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Paper Citation Record · LEDGER

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation

As of 18 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 0 inbound Pith citation observations for arXiv:2508.04022.

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

pith.paper-citation-record.v1
2508.04022 v1

Coverage vector

measured 63 of 63 reference resolution

Typed states for the displayed outbound observations.

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measured 63 of 63 standing notices

One-hop event checks from named stored sources.

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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

63 of 63 outbound references displayed

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External citation measurements

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Outbound references

Observation f6519a2f-745c-4a68-85b5-9a8d94362c75 · outbound

This paper cites Land cover classification from remote sensing images based on multi-scale fully convolutional network,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Land cover classification from remote sensing images based on multi-scale fully convolutional network,

Reference 1

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Observation 220e60ad-f866-4b02-9459-5a3bb77bb337 · outbound

This paper cites Land cover mapping at very high resolution with rotation equivariant cnns: Towards small yet accurate models,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Land cover mapping at very high resolution with rotation equivariant cnns: Towards small yet accurate models,

Reference 2

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Observation 8e1606fd-198d-4285-a0d9-85954fdf54c6 · outbound

This paper cites A scale-invariant change detection method for land use/cover change research,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation A scale-invariant change detection method for land use/cover change research,

Reference 3

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Observation d8167c8f-e1ab-4414-a2d2-80df3f0bc2b9 · outbound

This paper cites Siamese kpconv: 3d multiple change detection from raw point clouds using deep learning,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Siamese kpconv: 3d multiple change detection from raw point clouds using deep learning,

Reference 4

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Observation cec57662-e815-4eff-9650-e357bba77f21 · outbound

This paper cites Examining the impacts of future land use/land cover changes on climate in punjab province, pakistan: Implications for environmental sustainability and economic growth,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Examining the impacts of future land use/land cover changes on climate in punjab province, pakistan: Implications for environmental sustainability and economic growth,

Reference 5

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Observation 3e8177b6-76a0-4e29-b992-15e15f6d63f8 · outbound

This paper cites Dgnet: Distribution guided efficient learning for oil spill image segmentation,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Dgnet: Distribution guided efficient learning for oil spill image segmentation,

Reference 6

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Observation 4431c32f-cb53-4b1f-89ac-51470a1e2a83 · outbound

This paper cites Fully convolutional networks for semantic segmentation,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Fully convolutional networks for semantic segmentation,

Reference 7

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Observation 83503226-f82d-49f7-93a3-cfce31c42a27 · outbound

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

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation U-net: Convolutional networks for biomedical image segmentation,

Reference 8

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Observation d0e637a0-514c-472c-9993-0035c865fd4c · outbound

This paper cites Encoder- decoder with atrous separable convolution for semantic image segmen- tation,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Encoder- decoder with atrous separable convolution for semantic image segmen- tation,

Reference 9

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Observation b8c6d116-8e62-430f-b73e-0de031a8b2da · outbound

This paper cites Normattention- psn: A high-frequency region enhanced photometric stereo network with normalized attention,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Normattention- psn: A high-frequency region enhanced photometric stereo network with normalized attention,

Reference 10

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Observation c3322bcb-f51d-4c36-b6f3-210af729d14b · outbound

This paper cites Combining swin transformer with unet for remote sensing image semantic segmentation,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Combining swin transformer with unet for remote sensing image semantic segmentation,

Reference 12

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Observation d43cc841-3dd1-4d9e-b0d9-ae452894bf64 · outbound

This paper cites Unetformer: A unet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Unetformer: A unet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery,

Reference 13

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Observation c466ad27-1642-4c24-afc4-84f7df66cb2a · outbound

This paper cites Promptrestorer: A prompting image restoration method with degrada- tion perception,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Promptrestorer: A prompting image restoration method with degrada- tion perception,

Reference 14

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Observation 1f04d364-4c4b-4782-a962-5c4a8e284382 · outbound

This paper cites Category-guided graph convolu- tion network for semantic segmentation,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Category-guided graph convolu- tion network for semantic segmentation,

Reference 15

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Source-reported events for the cited work

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Observation 30f45968-feb2-45bd-be43-f235ef4a65fc · outbound

This paper cites Category attention guided network for semantic segmentation of fine-resolution remote sensing images,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Category attention guided network for semantic segmentation of fine-resolution remote sensing images,

Reference 16

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Source-reported events for the cited work

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Observation d86ab661-8ece-411c-8536-c91d7eb05c96 · outbound

This paper cites Cgglnet: Semantic seg- mentation network for remote sensing images based on category-guided global-local feature interaction,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Cgglnet: Semantic seg- mentation network for remote sensing images based on category-guided global-local feature interaction,

Reference 17

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Observation 708092a0-7c55-4989-af27-f471ba4249a6 · outbound

This paper cites Class- guided swin transformer for semantic segmentation of remote sensing imagery,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Class- guided swin transformer for semantic segmentation of remote sensing imagery,

Reference 18

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Observation d4e66918-30bf-4a65-be70-443d2e0168e1 · outbound

This paper cites Enhanced multi-level features for very high resolution remote sensing scene classification,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Enhanced multi-level features for very high resolution remote sensing scene classification,

Reference 19

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Observation 62295aa6-0ba0-4a29-a734-ea84628d2a35 · outbound

This paper cites Recovering surface normal and arbitrary images: A dual regression network for photometric stereo,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Recovering surface normal and arbitrary images: A dual regression network for photometric stereo,

Reference 20

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Source-reported events for the cited work

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Observation d77e2b39-5246-4424-bfa8-ecdcd915a0d1 · outbound

This paper cites Attention is all you need,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Attention is all you need,

Reference 21

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Source-reported events for the cited work

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Observation 9fedcac7-34db-4e88-b104-b46e11f3720e · outbound

This paper cites Rethinking Attention with Performers.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Rethinking Attention with Performers

Reference 22

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Observation ed68ae21-b2a4-4d44-a93c-879062e42b8b · outbound

This paper cites Transformers are rnns: Fast autoregressive transformers with linear attention,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Transformers are rnns: Fast autoregressive transformers with linear attention,

Reference 23

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Source-reported events for the cited work

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Observation 21c0b155-274c-4b48-9ec3-6a48c02476af · outbound

This paper cites Deep learning methods for calibrated photometric stereo and beyond,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Deep learning methods for calibrated photometric stereo and beyond,

Reference 24

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Observation 60e40d33-3d7a-418a-b160-53e19155a870 · outbound

This paper cites Esti- mating high-resolution surface normals via low-resolution photometric stereo images,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Esti- mating high-resolution surface normals via low-resolution photometric stereo images,

Reference 25

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Observation 2da84d4c-65da-431e-81f6-baf3e40f5fca · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 26

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Source-reported events for the cited work

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Observation 3813da77-c276-4e9a-ae18-4734e46011ef · outbound

This paper cites Vmamba: Visual state space model,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Vmamba: Visual state space model,

Reference 27

Resolution
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Observation 67f35f23-ee3d-4984-adda-ffdbb92148c1 · outbound

This paper cites Rs-mamba for large remote sensing image dense prediction,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Rs-mamba for large remote sensing image dense prediction,

Reference 28

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation fb418a6c-3228-4843-8c59-a319857fc0ea · outbound

This paper cites Rscama: Remote sensing image change captioning with state space model,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Rscama: Remote sensing image change captioning with state space model,

Reference 29

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Source-reported events for the cited work

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Observation ce9be3c4-40fa-4d07-93e0-28d1d0b99a59 · outbound

This paper cites A mamba-aware spatial spectral cross-modal network for remote sensing classification,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation A mamba-aware spatial spectral cross-modal network for remote sensing classification,

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation ea88707e-7197-4ded-bc9f-bbe747a1ab75 · outbound

This paper cites Fusionmamba: Efficient remote sensing image fusion with state space model,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Fusionmamba: Efficient remote sensing image fusion with state space model,

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 86680bc7-cfe0-4fff-b7a8-033696ecf171 · outbound

This paper cites Incorporating lambertian priors into surface normals measurement,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Incorporating lambertian priors into surface normals measurement,

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation f01662b8-6d77-4f3a-8180-df64bab3b515 · outbound

This paper cites Gr-psn: Learning to estimate surface normal and reconstruct photometric stereo images,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Gr-psn: Learning to estimate surface normal and reconstruct photometric stereo images,

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 950c36ea-e13e-4c9b-8a9f-a63154778297 · outbound

This paper cites Pyramid scene parsing network,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Pyramid scene parsing network,

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation eb67fa26-0c28-47ec-8322-7ca979c0d8ab · outbound

This paper cites Multistage attention resu-net for semantic segmentation of fine-resolution remote sensing images,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Multistage attention resu-net for semantic segmentation of fine-resolution remote sensing images,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:00:44.877491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T01:00:38.744389Z digest=sha256:b18c3dae490c2b045732c7061ac194b3dfedae3a174120054e5303bd95c08d95

Observation 150bb6c5-58f1-4e4a-a903-b9007d6952cd · outbound

This paper cites A novel transformer based semantic segmentation scheme for fine-resolution remote sensing images,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation A novel transformer based semantic segmentation scheme for fine-resolution remote sensing images,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:00:49.513439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T01:00:38.799425Z digest=sha256:d1b3a4a7d628aaa025c5d0e8e9c6e52c86c97493b4ccad94b1277f8a5c889006

Observation 86a5cd85-b411-4751-a417-a6c91a718802 · outbound

This paper cites Masked-attention mask transformer for universal image segmentation,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Masked-attention mask transformer for universal image segmentation,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:00:44.747991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T01:00:38.819703Z digest=sha256:90d7a104e9915372a87b1a90e77bd9e50e836e005ed0bfa2db2cd6b10ac5c04d

Observation 9ad9f2e1-d76d-40b4-b00b-d5b09fdfcf9c · outbound

This paper cites Multiscale prototype contrast network for high-resolution aerial imagery semantic segmentation,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Multiscale prototype contrast network for high-resolution aerial imagery semantic segmentation,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:00:44.638645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T01:00:38.920521Z digest=sha256:6dbb68d4e236cff137d8521846fd2d5c06510ab216b83b648f6d5dba2d3a82b2

Observation 35279cb1-6a9a-4baf-9cbc-ad0f39a16c0b · outbound

This paper cites Log-can: local-global class-aware network for semantic segmentation of remote sensing images,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Log-can: local-global class-aware network for semantic segmentation of remote sensing images,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:00:44.484411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T01:00:39.035890Z digest=sha256:55d3b32c712e146164bb9f241a47dc5fa9c0fe21e62bddc5a40703222d83f4f1

Observation 8325a556-391a-477b-8ef8-3cb5fe71a0ac · outbound

This paper cites Logcan++: Adaptive local-global class-aware network for semantic segmentation of remote sensing images,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Logcan++: Adaptive local-global class-aware network for semantic segmentation of remote sensing images,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:00:44.358322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T01:00:39.137215Z digest=sha256:244f0833086c600e3fc054054bbde7b45c1c4a99850f2a21181ae1f2b192ceb3

Observation dcf3f9d7-2425-422a-a15b-a637bf4d273f · outbound

This paper cites Class guided channel weighting network for fine-grained semantic segmentation,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Class guided channel weighting network for fine-grained semantic segmentation,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:00:44.240365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T01:00:39.188888Z digest=sha256:7825d47a835c790a794cf07d1c902463883d605fcb37563820c0d367a4e9ea48

Observation 560db11d-e848-4981-a798-9568405a6013 · outbound

This paper cites Learning to refine human pose estimation,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Learning to refine human pose estimation,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:00:44.112086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T01:00:39.255652Z digest=sha256:9cf167f23fe420da08f00586702f2c05d9d8697b4baf4d1147593a8bf67667f2

Observation 886f38e2-5117-4ccb-9632-99230ca80716 · outbound

This paper cites Prototypical networks for few-shot learning,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Prototypical networks for few-shot learning,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:00:43.951902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T01:00:39.364821Z digest=sha256:7cc22a8be7ec7395df1dea22ddd6ecf2a3f934bc40b7e282de5e8577f7c9bd56

Observation 46057c59-dd34-4718-b094-e804a5442b6e · outbound

This paper cites Hippo: Recurrent memory with optimal polynomial projections,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Hippo: Recurrent memory with optimal polynomial projections,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:00:43.799709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T01:00:39.498087Z digest=sha256:8ab9d2a7eaed4f48511ac34c41da67be0cbf688143a4c1636870a8cb09ca152b

Observation 4cf78e56-7e8e-4f8d-a623-9459f93062b0 · outbound

This paper cites Combining recurrent, convolutional, and continuous-time models with linear state space layers,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Combining recurrent, convolutional, and continuous-time models with linear state space layers,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:00:43.650209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T01:00:39.635380Z digest=sha256:aa9b7dda7d949001fb4cf5425e2c7f1842a5c5cbb7800c2e16c8d2f84a8d163d

Observation db9f8f1f-4d9e-4b23-a863-01bc47490d15 · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Efficiently Modeling Long Sequences with Structured State Spaces

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-06T01:00:39.707949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T01:00:39.707949Z digest=sha256:16cf205ab16e873192e8ce92eb6308b26adf8e9f69b38d52947155cdd1c632d0

Observation 9c2ed3bb-34c6-4f4f-abb7-f4fc1f7ddf14 · outbound

This paper cites Liquid Structural State-Space Models.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Liquid Structural State-Space Models

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-06T01:00:39.783246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T01:00:39.783246Z digest=sha256:b6eb7c57e0ef61d6b4077c00105be479625f8d836b6c6c044b9eaf371482da11

Observation 3e16e536-b3da-4707-85dc-7dc7b8510ca4 · outbound

This paper cites Simplified State Space Layers for Sequence Modeling.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Simplified State Space Layers for Sequence Modeling

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-06T01:00:39.901263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T01:00:39.901263Z digest=sha256:76fe8d191633b1b85a0053f6a2d90736a461858680087d162ddad4c3b5bfdffe

Observation 6695efcc-a676-4041-9cf5-6512d37f06af · outbound

This paper cites Samba: Semantic segmentation of remotely sensed images with state space model,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Samba: Semantic segmentation of remotely sensed images with state space model,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:00:43.433099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T01:00:39.955034Z digest=sha256:dccfc77f10374215806afd095dc03370c1014aad00115aeaf3fa6befb1f6f3f0

Observation 3241025d-076e-4afc-bbe3-94e833c5b4d3 · outbound

This paper cites PyramidMamba: Rethinking Pyramid Feature Fusion with Selective Space State Model for Semantic Segmentation of Remote Sensing Imagery.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation PyramidMamba: Rethinking Pyramid Feature Fusion with Selective Space State Model for Semantic Segmentation of Remote Sensing Imagery

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-06T01:00:40.024644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T01:00:40.024644Z digest=sha256:177ec14bb6f273a80171b09a1be1adfc475cbd35ad69e6b83929df13ac7edafa

Observation 3d2e44ce-fcda-4d1f-98ba-8484db0dbe86 · outbound

This paper cites A convnet for the 2020s,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation A convnet for the 2020s,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:00:43.279733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T01:00:40.156922Z digest=sha256:bebd3e940b61f45a2c266911c34841f022fc2355cfac968e0ce5bedee27af8d0

Observation e166c630-875a-42e7-b280-db3b7676f054 · outbound

This paper cites Pin the memory: Learning to generalize semantic segmentation,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Pin the memory: Learning to generalize semantic segmentation,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:00:43.152343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T01:00:40.267151Z digest=sha256:1a52142b9dd0e7872328e905b7072bf9ca1e2c12d8cf5ac0357d2769add20dbd

Observation c8682930-155b-4052-be44-2695fcbd121f · outbound

This paper cites Structtoken: Rethinking semantic segmentation with structural prior,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Structtoken: Rethinking semantic segmentation with structural prior,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:00:42.996152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T01:00:40.319598Z digest=sha256:430f6277b73bd4f7ac7f07d11662df914af325464c045429924094627c188397

Observation 8bbac2b8-fbeb-4ba8-85fd-7da6b32f8d7b · outbound

This paper cites Semantic segmentation of remote sensing images using multiway fusion network,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Semantic segmentation of remote sensing images using multiway fusion network,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:00:42.780951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T01:00:40.374998Z digest=sha256:90886c9b89b02bda9530324baf9f307806c83906d79a41d43caa7388c824c9ac

Observation 2d8cfbf1-7177-41bc-935a-841ac93c93eb · outbound

This paper cites RWKV: Reinventing RNNs for the Transformer Era.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation RWKV: Reinventing RNNs for the Transformer Era

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-06T01:00:40.465729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T01:00:40.465729Z digest=sha256:20086f5b722842742f69851720840fb8290ec3b9a9e1073dd8884954a7794e4d

Observation 8edba2d7-6dde-4e53-895d-9000b230cbf0 · outbound

This paper cites Hyena hierarchy: Towards larger con- volutional language models,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Hyena hierarchy: Towards larger con- volutional language models,

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:00:42.590933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T01:00:40.546379Z digest=sha256:6c6a4eabebe950b6c985e07de9becc5c565590366e3fb597fc739356d9e3968e

Observation e41541cf-5ea7-4794-b443-3e081dae173a · outbound

This paper cites Deep learning face representa- tion by joint identification-verification,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Deep learning face representa- tion by joint identification-verification,

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:00:42.458104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T01:00:40.651671Z digest=sha256:c860260e59d43e7c5c03976f78fe8869d265590ea241203cc8c868b2bdd3a8a4

Observation b9a1cbe4-0e4d-4aed-93be-855d2a08f444 · outbound

This paper cites Dual-branch network for spatial-channel stream modeling based on the state space model for remote sensing image segmentation,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Dual-branch network for spatial-channel stream modeling based on the state space model for remote sensing image segmentation,

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:00:42.216781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T01:00:40.787768Z digest=sha256:f9ddcc61b4b169473dd1aed2856c0cef54650e201e203f8f5dda3bc57fd8215a

Observation 8b9f273f-1169-428f-bc04-d78882d1ce88 · outbound

This paper cites Parallelizing Linear Recurrent Neural Nets Over Sequence Length.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Parallelizing Linear Recurrent Neural Nets Over Sequence Length

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-06T01:00:40.887033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T01:00:40.887033Z digest=sha256:2836bc77b4c9940ecbf90c626873c86d04bbb95e7f2726f4db060f8647584c08

Observation b24d66e7-be21-4d46-84ac-f352204fef2f · outbound

This paper cites LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-06T01:00:40.950101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T01:00:40.950101Z digest=sha256:4c927da5ff4b2bd3c8e6d49c6b4b210c9fa7d688805f1c44086e64bcb56078a4

Observation daae8708-042f-4370-a25f-462292b9ec3b · outbound

This paper cites Maxvit: Multi-axis vision transformer,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Maxvit: Multi-axis vision transformer,

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:00:42.069539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T01:00:41.030943Z digest=sha256:d3f54644fb95a4407cdd97ea7ab2c489af4895234ff97d1063b27fad153ce760

Observation e8b171ce-9120-4683-883e-c6fedc90bcc5 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows,.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:00:41.932941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T01:00:41.120632Z digest=sha256:f3a99943b8d13791a84f078c2a6337d7d0af56229b3793f99a81904c0822a170

Observation afca1150-c8d9-43e8-bca2-5cf5e71a06be · outbound

This paper cites Vision Mamba: A Comprehensive Survey and Taxonomy.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Vision Mamba: A Comprehensive Survey and Taxonomy

Reference 63

Resolution
unresolved
no resolver link, observed 2026-08-06T01:00:41.273682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T01:00:41.273682Z digest=sha256:b0bfff32e096ad4a0271e79a684416f3f437bfb9fb74c73b0582744637161353

Observation 57d1d8a0-00d6-4753-bb6e-2f3ab64c1f4d · outbound

This paper cites Subsequently, he served as an Associate Dean for the College of Engineering for eight years.

Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation Subsequently, he served as an Associate Dean for the College of Engineering for eight years

Reference 1991

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:00:41.675350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T01:00:41.369759Z digest=sha256:e22b15541fc1b5a1336f40b93be0abfb5d4f9b045438a737c9c2f4f10da69431

Pith citing papers

No inbound Pith citation observations are available.