A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation | Paper Citation Record

Observation 0f6bb193-9d88-4d21-bdf3-e842584a22e7 · outbound

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

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Segnet: A deep convolutional encoder-decoder architecture for image segmentation

Reference 1

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This paper cites Vision transformers for remote sensing image classification.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Vision transformers for remote sensing image classification

Reference 2

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This paper cites Sen1floods11: A georeferenced dataset to train and test deep learning flood algorithms for sentinel-1.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Sen1floods11: A georeferenced dataset to train and test deep learning flood algorithms for sentinel-1

Reference 3

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This paper cites Crossvit: Cross-attention multi-scale vision transformer for image classification.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Crossvit: Cross-attention multi-scale vision transformer for image classification

Reference 4

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This paper cites Remote sensing image change detection with transformers.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Remote sensing image change detection with transformers

Reference 5

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This paper cites F2Net: A Frequency-Fused Network for Ultra-High Resolution Remote Sensing Segmentation.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation F2Net: A Frequency-Fused Network for Ultra-High Resolution Remote Sensing Segmentation

Reference 6

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This paper cites Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Reference 7

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This paper cites Deeplab: Semantic image segmentation with deep con- volutional nets, atrous convolution, and fully connected crfs.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Deeplab: Semantic image segmentation with deep con- volutional nets, atrous convolution, and fully connected crfs

Reference 8

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Observation 56034fb8-1ccb-4b67-af6f-71f95cc7bb16 · outbound

This paper cites Encoder-decoder with atrous separable convolution for semantic image segmentation.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Encoder-decoder with atrous separable convolution for semantic image segmentation

Reference 9

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Observation e2ce1d46-6d23-46b4-b36e-666a4d2fa009 · outbound

This paper cites Lanet: Local attention embed- ding to improve the semantic segmentation of remote sensing images.IEEE Transactions on Geoscience and Remote Sensing , 59(1):426–435, 2020.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Lanet: Local attention embed- ding to improve the semantic segmentation of remote sensing images.IEEE Transactions on Geoscience and Remote Sensing , 59(1):426–435, 2020

Reference 10

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This paper cites Adversarial shape learning for building extraction in vhr remote sensing images.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Adversarial shape learning for building extraction in vhr remote sensing images

Reference 11

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This paper cites Road ex- traction based on direction consistency segmentation.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Road ex- traction based on direction consistency segmentation

Reference 12

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This paper cites The Binary Quantized Neural Network for Dense Prediction via Specially Designed Upsampling and Attention.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation The Binary Quantized Neural Network for Dense Prediction via Specially Designed Upsampling and Attention

Reference 13

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 14

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This paper cites Transform dual-branch attention net: Efficient semantic seg- mentation of ultra-high-resolution remote sensing images.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Transform dual-branch attention net: Efficient semantic seg- mentation of ultra-high-resolution remote sensing images

Reference 15

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This paper cites Multiscale refinement network for water- body segmentation in high-resolution satellite imagery.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Multiscale refinement network for water- body segmentation in high-resolution satellite imagery

Reference 16

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This paper cites Deep residual learning for image recognition.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Deep residual learning for image recognition

Reference 17

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This paper cites Ldnet: Semantic segmentation of high- resolution images via learnable patch proposal and dynamic refinement.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Ldnet: Semantic segmentation of high- resolution images via learnable patch proposal and dynamic refinement

Reference 18

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This paper cites A method for stochastic optimization.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation A method for stochastic optimization

Reference 19

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This paper cites Global-local attention network for semantic segmentation in aerial images.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Global-local attention network for semantic segmentation in aerial images

Reference 20

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This paper cites Energy mini- mum regularization in continual learning.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Energy mini- mum regularization in continual learning

Reference 21

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This paper cites Fusing multitask mod- els by recursive least squares.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Fusing multitask mod- els by recursive least squares

Reference 22

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Observation e505fb61-1769-4c38-8eeb-06e7a92d1a72 · outbound

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

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Refinenet: Multi-path refinement networks for high-resolution semantic segmentation

Reference 23

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This paper cites Feature pyramid networks for object detection.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Feature pyramid networks for object detection

Reference 24

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This paper cites Focal loss for dense object detection.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Focal loss for dense object detection

Reference 25

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This paper cites Connecting image denoising and high-level vision tasks via deep learning.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Connecting image denoising and high-level vision tasks via deep learning

Reference 26

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This paper cites When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach

Reference 27

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

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Swin transformer: Hierarchical vision transformer using shifted windows

Reference 28

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This paper cites Llm- cot enhanced graph neural recommendation with harmonized group policy optimization.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Llm- cot enhanced graph neural recommendation with harmonized group policy optimization

Reference 29

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This paper cites Geogrambench: Benchmarking the geometric program reasoning in modern llms.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Geogrambench: Benchmarking the geometric program reasoning in modern llms

Reference 30

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This paper cites Dlnet: A dual-level network with self-and cross-attention for high-resolution remote sensing segmentation.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Dlnet: A dual-level network with self-and cross-attention for high-resolution remote sensing segmentation

Reference 31

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This paper cites Learning decon- volution network for semantic segmentation.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Learning decon- volution network for semantic segmentation

Reference 32

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This paper cites ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

Reference 33

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This paper cites Synthetic Lung X-ray Generation through Cross-Attention and Affinity Transformation.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Synthetic Lung X-ray Generation through Cross-Attention and Affinity Transformation

Reference 34

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This paper cites U-net: Convo- lutional networks for biomedical image segmentation.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation U-net: Convo- lutional networks for biomedical image segmentation

Reference 35

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This paper cites Incremental few shot se- mantic segmentation via class-agnostic mask proposal and language-driven classifier.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Incremental few shot se- mantic segmentation via class-agnostic mask proposal and language-driven classifier

Reference 36

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

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Uhrsnet: A semantic segmentation network specifically for ultra-high-resolution images

Reference 37

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This paper cites Decouple the high-frequency and low-frequency information of images for semantic segmentation.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Decouple the high-frequency and low-frequency information of images for semantic segmentation

Reference 38

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This paper cites Cognitive Memory in Large Language Models.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Cognitive Memory in Large Language Models

Reference 39

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This paper cites Densenet-based land cover classification network with deep fusion.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Densenet-based land cover classification network with deep fusion

Reference 40

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This paper cites Mbnet: A multi-resolution branch net- work for semantic segmentation of ultra-high resolution images.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Mbnet: A multi-resolution branch net- work for semantic segmentation of ultra-high resolution images

Reference 41

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This paper cites Class-incremental learning for semantic segmentation in aerial imagery via distillation in all aspects.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Class-incremental learning for semantic segmentation in aerial imagery via distillation in all aspects

Reference 42

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This paper cites Class-incremental semantic segmentation of aerial images via pixel-level feature generation and task-wise distillation.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Class-incremental semantic segmentation of aerial images via pixel-level feature generation and task-wise distillation

Reference 43

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This paper cites Boosting semantic segmentation of aerial images via decoupled and multilevel compaction and dispersion.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Boosting semantic segmentation of aerial images via decoupled and multilevel compaction and dispersion

Reference 44

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This paper cites Edge-guided and Class-balanced Active Learning for Semantic Segmentation of Aerial Images.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Edge-guided and Class-balanced Active Learning for Semantic Segmentation of Aerial Images

Reference 45

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This paper cites A data-related patch proposal for semantic segmentation of aerial images.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation A data-related patch proposal for semantic segmentation of aerial images

Reference 46

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Observation 92f1fc04-d9ad-49af-91da-fc945fb6eb0f · outbound

This paper cites Lifelong Learning and Selective Forgetting via Contrastive Strategy.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Lifelong Learning and Selective Forgetting via Contrastive Strategy

Reference 47

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This paper cites Organizing Background to Explore Latent Classes for Incremental Few-shot Semantic Segmentation.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Organizing Background to Explore Latent Classes for Incremental Few-shot Semantic Segmentation

Reference 48

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This paper cites GMM-Based Comprehensive Feature Extraction and Relative Distance Preservation For Few-Shot Cross-Modal Retrieval.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation GMM-Based Comprehensive Feature Extraction and Relative Distance Preservation For Few-Shot Cross-Modal Retrieval

Reference 49

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This paper cites Max-deeplab: End-to-end panoptic segmentation with mask trans- formers.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Max-deeplab: End-to-end panoptic segmentation with mask trans- formers

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:12.741820Z

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Observation 5a9c3554-057b-400d-88b3-8d892f011e03 · outbound

This paper cites Deep high-resolution representation learning for visual recognition.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Deep high-resolution representation learning for visual recognition

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:12.733874Z

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Observation 2028b8e9-5802-4cef-95b7-8c686fa041e4 · outbound

This paper cites Detect globally, refine locally: A novel approach to saliency detection.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Detect globally, refine locally: A novel approach to saliency detection

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:12.725655Z

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Observation 438a8432-45b0-4b39-bb28-488642f703b0 · outbound

This paper cites Non- local neural networks.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Non- local neural networks

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:12.716101Z

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Observation 670f3fbb-fc7c-4ba1-b92d-3f89a9c5455b · outbound

This paper cites Continual learning for image segmentation with dynamic query.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Continual learning for image segmentation with dynamic query

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:12.707385Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3cd3b27d-502a-4670-a4d9-a1a74f483eab · outbound

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

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Segformer: Simple and efficient design for semantic segmentation with transformers

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:12.698253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5da9a71f-b019-499b-aeee-179f85ab3b52 · outbound

This paper cites GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:10.857306Z

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Observation 858ddb43-31da-4e4b-9d60-887041229926 · outbound

This paper cites Flexdataset: Crafting annotated dataset gen- eration for diverse applications.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Flexdataset: Crafting annotated dataset gen- eration for diverse applications

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:12.689530Z

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Observation e39340ab-c186-477f-9a59-5fb753fe70ba · outbound

This paper cites Bisenet: Bilateral segmentation network for real-time seman- tic segmentation.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Bisenet: Bilateral segmentation network for real-time seman- tic segmentation

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:12.679363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 891dcca7-5cc9-48df-9346-b5201673927a · outbound

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

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Multi-Scale Context Aggregation by Dilated Convolutions

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:11.119246Z

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Observation 2fea1001-b408-4a98-963d-45ed2290c395 · outbound

This paper cites Context encoding for semantic segmentation.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Context encoding for semantic segmentation

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:12.669093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 67607682-e34d-4757-948a-8aaeaf92b720 · outbound

This paper cites Road extraction by deep residual u-net.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Road extraction by deep residual u-net

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:12.659914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5627d22d-9e9c-471c-91e9-bef54383b15c · outbound

This paper cites End-to-end remote sensing change detection of unregistered bi-temporal images for natural disasters.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation End-to-end remote sensing change detection of unregistered bi-temporal images for natural disasters

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:12.625625Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c8cf4d0a-1e66-49c2-b701-ac86b6975f46 · outbound

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

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Icnet for real-time semantic segmentation on high-resolution images

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:12.398276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d90c5507-8e1a-4305-9005-44e6a9057cd4 · outbound

This paper cites Cooperative connection transformer for remote sensing image captioning.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation Cooperative connection transformer for remote sensing image captioning

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:12.193036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation edf7276f-2e30-44ec-a024-aa98c6b1ed53 · outbound

This paper cites DynRsl-VLM: Enhancing Autonomous Driving Perception with Dynamic Resolution Vision-Language Models.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation DynRsl-VLM: Enhancing Autonomous Driving Perception with Dynamic Resolution Vision-Language Models

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:11.687547Z

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