Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding | Paper Citation Record

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This paper cites Siedob: Semantic image editing by disentangling object and background,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Siedob: Semantic image editing by disentangling object and background,

Reference 1

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This paper cites Editgan: High-precision semantic image editing,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Editgan: High-precision semantic image editing,

Reference 2

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Observation 01e437a9-6c20-42f5-87d1-c0b1586ab0a1 · outbound

This paper cites Sesame: Semantic editing of scenes by adding, manipulating or erasing objects,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Sesame: Semantic editing of scenes by adding, manipulating or erasing objects,

Reference 3

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Observation 3e95332e-09c3-44ec-b973-8b9f7c93e322 · outbound

This paper cites Incremental learning for semantic segmentation of large-scale remote sensing data,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Incremental learning for semantic segmentation of large-scale remote sensing data,

Reference 4

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This paper cites A survey on continual semantic segmentation: Theory, challenge, method and application,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding A survey on continual semantic segmentation: Theory, challenge, method and application,

Reference 6

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This paper cites Birds of a feather flock together: Category-divergence guidance for domain adaptive seg- mentation,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Birds of a feather flock together: Category-divergence guidance for domain adaptive seg- mentation,

Reference 7

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This paper cites Inherit with distillation and evolve with contrast: Exploring class incremental semantic segmentation without exemplar memory,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Inherit with distillation and evolve with contrast: Exploring class incremental semantic segmentation without exemplar memory,

Reference 8

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Observation 71c97722-0662-4bc6-ab3a-690966779d3b · outbound

This paper cites Learning at a glance: Towards in- terpretable data-limited continual semantic segmentation via semantic- invariance modelling,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Learning at a glance: Towards in- terpretable data-limited continual semantic segmentation via semantic- invariance modelling,

Reference 9

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This paper cites Low light video enhancement using synthetic data produced with an intermediate domain mapping,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Low light video enhancement using synthetic data produced with an intermediate domain mapping,

Reference 10

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This paper cites Deep learning for image enhancement and correction in magnetic resonance imaging—state-of-the-art and challenges,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Deep learning for image enhancement and correction in magnetic resonance imaging—state-of-the-art and challenges,

Reference 11

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Observation 3e1a8246-0590-4fdc-b6a8-00e2d5c1dc57 · outbound

This paper cites Fice: Text-conditioned fashion-image editing with guided gan inversion,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Fice: Text-conditioned fashion-image editing with guided gan inversion,

Reference 12

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Observation c73bfdea-fd10-4de6-971e-7d6817e5deb2 · outbound

This paper cites Semantic image synthesis with spatially-adaptive normalization,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Semantic image synthesis with spatially-adaptive normalization,

Reference 13

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Observation 5a896c2b-910a-4ad4-a004-6f5c1c2907c0 · outbound

This paper cites Semantic probability distribution modeling for diverse semantic image synthesis,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Semantic probability distribution modeling for diverse semantic image synthesis,

Reference 14

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This paper cites Semantic image synthesis via adversarial learning,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Semantic image synthesis via adversarial learning,

Reference 15

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Observation baf7c52e-8d0e-47cf-af37-c3b364a6e417 · outbound

This paper cites High-resolution image synthesis and semantic manipulation with condi- tional gans,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding High-resolution image synthesis and semantic manipulation with condi- tional gans,

Reference 16

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Observation 118b968b-ef51-46b7-be80-53260a57dd7c · outbound

This paper cites Sean: Image synthesis with semantic region-adaptive normalization,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Sean: Image synthesis with semantic region-adaptive normalization,

Reference 17

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This paper cites Auto-Encoding Variational Bayes.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Auto-Encoding Variational Bayes

Reference 18

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This paper cites Image-to-image translation with conditional adversarial networks,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Image-to-image translation with conditional adversarial networks,

Reference 19

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This paper cites Local class-specific and global image-level generative adversarial networks for semantic- guided scene generation,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Local class-specific and global image-level generative adversarial networks for semantic- guided scene generation,

Reference 20

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This paper cites Efficient semantic image synthesis via class-adaptive normalization,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Efficient semantic image synthesis via class-adaptive normalization,

Reference 21

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This paper cites Oasis: only adversarial supervision for semantic image synthesis,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Oasis: only adversarial supervision for semantic image synthesis,

Reference 22

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This paper cites Edge guided gans with multi-scale contrastive learning for semantic image synthesis,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Edge guided gans with multi-scale contrastive learning for semantic image synthesis,

Reference 23

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This paper cites Retrieval-based spatially adaptive normalization for semantic image synthesis,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Retrieval-based spatially adaptive normalization for semantic image synthesis,

Reference 24

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This paper cites Place: Adaptive layout- semantic fusion for semantic image synthesis,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Place: Adaptive layout- semantic fusion for semantic image synthesis,

Reference 25

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This paper cites Freestyle layout- to-image synthesis,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Freestyle layout- to-image synthesis,

Reference 26

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Observation 481254c6-581b-48b6-ba72-8d925b8aeae9 · outbound

This paper cites Remote sensing image synthesis via semantic embedding generative adversarial networks,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Remote sensing image synthesis via semantic embedding generative adversarial networks,

Reference 27

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This paper cites Semantic-shape adaptive feature modulation for semantic image synthesis,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Semantic-shape adaptive feature modulation for semantic image synthesis,

Reference 28

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This paper cites Semantically multi-modal image synthesis,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Semantically multi-modal image synthesis,

Reference 29

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This paper cites Spatially-adaptive pixelwise networks for fast image translation,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Spatially-adaptive pixelwise networks for fast image translation,

Reference 30

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Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Spatially constrained gan for face and fashion synthesis,

Reference 31

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This paper cites Local class-specific and global image-level generative adversarial networks for semantic- guided scene generation,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Local class-specific and global image-level generative adversarial networks for semantic- guided scene generation,

Reference 32

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This paper cites Step: Style-based encoder pre-training for multi-modal image synthesis,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Step: Style-based encoder pre-training for multi-modal image synthesis,

Reference 33

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This paper cites Local class-specific and global image-level generative adversarial networks for semantic- guided scene generation,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Local class-specific and global image-level generative adversarial networks for semantic- guided scene generation,

Reference 34

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This paper cites Image synthesis via semantic composition,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Image synthesis via semantic composition,

Reference 35

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This paper cites Learning local image descriptors,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Learning local image descriptors,

Reference 36

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This paper cites Local feature descriptor for image matching: A survey,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Local feature descriptor for image matching: A survey,

Reference 37

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Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding An efficient image descriptor for image classification and cbir,

Reference 38

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Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Deep residual learning for image recognition,

Reference 39

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Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Xception: Deep learning with depthwise separable convolu- tions,

Reference 40

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This paper cites Rotate to attend: Convolutional triplet attention module,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Rotate to attend: Convolutional triplet attention module,

Reference 41

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This paper cites Scconv: Spatial and channel reconstruction convolution for feature redundancy,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Scconv: Spatial and channel reconstruction convolution for feature redundancy,

Reference 42

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This paper cites You Only Need Adversarial Supervision for Semantic Image Synthesis.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding You Only Need Adversarial Supervision for Semantic Image Synthesis

Reference 43

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This paper cites Large Scale GAN Training for High Fidelity Natural Image Synthesis.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Large Scale GAN Training for High Fidelity Natural Image Synthesis

Reference 44

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This paper cites Conditional Generative Adversarial Nets.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Conditional Generative Adversarial Nets

Reference 45

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This paper cites Self-attention generative adversarial networks,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Self-attention generative adversarial networks,

Reference 46

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Observation 591fd584-aad5-4b39-b2c2-4c54e0a026d7 · outbound

This paper cites Spectral Normalization for Generative Adversarial Networks.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Spectral Normalization for Generative Adversarial Networks

Reference 47

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

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 48

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This paper cites Unpaired image-to-image translation with shortest path regularization,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Unpaired image-to-image translation with shortest path regularization,

Reference 49

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Observation e1e64839-980a-43c5-a6c5-08431b716530 · outbound

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

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Land-cover classification with high-resolution remote sensing images using transferable deep models,

Reference 50

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Observation 878bc95b-8a9e-4d15-bb8c-284e3121ed8b · outbound

This paper cites Isprs semantic labeling contest,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Isprs semantic labeling contest,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:58:40.233497Z

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Observation efb836d0-b0de-4fd0-aaf6-54ba635f4d2c · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Adam: A Method for Stochastic Optimization

Reference 52

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Observation e1744b94-11f7-49e7-b151-47bcac5c6e07 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Gans trained by a two time-scale update rule converge to a local nash equilibrium,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:58:40.209930Z

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Observation 9b231155-542a-436a-9eea-9b8b3a65b5f9 · outbound

This paper cites Inception-v3 for flower classification,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Inception-v3 for flower classification,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:58:40.114840Z

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Observation 5def34cb-39b2-477b-8490-046065dcf310 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Gans trained by a two time-scale update rule converge to a local nash equilibrium,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:58:40.099312Z

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Observation 90704f69-8e8e-4fa6-96d9-dbabed497eb7 · outbound

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

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding Encoder- decoder with atrous separable convolution for semantic image segmen- tation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:58:40.025546Z

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Observation 16b6fe66-99a8-44d0-9c2c-e4bb7e367042 · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric,.

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding The unreasonable effectiveness of deep features as a perceptual metric,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:58:39.950231Z

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