited work.

Unsupervised Instance Segmentation with Superpixels Unresolved cited work

Reference 2019

Resolution
unresolved
raw_fallback, observed 2026-08-05T11:21:03.346585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T11:20:57.793455Z digest=sha256:60d2198b7a093726d9c0eaa23905755a4547502c67ec5d1df8795d0b7e2a4b22

Observation 31bff2cd-11e3-4e65-b07f-d0a25a463d78 · outbound

This paper cites 3493–3402.

Unsupervised Instance Segmentation with Superpixels 3493–3402

Reference 2020

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:21:03.411844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T11:20:57.452597Z digest=sha256:2588948fc0b2cb0fa26c3055cf7e9345314f283b7a171129cfc83cf159eb68a4

Observation 2ffd857f-fda5-4736-9e42-d61cd1b509b6 · outbound

This paper cites 10012–10022.

Unsupervised Instance Segmentation with Superpixels 10012–10022

Reference 2021

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:21:03.199464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T11:20:58.569107Z digest=sha256:4d497306d76e3b95ab2f56d8688a70aabf68d8344b9525f7a60f681980be9026

Pith citing papers

No inbound Pith citation observations are available.