Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering | Paper Citation Record

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This paper cites Memory Aware Synapses: Learning what (not) to forget.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Memory Aware Synapses: Learning what (not) to forget

Reference 1

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This paper cites Minimal topology for a radial basis functions neural network for pattern classification.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Minimal topology for a radial basis functions neural network for pattern classification

Reference 2

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This paper cites Bors and Nikolaos Nasios.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Bors and Nikolaos Nasios

Reference 3

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This paper cites Deep cluster- ing for unsupervised learning of visual features.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Deep cluster- ing for unsupervised learning of visual features

Reference 4

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This paper cites Modeling the background for incremental learning in semantic segmentation.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Modeling the background for incremental learning in semantic segmentation

Reference 5

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This paper cites Cucl: Codebook for unsupervised continual learning.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Cucl: Codebook for unsupervised continual learning

Reference 6

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This paper cites Mean shift, mode seeking, and clustering.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Mean shift, mode seeking, and clustering

Reference 7

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This paper cites Mean shift: a robust approach toward feature space analysis.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Mean shift: a robust approach toward feature space analysis

Reference 8

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This paper cites Unsupervised domain adaptation for video transformers in action recognition.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Unsupervised domain adaptation for video transformers in action recognition

Reference 9

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This paper cites PODNet: Pooled outputs distillation for small-tasks incremental learning.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering PODNet: Pooled outputs distillation for small-tasks incremental learning

Reference 10

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Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering something something

Reference 11

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Observation e03ebf39-99f9-49c6-9f6e-33490d1a0fcd · outbound

This paper cites Dual alignment un- supervised domain adaptation for video-text retrieval.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Dual alignment un- supervised domain adaptation for video-text retrieval

Reference 12

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Observation c4b2f060-00da-4de8-a22d-dda26c543ca9 · outbound

This paper cites Unsupervised continual learning via pseudo la- bels.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Unsupervised continual learning via pseudo la- bels

Reference 13

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This paper cites Deep residual learning for image recognition.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Deep residual learning for image recognition

Reference 14

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This paper cites Compacting, picking and growing for unforgetting continual learning.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Compacting, picking and growing for unforgetting continual learning

Reference 15

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This paper cites The Kinetics Human Action Video Dataset.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering The Kinetics Human Action Video Dataset

Reference 16

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This paper cites Adam: A Method for Stochastic Optimization.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Adam: A Method for Stochastic Optimization

Reference 17

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This paper cites Overcoming catastrophic forgetting in neural networks.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Overcoming catastrophic forgetting in neural networks

Reference 18

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This paper cites Trust-region adaptive frequency for online continual learning.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Trust-region adaptive frequency for online continual learning

Reference 19

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This paper cites Hmdb: a large video database for human motion recognition.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Hmdb: a large video database for human motion recognition

Reference 20

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This paper cites The hungarian method for the assignment problem.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering The hungarian method for the assignment problem

Reference 21

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Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Unresolved cited work

Reference 22

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This paper cites Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation

Reference 23

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This paper cites Prototype-guided continual adaptation for class-incremental unsuper- vised domain adaptation.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Prototype-guided continual adaptation for class-incremental unsuper- vised domain adaptation

Reference 24

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This paper cites Focal loss for dense object detection.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Focal loss for dense object detection

Reference 25

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This paper cites Gradient episodic memory for continual learning.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Gradient episodic memory for continual learning

Reference 26

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This paper cites Class in- cremental learning for video action classification.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Class in- cremental learning for video action classification

Reference 27

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This paper cites Visualizing data using t-sne.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Visualizing data using t-sne

Reference 28

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This paper cites Representational Continuity for Unsupervised Continual Learning.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Representational Continuity for Unsupervised Continual Learning

Reference 29

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This paper cites Class-incremental learning on video-based action recognition by distillation of various knowledge.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Class-incremental learning on video-based action recognition by distillation of various knowledge

Reference 30

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This paper cites Class-incremental learning: survey and performance evalua- tion on image classification.IEEE Trans.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Class-incremental learning: survey and performance evalua- tion on image classification.IEEE Trans

Reference 31

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This paper cites Class-incremental learning for action recognition in videos.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Class-incremental learning for action recognition in videos

Reference 32

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This paper cites Pytorch: An imperative style, high-performance deep learning library.Advances in neural infor- mation processing systems, 32, 2019.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Pytorch: An imperative style, high-performance deep learning library.Advances in neural infor- mation processing systems, 32, 2019

Reference 33

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This paper cites Pedregosa, G.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Pedregosa, G

Reference 34

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This paper cites Learning a condensed frame for memory-efficient video class-incremental learning.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Learning a condensed frame for memory-efficient video class-incremental learning

Reference 35

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This paper cites Space-time prompting for video class-incremental learning.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Space-time prompting for video class-incremental learning

Reference 36

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This paper cites Continual unsupervised representation learning.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Continual unsupervised representation learning

Reference 37

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This paper cites Lam- pert.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Lam- pert

Reference 38

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This paper cites Unsupervised video domain adaptation with masked pre-training and col- laborative self-training.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Unsupervised video domain adaptation with masked pre-training and col- laborative self-training

Reference 39

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This paper cites Continual learning with deep generative replay.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Continual learning with deep generative replay

Reference 40

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This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 41

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This paper cites Unsu- pervised continual learning for gradually varying domains.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Unsu- pervised continual learning for gradually varying domains

Reference 42

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This paper cites Scan: Learning to classify images without labels.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Scan: Learning to classify images without labels

Reference 43

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This paper cites PIVOT: prompting for video continual learning.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering PIVOT: prompting for video continual learning

Reference 44

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This paper cites vCLIMB: A novel video class incremental learning benchmark.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering vCLIMB: A novel video class incremental learning benchmark

Reference 45

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This paper cites Temporal segment networks for action recognition in videos.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Temporal segment networks for action recognition in videos

Reference 46

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This paper cites Videomae v2: Scaling video masked autoencoders with dual masking.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Videomae v2: Scaling video masked autoencoders with dual masking

Reference 47

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This paper cites Learning to prompt for contin- ual learning.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Learning to prompt for contin- ual learning

Reference 48

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This paper cites Class-incremental unsupervised domain adaptation via pseudo-label distillation.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Class-incremental unsupervised domain adaptation via pseudo-label distillation

Reference 49

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This paper cites Class-incremental learning with strong pre-trained models.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Class-incremental learning with strong pre-trained models

Reference 50

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Observation 39eda1b9-eea1-4f50-adc2-96945a2fbb1c · outbound

This paper cites Large scale incremental learning.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Large scale incremental learning

Reference 51

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Observation c86b62e7-1c8e-4047-bf3f-509275fcf82a · outbound

This paper cites Unsupervised feature learn- ing via non-parametric instance discrimination.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Unsupervised feature learn- ing via non-parametric instance discrimination

Reference 52

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Observation be4d1e1e-5757-4c65-b44f-8d1f9ad5e69e · outbound

This paper cites Unsupervised deep embedding for clus- tering analysis.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Unsupervised deep embedding for clus- tering analysis

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:03:55.119486Z

Source-reported events for the cited work

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Observation 9d8c5dfe-b5f1-419f-aea7-c97f6ee429fe · outbound

This paper cites an unresolved cited work.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:03:54.936212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a0345461-b9f8-4cf7-97eb-0f0febe9926f · outbound

This paper cites an unresolved cited work.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:03:54.740877Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 495a5ea2-4d19-4716-a225-5a58f29a056b · outbound

This paper cites Unsupervised learning from video with deep neural embeddings.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering Unsupervised learning from video with deep neural embeddings

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:03:54.569834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 90b51337-766b-4a62-bfd4-e18710013f85 · outbound

This paper cites The temporal data augmentation, as proposed in [46], is also applied.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering The temporal data augmentation, as proposed in [46], is also applied

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:03:54.423103Z

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Observation cbcfd82e-9d08-44d5-92a3-095a09cffacd · outbound

This paper cites KURPUKDEE AND BORS: UNSUPERVISED VIDEO CONTINUAL LEARNING 13.

Unsupervised Video Continual Learning via Non-Parametric Deep Embedded Clustering KURPUKDEE AND BORS: UNSUPERVISED VIDEO CONTINUAL LEARNING 13

Reference 368

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:03:59.804166Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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