DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network | Paper Citation Record

Observation c98a7212-2e22-44fa-8a1d-a573fce20a3b · outbound

This paper cites Graphcut textures: Image and video synthesis using graph 9 cuts[J].

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Graphcut textures: Image and video synthesis using graph 9 cuts[J]

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.823771Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8a4e9b9b-1b9a-4492-9af7-c16d962b8f06 · outbound

This paper cites Local spatiotemporal features for dynamic texture synthesis[J].

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Local spatiotemporal features for dynamic texture synthesis[J]

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.807147Z

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Observation 52861e81-5d34-43c9-8043-e552c5348265 · outbound

This paper cites An intelligent electronic lock for remote-control system based on the internet of things[C]//journal of physics: conference series.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network An intelligent electronic lock for remote-control system based on the internet of things[C]//journal of physics: conference series

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.790749Z

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Observation b20a5dc7-3ddc-4d6c-8f68-bd04f99fc651 · outbound

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

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 4

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This paper cites an unresolved cited work.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-11T06:00:00.773211Z

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Observation 77cd5d04-d8ad-4bea-8b5f-a6f9ea76cfaa · outbound

This paper cites Synthesising Dynamic Textures using Convolutional Neural Networks.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Synthesising Dynamic Textures using Convolutional Neural Networks

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-11T06:00:00.303245Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a2d59af6-c6f7-4a57-a4c2-addc7a4ee7fd · outbound

This paper cites Two-stream convolutional networks for dynamic texture synthesis[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Two-stream convolutional networks for dynamic texture synthesis[C]//Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.755846Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 096c5104-7034-4eda-a0a9-99517279191c · outbound

This paper cites Dynamic texture modeling and synthesis using multi-kernel Gaussian process dynamic model[J].

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Dynamic texture modeling and synthesis using multi-kernel Gaussian process dynamic model[J]

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.739741Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3c93139f-bef2-45a4-8a12-de36854601a5 · outbound

This paper cites Generating videos with scene dynamics[J].

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Generating videos with scene dynamics[J]

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.723053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8b2be5b2-a99f-410a-b037-dc71c125bda9 · outbound

This paper cites Improved YOLOv5 Based on Attention Mechanism and FasterNet for Foreign Object Detection on Railway and Airway tracks.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Improved YOLOv5 Based on Attention Mechanism and FasterNet for Foreign Object Detection on Railway and Airway tracks

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-11T06:00:00.279840Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f8dbf52e-3cd5-4b04-9d6f-4f7f5a03ab63 · outbound

This paper cites A Neural Matrix Decomposition Recommender System Model based on the Multimodal Large Language Model.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network A Neural Matrix Decomposition Recommender System Model based on the Multimodal Large Language Model

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T05:59:59.783678Z

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Observation e1696ae0-5a5d-4292-8f39-5a82193d00f4 · outbound

This paper cites Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatment.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatment

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-11T06:00:00.238416Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2a9634da-2a98-4e09-9416-8d925361ae7b · outbound

This paper cites Conditional generative ConvNets for exemplar-based texture synthesis[J].

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Conditional generative ConvNets for exemplar-based texture synthesis[J]

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.706016Z

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Observation 3ae060ed-a610-49ad-99aa-f1974e28f745 · outbound

This paper cites A Multimodal Fusion Network For Student Emotion Recognition Based on Transformer and Tensor Product.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network A Multimodal Fusion Network For Student Emotion Recognition Based on Transformer and Tensor Product

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-11T06:00:00.214852Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8d9554d3-5555-4fda-87a4-d7ef8c85487b · outbound

This paper cites Dynamic textures[C]//Proceedings Eighth IEEE International Conference on Computer Vision.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Dynamic textures[C]//Proceedings Eighth IEEE International Conference on Computer Vision

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.690308Z

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Observation 7ecc6943-9c31-4063-afae-17c6fc3bdd94 · outbound

This paper cites Improved Unet model for brain tumor image segmentation based on ASPP-coordinate attention mechanism.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Improved Unet model for brain tumor image segmentation based on ASPP-coordinate attention mechanism

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-11T06:00:00.191361Z

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Observation 1bbab63b-3d5f-497f-ac18-29394c68542f · outbound

This paper cites AI-based NLP section discusses the application and effect of bag-of- words models and TF-IDF in NLP tasks[J].

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network AI-based NLP section discusses the application and effect of bag-of- words models and TF-IDF in NLP tasks[J]

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.673471Z

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Observation 3a7f9f76-4696-4924-a71a-47d0af580662 · outbound

This paper cites Learning dynamic generator model by alternating back-propagation through time[C]//Proceedings of the AAAI Conference on Artificial Intelligence.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Learning dynamic generator model by alternating back-propagation through time[C]//Proceedings of the AAAI Conference on Artificial Intelligence

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.656918Z

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Observation 814dc0c7-5882-48d4-a633-0f05ec7c9e80 · outbound

This paper cites MPGAAN: Effective and Efficient Heterogeneous Information Network Classification[J].

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network MPGAAN: Effective and Efficient Heterogeneous Information Network Classification[J]

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.641887Z

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Observation 0ac8b15e-3b6a-4a39-a6d8-af435c4fe308 · outbound

This paper cites Improving text-image matching with adversarial learning and circle loss for multi-modal steganography[C]//International Workshop on Digital Watermarking.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Improving text-image matching with adversarial learning and circle loss for multi-modal steganography[C]//International Workshop on Digital Watermarking

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.625936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 49169f32-0e34-4bfa-bd3b-bf4196f61a53 · outbound

This paper cites InGAN: Capturing and Remapping the "DNA" of a Natural Image.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network InGAN: Capturing and Remapping the "DNA" of a Natural Image

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-11T06:00:00.168177Z

Source-reported events for the cited work

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Observation b0880363-89ff-45a3-a732-76da67d3e09a · outbound

This paper cites Singan: Learning a generative model from a single natural image[C]//Proceedings of the IEEE/CVF international conference on computer vision.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Singan: Learning a generative model from a single natural image[C]//Proceedings of the IEEE/CVF international conference on computer vision

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.610277Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9dc7ec80-620c-4123-82cd-d97276056e65 · outbound

This paper cites Stock price prediction based on hybrid CNN-LSTM model.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Stock price prediction based on hybrid CNN-LSTM model

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.594253Z

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Observation 2054125f-8e65-46dc-97af-9cc6015e2e34 · outbound

This paper cites an unresolved cited work.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-11T06:00:00.578623Z

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Observation e879efd8-9a17-499b-9016-82b417d8598d · outbound

This paper cites Image Shape Manipulation from a Single Augmented Training Sample.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Image Shape Manipulation from a Single Augmented Training Sample

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-11T06:00:00.145471Z

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Observation 752cdbb9-203e-4f01-81d3-f5bcf4a94402 · outbound

This paper cites The application of artificial intelligence technology in assembly techniques within the industrial sector[J].

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network The application of artificial intelligence technology in assembly techniques within the industrial sector[J]

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.562449Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6b9b7348-0d09-4bc8-8c1f-3e42bf24b94f · outbound

This paper cites Multi-modal steganography based on semantic 10 relevancy[C]//International Workshop on Digital Watermarking.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Multi-modal steganography based on semantic 10 relevancy[C]//International Workshop on Digital Watermarking

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.547148Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fb2340c2-111a-43fe-97fa-341835b228e3 · outbound

This paper cites Deep Learning with Improved Metaheuristic Optimization for Traffic Flow Prediction[J].

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Deep Learning with Improved Metaheuristic Optimization for Traffic Flow Prediction[J]

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.531658Z

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Observation 955a4908-85d3-4e6a-95a5-292b92d8e9c3 · outbound

This paper cites Visual dynamics: Probabilistic future frame synthesis via cross convolutional networks[J].

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Visual dynamics: Probabilistic future frame synthesis via cross convolutional networks[J]

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.516553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f5b15a30-1bb2-46b9-a984-d1997942cf39 · outbound

This paper cites Research on Autonomous Driving Decision-making Strategies based Deep Reinforcement Learning.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Research on Autonomous Driving Decision-making Strategies based Deep Reinforcement Learning

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-11T06:00:00.121107Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation caae889f-ffac-46f8-b609-bee8e89664b1 · outbound

This paper cites Temporal generative adversarial nets with singular value clipping[C]//Proceedings of the IEEE international conference on computer vision.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Temporal generative adversarial nets with singular value clipping[C]//Proceedings of the IEEE international conference on computer vision

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.501312Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 720017ec-055d-4365-a51f-6066387b254f · outbound

This paper cites an unresolved cited work.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-08-11T06:00:00.485236Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4aac59a6-5899-468b-af5c-53619d20b8b0 · outbound

This paper cites Mocogan: Decomposing motion and content for video generation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Mocogan: Decomposing motion and content for video generation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.468801Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c8c2f5d5-6ac5-443f-8d7b-e2d48d3930e3 · outbound

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

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Large Scale GAN Training for High Fidelity Natural Image Synthesis

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T05:59:59.901357Z

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Observation 29b114f0-ccca-4450-bd2c-d1a794a7e064 · outbound

This paper cites Adversarial Video Generation on Complex Datasets.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Adversarial Video Generation on Complex Datasets

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-11T05:59:59.906271Z

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Observation f96e5c00-3dd5-4b33-976d-b6bc8cd25879 · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T05:59:59.911511Z

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Observation db38c5bd-44d5-4740-9774-e500219ff25f · outbound

This paper cites Editable neural radiance fields convert 2D to 3D furniture texture[J].

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Editable neural radiance fields convert 2D to 3D furniture texture[J]

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.452626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 82eb08b8-7d4e-4fa1-a47d-251dd7891403 · outbound

This paper cites Improved training of wasserstein gans[J].

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Improved training of wasserstein gans[J]

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.436067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8a860694-8eec-403a-996d-31f88c3191a3 · outbound

This paper cites A new large scale dynamic texture dataset with application to convnet understanding[C]//Proceedings of the European Conference on Computer Vision (ECCV).

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network A new large scale dynamic texture dataset with application to convnet understanding[C]//Proceedings of the European Conference on Computer Vision (ECCV)

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.419985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b108626c-01b6-4f81-8eb6-677b334aa198 · outbound

This paper cites Multiscale structural similarity for image quality assessment[C]//The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Multiscale structural similarity for image quality assessment[C]//The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.400127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d01a8771-aa64-48e0-a41f-f9dba51970e7 · outbound

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

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Gans trained by a two time-scale update rule converge to a local nash equilibrium[J]

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.382897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6fe98096-3ec3-4d06-8ead-357d0dc2405f · outbound

This paper cites Continuous and diverse image-to-image translation via signed attribute vectors[J].

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Continuous and diverse image-to-image translation via signed attribute vectors[J]

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.367008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 87c66a16-8ed6-452a-93e6-78f0f35788f9 · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network The unreasonable effectiveness of deep features as a perceptual metric[C]//Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.350919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a14f93a1-4218-4bae-94d3-b214690e76c7 · outbound

This paper cites The application of Augmented Reality (AR) in Remote Work and Education.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network The application of Augmented Reality (AR) in Remote Work and Education

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-08-11T06:00:00.040807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f6ab3213-2c49-4495-bd05-181597baf6af · outbound

This paper cites Utilizing Deep Learning to Optimize Software Development Processes.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Utilizing Deep Learning to Optimize Software Development Processes

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-11T05:59:59.955631Z

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Observation 4b62bcf9-6304-4c46-854c-b38b4e8ca07c · outbound

This paper cites Task allocation planning based on hierarchical task network for national economic mobilization[J].

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Task allocation planning based on hierarchical task network for national economic mobilization[J]

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:00.335174Z

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