SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation | Paper Citation Record

Observation 3fc5f7fb-615c-45b1-8dcf-cceb04d340c1 · outbound

This paper cites Land-cover classification with high-resolution remote sensing images using transferable deep models,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Land-cover classification with high-resolution remote sensing images using transferable deep models,

Reference 1

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Observation 31e13516-841f-4715-860c-f11d51d649b1 · outbound

This paper cites Enabling country-scale land cover mapping with meter-resolution satellite imagery,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Enabling country-scale land cover mapping with meter-resolution satellite imagery,

Reference 2

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Observation d78d086d-5f4e-4c88-adfe-df2eeb4ed6aa · outbound

This paper cites Fully convolutional networks for semantic segmentation,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Fully convolutional networks for semantic segmentation,

Reference 3

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Observation 92efb76b-1094-42ee-b70d-ccadd4799c37 · outbound

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

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation U-net: Convolutional networks for biomedical image segmentation,

Reference 4

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Observation 672c0fd1-19c3-4976-8810-7474193440e2 · outbound

This paper cites Segnet: A deep con- volutional encoder-decoder architecture for image segmentation,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Segnet: A deep con- volutional encoder-decoder architecture for image segmentation,

Reference 5

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Observation 8e94574e-1a6a-460f-96f4-aa4afd1090c9 · outbound

This paper cites Per-pixel classification is not all you need for semantic segmentation,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Per-pixel classification is not all you need for semantic segmentation,

Reference 6

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Observation ebe2ea80-c1be-4423-babd-b7646a360f5b · outbound

This paper cites Segment anything,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Segment anything,

Reference 7

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Observation 6d5752d5-ebb1-4016-b45a-5cf40c730f40 · outbound

This paper cites Rsprompter: Learning to prompt for remote sensing instance seg- mentation based on visual foundation model,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Rsprompter: Learning to prompt for remote sensing instance seg- mentation based on visual foundation model,

Reference 8

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Observation f16f8f78-e73e-4a51-bc8c-a71513319879 · outbound

This paper cites From contexts to locality: Ultra-high resolution image segmentation via locality-aware contextual correlation,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation From contexts to locality: Ultra-high resolution image segmentation via locality-aware contextual correlation,

Reference 9

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Observation f1f70c32-5620-426f-b2e2-65a5e1fdc647 · outbound

This paper cites Rest: Holistic learning for end-to-end semantic segmentation of whole-scene remote sensing imagery,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Rest: Holistic learning for end-to-end semantic segmentation of whole-scene remote sensing imagery,

Reference 10

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Observation 2a228aef-6dd3-4123-86b5-ce73ab016ef1 · outbound

This paper cites Collaborative global-local networks for memory-efficient segmentation of ultra-high resolution images,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Collaborative global-local networks for memory-efficient segmentation of ultra-high resolution images,

Reference 11

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Observation 85a2afdd-15cc-49e6-b492-68af81505a61 · outbound

This paper cites Patch proposal network for fast semantic segmentation of high-resolution images,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Patch proposal network for fast semantic segmentation of high-resolution images,

Reference 12

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Observation e7bae7aa-a9d4-4281-b0dd-cc9b10c2bf24 · outbound

This paper cites Uhrsnet: A semantic segmentation network specifically for ultra-high- resolution images,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Uhrsnet: A semantic segmentation network specifically for ultra-high- resolution images,

Reference 13

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Observation 1a0d20f5-bd0b-458d-9922-208a9ad27bc4 · outbound

This paper cites Isdnet: Integrating shallow and deep net- works for efficient ultra-high resolution segmentation,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Isdnet: Integrating shallow and deep net- works for efficient ultra-high resolution segmentation,

Reference 14

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Observation b842873f-e041-43c3-9afd-3cb00d105926 · outbound

This paper cites Guided patch-grouping wavelet transformer with spatial congruence for ultra-high resolution segmentation,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Guided patch-grouping wavelet transformer with spatial congruence for ultra-high resolution segmentation,

Reference 15

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Observation eec7d046-5603-4f39-af4e-84de956dc1ad · outbound

This paper cites Pyramid scene parsing network,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Pyramid scene parsing network,

Reference 16

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Observation 4d15b85b-580e-4fd0-8630-fdb43b045fe7 · outbound

This paper cites Icnet for real-time semantic segmentation on high-resolution images,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Icnet for real-time semantic segmentation on high-resolution images,

Reference 17

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Observation c179d657-9a39-4c35-91a6-b7ba35d90bd3 · outbound

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

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Encoder- decoder with atrous separable convolution for semantic image segmen- tation,

Reference 18

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Observation d2ed0d59-ce4e-4267-9f34-418d765f9692 · outbound

This paper cites D-linknet: Linknet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation D-linknet: Linknet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction,

Reference 19

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Observation 8b569c26-c2f6-4dd5-a815-4ec9f49f8ba5 · outbound

This paper cites Deep residual learning for image recognition,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Deep residual learning for image recognition,

Reference 20

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Observation 765932fd-da25-4f4c-b9c1-e0e8e92ba36d · outbound

This paper cites Multi-scale context ag- gregation for semantic segmentation of remote sensing images,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Multi-scale context ag- gregation for semantic segmentation of remote sensing images,

Reference 21

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Observation 1ddaf673-b9ef-4f69-a35b-40229fdf9c18 · outbound

This paper cites Bisenet: Bilateral segmentation network for real-time semantic segmentation,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Bisenet: Bilateral segmentation network for real-time semantic segmentation,

Reference 22

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Observation 0f234054-bfab-4839-a5ab-9cccef058586 · outbound

This paper cites Re- thinking bisenet for real-time semantic segmentation,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Re- thinking bisenet for real-time semantic segmentation,

Reference 23

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Observation d539caaf-cd1f-4aa4-a8e0-cca5ee7a2174 · outbound

This paper cites An image is worth 16x16 words: transformers for image recognition at scale,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation An image is worth 16x16 words: transformers for image recognition at scale,

Reference 24

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Observation f230ebe5-3c28-4d4d-b2b4-6718179220fa · outbound

This paper cites Segformer: Simple and efficient design for semantic segmentation with transformers,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Segformer: Simple and efficient design for semantic segmentation with transformers,

Reference 25

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Observation e4039af3-1ba3-4910-8c35-910f14766faa · outbound

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

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 26

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Observation a2af4dfc-a20d-4912-bc9c-8ad6df3a9449 · outbound

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

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Masked-attention mask transformer for universal image segmentation,

Reference 27

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Observation a779d667-f15c-4603-8962-80f59170433d · outbound

This paper cites Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,

Reference 28

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Observation da49b397-ed05-4e61-9cf1-923bee61f93a · outbound

This paper cites Dual attention network for scene segmentation,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Dual attention network for scene segmentation,

Reference 29

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Observation b4757bd3-52dd-4bc2-8d26-5fa7ffc2cd00 · outbound

This paper cites Scattnet: Semantic segmentation network with spatial and channel attention mechanism for high-resolution remote sensing images,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Scattnet: Semantic segmentation network with spatial and channel attention mechanism for high-resolution remote sensing images,

Reference 30

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Observation d8d9c0b3-c763-48ec-b4c3-43a1da6495b6 · outbound

This paper cites Lanet: Local attention embedding to improve the semantic segmentation of remote sensing images,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Lanet: Local attention embedding to improve the semantic segmentation of remote sensing images,

Reference 31

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Observation b109aa64-c9bd-445a-8f2b-9962a0c92852 · outbound

This paper cites Rsrefseg 2: Decoupling referring remote sensing image segmentation with founda- tion models,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Rsrefseg 2: Decoupling referring remote sensing image segmentation with founda- tion models,

Reference 32

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Observation 74783052-29bb-4ea3-8eb0-65da0eb0fcab · outbound

This paper cites arXiv preprint arXiv:2511.20306 (2025) 3.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation arXiv preprint arXiv:2511.20306 (2025) 3

Reference 33

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Observation f218fcf9-a73b-4387-960d-115f9a88ee44 · outbound

This paper cites Mamba: Linear-time sequence modeling with selective state spaces,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Mamba: Linear-time sequence modeling with selective state spaces,

Reference 34

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Observation 7a715579-053f-4256-8794-97b8005e7d7e · outbound

This paper cites State-space models,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation State-space models,

Reference 35

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Observation ba608528-51a8-4f1f-afd1-dd77263eb7b0 · outbound

This paper cites Dynamicvis: An efficient and general visual foundation model for remote sensing image understanding.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Dynamicvis: An efficient and general visual foundation model for remote sensing image understanding

Reference 36

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Observation d05faf8a-ad60-43fc-bfb0-d33caf904ee0 · outbound

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

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Rs-mamba for large remote sensing image dense prediction,

Reference 37

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Observation 7c1e56ea-a15e-4bc2-9a2a-b27af1ae28f9 · outbound

This paper cites Rs 3 mamba: Visual state space model for remote sensing image semantic segmentation,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Rs 3 mamba: Visual state space model for remote sensing image semantic segmentation,

Reference 38

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Observation c2cf2fa8-a6b2-4d94-a393-705b5b02e1e2 · outbound

This paper cites Unet- mamba: An efficient unet-like mamba for semantic segmentation of high-resolution remote sensing images,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Unet- mamba: An efficient unet-like mamba for semantic segmentation of high-resolution remote sensing images,

Reference 39

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Observation 4853a6e4-1f13-4f87-8f90-017741bbbee5 · outbound

This paper cites Cascadepsp: Toward class-agnostic and very high-resolution segmentation via global and local refinement,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Cascadepsp: Toward class-agnostic and very high-resolution segmentation via global and local refinement,

Reference 40

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Observation 6d4b2339-3fdd-4fe0-8909-e08fbfa0cdb2 · outbound

This paper cites Pointrend: Image seg- mentation as rendering,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Pointrend: Image seg- mentation as rendering,

Reference 41

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Observation ac4bac95-9002-42c4-8886-b14a200feefb · outbound

This paper cites Memory- constrained semantic segmentation for ultra-high resolution uav im- agery,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Memory- constrained semantic segmentation for ultra-high resolution uav im- agery,

Reference 42

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Observation 29206df6-78b1-419c-808d-05d21698b813 · outbound

This paper cites Ultra-high resolution segmen- tation with ultra-rich context: A novel benchmark,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Ultra-high resolution segmen- tation with ultra-rich context: A novel benchmark,

Reference 43

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Observation 3fb5dba2-6c1e-4888-9f5d-328d659fae84 · outbound

This paper cites Wave-vit: Unifying wavelet and transformers for visual representation learning,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Wave-vit: Unifying wavelet and transformers for visual representation learning,

Reference 44

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Observation 493c78f0-543c-416e-851c-56d6dbcb08bf · outbound

This paper cites Boosting the dual-stream architecture in ultra-high resolution segmentation with resolution-biased uncertainty estimation,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Boosting the dual-stream architecture in ultra-high resolution segmentation with resolution-biased uncertainty estimation,

Reference 45

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Observation a95d2ab9-8f1a-475d-b143-d5e9a5e79d87 · outbound

This paper cites Ultra-high resolution segmentation via boundary-enhanced patch-merging transformer,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Ultra-high resolution segmentation via boundary-enhanced patch-merging transformer,

Reference 46

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Observation 18345afb-2de1-4405-839d-0a3da87c9b89 · outbound

This paper cites Ultra-high resolution image segmentation via locality-aware context fusion and alternating local enhancement,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Ultra-high resolution image segmentation via locality-aware context fusion and alternating local enhancement,

Reference 47

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Observation 4b439476-8301-4662-b70d-3309f25cf136 · outbound

This paper cites Progressive semantic seg- mentation,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Progressive semantic seg- mentation,

Reference 48

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Observation 92b467d0-10ba-4746-83ea-4d8367d694c2 · outbound

This paper cites Looking outside the window: Wide-context transformer for the semantic segmentation of high-resolution remote sensing im- ages,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Looking outside the window: Wide-context transformer for the semantic segmentation of high-resolution remote sensing im- ages,

Reference 49

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Observation ae3b17fb-d7ac-47d3-a48c-4076d17f794f · outbound

This paper cites Deepglobe 2018: A challenge to parse the earth through satellite images,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Deepglobe 2018: A challenge to parse the earth through satellite images,

Reference 50

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This paper cites Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark,

Reference 51

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Observation d15db1f4-b5d5-40b0-9133-55f364e642d0 · outbound

This paper cites Deep high-resolution repre- sentation learning for human pose estimation,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Deep high-resolution repre- sentation learning for human pose estimation,

Reference 52

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Observation 7fd1f1e4-0ce6-45ed-9a70-e763224f0c93 · outbound

This paper cites Object-contextual representations for semantic segmentation,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Object-contextual representations for semantic segmentation,

Reference 53

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Observation 081dbfac-9cd1-4a95-a165-9c7a9522a7b0 · outbound

This paper cites Convnext v2: Co-designing and scaling convnets with masked autoencoders,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Convnext v2: Co-designing and scaling convnets with masked autoencoders,

Reference 54

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Observation 6b67de03-68fa-4a35-84b4-fe131e5a58b4 · outbound

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

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-06-29T23:14:02.051708Z

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Observation 0934801b-a9dd-4e79-99bf-9bf1c3b27532 · outbound

This paper cites Unified perceptual parsing for scene understanding,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Unified perceptual parsing for scene understanding,

Reference 56

Resolution: unresolved; no resolver link, observed 2026-06-29T23:05:38.112865Z

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Observation f872bedb-2ce9-440f-9e82-d97bcbd4610b · outbound

This paper cites Decoupled Weight Decay Regularization.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Decoupled Weight Decay Regularization

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-06-29T23:14:02.054487Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2d27a34a-36e9-40d0-b9fb-79e5e8948c1c · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Pytorch: An imperative style, high-performance deep learning library,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-06-29T23:05:38.112865Z

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Observation 38a294ce-f51c-47f8-bf28-07eacb4145bb · outbound

This paper cites Mmsegmentation: Openmmlab semantic segmentation toolbox and benchmark,.

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation Mmsegmentation: Openmmlab semantic segmentation toolbox and benchmark,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-06-29T23:05:38.112865Z

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