Supervised Contrastive Learning for Ordinal Engagement Measurement | Paper Citation Record

Observation f707d09b-c0b8-4f64-a4d9-143a0ef3a76b · outbound

This paper cites Covid-19 pandemic–online education in the new normal and the next normal,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Covid-19 pandemic–online education in the new normal and the next normal,

Reference 1

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Observation 82603aa7-b16b-49ac-b737-b2de7ab456a6 · outbound

This paper cites Perceptions and behaviors of learner engagement with virtual educational platforms,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Perceptions and behaviors of learner engagement with virtual educational platforms,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:56.353038Z

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Observation 5639f126-3bcf-432c-b689-6bd4a751c6c9 · outbound

This paper cites Engagement in online learning: student attitudes and behavior during covid-19,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Engagement in online learning: student attitudes and behavior during covid-19,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:56.191942Z

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Observation fb7c8c60-d51f-4c00-8a03-945a2503ef12 · outbound

This paper cites Student engagement, academic self-efficacy, and academic motivation as predictors of academic performance,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Student engagement, academic self-efficacy, and academic motivation as predictors of academic performance,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:55.987276Z

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Observation 80c3b27d-0655-4570-90cd-148ca22a493c · outbound

This paper cites Automatic prediction of presentation style and student engagement from videos,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Automatic prediction of presentation style and student engagement from videos,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:55.818493Z

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Observation 41d63282-b3a0-418d-a937-ff30a9720c01 · outbound

This paper cites Inconsistencies in measuring student engagement in virtual learning-a critical review,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Inconsistencies in measuring student engagement in virtual learning-a critical review,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:55.658957Z

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Observation c0871732-43f9-4864-9b50-2aec32fd1130 · outbound

This paper cites Automatic engagement estimation in smart education/learning settings: a systematic review of engage- ment definitions, datasets, and methods,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Automatic engagement estimation in smart education/learning settings: a systematic review of engage- ment definitions, datasets, and methods,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:55.428807Z

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Observation 9d6c7585-89a5-4934-b50c-a20b8c0e8a0a · outbound

This paper cites The challenges of defining and measuring student engagement in science,.

Supervised Contrastive Learning for Ordinal Engagement Measurement The challenges of defining and measuring student engagement in science,

Reference 8

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Observation 9c0e8325-7a52-4445-bc7b-7b605ea5254a · outbound

This paper cites Advanced, analytic, auto- mated (aaa) measurement of engagement during learning,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Advanced, analytic, auto- mated (aaa) measurement of engagement during learning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:55.232816Z

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Observation 2f77d325-5539-4bb0-8ff7-a157cf9231bb · outbound

This paper cites Improving state-of-the-art in detecting student engagement with resnet and tcn hybrid network,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Improving state-of-the-art in detecting student engagement with resnet and tcn hybrid network,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:55.060788Z

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Observation b7b98afc-0d8e-4347-8445-f70a92fd4454 · outbound

This paper cites Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:54.829766Z

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Observation 8113bdbf-27db-42a0-b975-a7380d1bf3f4 · outbound

This paper cites Affect-driven ordinal engagement measure- ment from videos,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Affect-driven ordinal engagement measure- ment from videos,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:54.620232Z

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Observation cc9077bd-e930-4b4c-867c-eba4c717ad02 · outbound

This paper cites Tclr: Temporal con- trastive learning for video representation,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Tclr: Temporal con- trastive learning for video representation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:54.458905Z

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Observation 83965dba-99b6-4755-a608-0c49877eb549 · outbound

This paper cites Deep learning based engagement recognition in highly imbalanced data,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Deep learning based engagement recognition in highly imbalanced data,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:54.222483Z

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Observation 9fecb6be-8ea4-4ec9-8afc-6fd498b5b7ab · outbound

This paper cites Deep facial spatiotemporal network for engagement prediction in online learning,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Deep facial spatiotemporal network for engagement prediction in online learning,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:54.037486Z

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Observation 6272eec6-6a2a-4691-8326-1ba220db650f · outbound

This paper cites Class-attention Video Transformer for Engagement Intensity Prediction.

Supervised Contrastive Learning for Ordinal Engagement Measurement Class-attention Video Transformer for Engagement Intensity Prediction

Reference 16

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Observation 3a382c54-799b-4a1d-b863-e95cff580b65 · outbound

This paper cites Fine-grained engagement recognition in online learning environment,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Fine-grained engagement recognition in online learning environment,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:53.851379Z

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Observation 1872cf92-1a54-45fe-9e5f-8a386b91aff7 · outbound

This paper cites The faces of engagement: Automatic recognition of student engagement from facial expressions,.

Supervised Contrastive Learning for Ordinal Engagement Measurement The faces of engagement: Automatic recognition of student engagement from facial expressions,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:53.664219Z

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Observation c95b0a7b-2667-4dcc-97dd-f635381d2337 · outbound

This paper cites Toward active and unobtrusive engagement assessment of distance learners,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Toward active and unobtrusive engagement assessment of distance learners,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:53.495080Z

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Observation 5edcad15-028e-4fd9-9f2c-91b1dc61f8fb · outbound

This paper cites Prediction and lo- calization of student engagement in the wild,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Prediction and lo- calization of student engagement in the wild,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:53.312218Z

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Observation 72c9571b-c035-440c-816a-0b1cd98581b5 · outbound

This paper cites Automatic engagement prediction with gap feature,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Automatic engagement prediction with gap feature,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:53.126506Z

Source-reported events for the cited work

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Observation 159d185a-5083-41c0-97cb-6a8dc34ccfa0 · outbound

This paper cites Multimodal approach to engagement and disengagement detection with highly imbalanced in-the-wild data,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Multimodal approach to engagement and disengagement detection with highly imbalanced in-the-wild data,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:52.986680Z

Source-reported events for the cited work

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Observation 417f3597-4cc2-4dc8-aa96-ffeeb3404235 · outbound

This paper cites Predicting engagement intensity in the wild using temporal convolutional network,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Predicting engagement intensity in the wild using temporal convolutional network,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:52.815687Z

Source-reported events for the cited work

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Observation 6c03429b-9df4-4675-9cc1-66f8eee36d13 · outbound

This paper cites Faceen- gage: robust estimation of gameplay engagement from user-contributed (youtube) videos,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Faceen- gage: robust estimation of gameplay engagement from user-contributed (youtube) videos,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:52.661574Z

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Observation 13c5f8db-9d01-497a-ae39-1e5a5ce5d423 · outbound

This paper cites Advanced multi-instance learning method with multi-features engineering and conservative opti- mization for engagement intensity prediction,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Advanced multi-instance learning method with multi-features engineering and conservative opti- mization for engagement intensity prediction,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:52.454959Z

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Observation 80ec9b56-cc99-4d6f-88ab-4f6a41a84d81 · outbound

This paper cites Automatic student engagement in online learning environment based on neural turing machine,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Automatic student engagement in online learning environment based on neural turing machine,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:52.270758Z

Source-reported events for the cited work

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Observation 08622ea1-fa67-41a8-8748-fe2c52b48162 · outbound

This paper cites Engagement detection with multi-task training in e-learning environments,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Engagement detection with multi-task training in e-learning environments,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:52.053408Z

Source-reported events for the cited work

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Observation 76e89753-4dc5-4366-a95c-e00e7ab02568 · outbound

This paper cites Automatic student engagement measurement using machine learning techniques: A literature study of data and methods,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Automatic student engagement measurement using machine learning techniques: A literature study of data and methods,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:51.849853Z

Source-reported events for the cited work

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Observation ae94ee46-5ff6-409f-bd8d-d7d955e0d04e · outbound

This paper cites A simple framework for contrastive learning of visual representations,.

Supervised Contrastive Learning for Ordinal Engagement Measurement A simple framework for contrastive learning of visual representations,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:51.648572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 438e66df-9a1f-4717-83e9-9bf125c1be5d · outbound

This paper cites Supervised contrastive learning for detecting anomalous driving behaviours from multimodal videos,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Supervised contrastive learning for detecting anomalous driving behaviours from multimodal videos,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:51.425049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e24f2c18-42ae-4db8-a918-932c723ad8e6 · outbound

This paper cites Supervised Contrastive Learning.

Supervised Contrastive Learning for Ordinal Engagement Measurement Supervised Contrastive Learning

Reference 31

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Source-reported events for the cited work

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Observation 3f62318f-7524-4436-9125-ecf323ec88d5 · outbound

This paper cites Time series contrastive learning with information-aware augmentations,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Time series contrastive learning with information-aware augmentations,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:51.228199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8011e5fa-304e-4e18-8fd4-b899c411909b · outbound

This paper cites Rank-N-Contrast: Learning Continuous Representations for Regression.

Supervised Contrastive Learning for Ordinal Engagement Measurement Rank-N-Contrast: Learning Continuous Representations for Regression

Reference 33

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Observation 5cad6be6-d70d-4745-8a86-76b481f9d4a3 · outbound

This paper cites Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification.

Supervised Contrastive Learning for Ordinal Engagement Measurement Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:41.269547Z

Source-reported events for the cited work

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Observation bfebb228-fb76-487d-be10-933f798833e1 · outbound

This paper cites Improving contrastive learning on imbalanced data via open-world sampling,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Improving contrastive learning on imbalanced data via open-world sampling,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:51.039919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8f7542e0-74fb-41bf-86d6-716cce68ece8 · outbound

This paper cites A circumplex model of affect.

Supervised Contrastive Learning for Ordinal Engagement Measurement A circumplex model of affect

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:41.497804Z

Source-reported events for the cited work

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Observation 307a0872-325b-41a9-8f97-17ee5e60d32e · outbound

This paper cites Engagement detection in online learning: a review,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Engagement detection in online learning: a review,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:41.674694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f6177fd-3758-4977-aad6-f09088e4ce4c · outbound

This paper cites An empirical survey of data augmentation for time series classification with neural networks,.

Supervised Contrastive Learning for Ordinal Engagement Measurement An empirical survey of data augmentation for time series classification with neural networks,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:50.878560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5fd5c35f-9645-4e79-b3f4-6ca787370e58 · outbound

This paper cites A simple approach to ordinal classification,.

Supervised Contrastive Learning for Ordinal Engagement Measurement A simple approach to ordinal classification,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:50.701674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 88c28f91-e285-4304-9b92-6484b0aed348 · outbound

This paper cites DAiSEE: Towards User Engagement Recognition in the Wild.

Supervised Contrastive Learning for Ordinal Engagement Measurement DAiSEE: Towards User Engagement Recognition in the Wild

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:42.053796Z

Source-reported events for the cited work

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Observation 1d1525e1-abd3-4180-8c65-2c02cb94cb75 · outbound

This paper cites Learning deep spatiotemporal feature for engagement recognition of online courses,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Learning deep spatiotemporal feature for engagement recognition of online courses,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:50.545131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 127f5ddf-aff7-45fd-b039-890b3e09bddf · outbound

This paper cites An novel end-toend network for automatic student engagement recognition,.

Supervised Contrastive Learning for Ordinal Engagement Measurement An novel end-toend network for automatic student engagement recognition,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:50.381639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0e786453-a5d9-4771-bce7-39458633937a · outbound

This paper cites An optimized cnn model for engagement recognition in an e-learning environment,.

Supervised Contrastive Learning for Ordinal Engagement Measurement An optimized cnn model for engagement recognition in an e-learning environment,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:50.230652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c8b22267-0993-4f7c-85cc-c5732877eabd · outbound

This paper cites Threedimen- sional densenet self-attention neural network for automatic detection of student’s engagement,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Threedimen- sional densenet self-attention neural network for automatic detection of student’s engagement,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:50.072149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e763972d-7e28-496d-aae8-2c10a099467a · outbound

This paper cites Students engagement level detection in online e-learning using hybrid efficientnetb7 together with tcn, lstm, and bi-lstm,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Students engagement level detection in online e-learning using hybrid efficientnetb7 together with tcn, lstm, and bi-lstm,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:49.878041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0076a47a-da71-4972-a4da-169d51a33eac · outbound

This paper cites Do I Have Your Attention: A Large Scale Engagement Prediction Dataset and Baselines.

Supervised Contrastive Learning for Ordinal Engagement Measurement Do I Have Your Attention: A Large Scale Engagement Prediction Dataset and Baselines

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:53:46.629125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 09ea5bd5-2c58-4f1d-b3f5-ea0b7e331432 · outbound

This paper cites Recognition of student engagement and affective states using convnextlarge and ensemble gru in e-learning,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Recognition of student engagement and affective states using convnextlarge and ensemble gru in e-learning,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:49.708427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f60a91f8-bfdb-49aa-b90a-e46f6b462031 · outbound

This paper cites Multimodal graph learning based on 3d haar semi-tight framelet for student engagement prediction,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Multimodal graph learning based on 3d haar semi-tight framelet for student engagement prediction,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:49.551628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a8be962a-0657-49f1-8cca-d6d990cb6e70 · outbound

This paper cites Re-distributing facial features for engagement prediction with moderntcn,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Re-distributing facial features for engagement prediction with moderntcn,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:49.380318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 85a12668-4209-44e5-9005-cb16032c644d · outbound

This paper cites Msc-trans: A multi-feature-fusion network with encoding structure for student engagement detection,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Msc-trans: A multi-feature-fusion network with encoding structure for student engagement detection,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:49.221388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0811a5f5-ba1c-49ec-8d47-546e7fd0e426 · outbound

This paper cites Detection of student engagement in e-learning environments using efficientnetv2- l together with rnn-based models,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Detection of student engagement in e-learning environments using efficientnetv2- l together with rnn-based models,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:49.064629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e573eecf-922b-40bb-b3fa-1dd74e5a99b4 · outbound

This paper cites Enhancing frame-level student engagement clas- sification through knowledge transfer techniques,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Enhancing frame-level student engagement clas- sification through knowledge transfer techniques,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:48.862192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7675c534-43cd-4a24-a7df-4dc3ad45f575 · outbound

This paper cites A self- supervised learning network for student engagement recognition from facial expressions,.

Supervised Contrastive Learning for Ordinal Engagement Measurement A self- supervised learning network for student engagement recognition from facial expressions,

Reference 53

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Observation 0dcfd781-8c82-4881-bf0b-2f455b61c49d · outbound

This paper cites Engagement measurement based on facial landmarks and spatial-temporal graph convolutional networks,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Engagement measurement based on facial landmarks and spatial-temporal graph convolutional networks,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:48.536794Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 76b9fa77-19ec-41f0-a87f-ad2534c05d27 · outbound

This paper cites Bag of States: A Non-sequential Approach to Video-based Engagement Measurement.

Supervised Contrastive Learning for Ordinal Engagement Measurement Bag of States: A Non-sequential Approach to Video-based Engagement Measurement

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:44.344779Z

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Observation 317e8a53-c2ac-4ee2-9583-1cf1d2748ffa · outbound

This paper cites Blink rate patterns provide a reliable measure of individual engagement with scene content,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Blink rate patterns provide a reliable measure of individual engagement with scene content,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:48.404471Z

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Observation 8956b785-a4d5-4287-8080-bb33697168f3 · outbound

This paper cites Openface 2.0: Facial behavior analysis toolkit,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Openface 2.0: Facial behavior analysis toolkit,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:48.227012Z

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Observation c6abcaa5-9ef3-49b8-9c79-8492a5eeb6ae · outbound

This paper cites Understanding the behaviour of contrastive loss,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Understanding the behaviour of contrastive loss,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:48.027659Z

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Observation 91021f1e-9283-41f6-83a8-f6657dec08e1 · outbound

This paper cites The ordinal nature of emotions: An emerging approach,.

Supervised Contrastive Learning for Ordinal Engagement Measurement The ordinal nature of emotions: An emerging approach,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:47.837059Z

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Observation 462ae4f6-2df2-4c5f-8606-47f6c639cca1 · outbound

This paper cites Estimation of continuous valence and arousal levels from faces in naturalistic conditions,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Estimation of continuous valence and arousal levels from faces in naturalistic conditions,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:47.631267Z

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Observation 17baff15-cb7b-45bd-9420-cf3677d1d1b0 · outbound

This paper cites Affectnet: A database for facial expression, valence, and arousal computing in the wild,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Affectnet: A database for facial expression, valence, and arousal computing in the wild,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:47.428744Z

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Observation 764865f9-7aaa-4cd4-bdb0-f2e3ec478d86 · outbound

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

Supervised Contrastive Learning for Ordinal Engagement Measurement Pytorch: An imperative style, high-performance deep learning library,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:45.643667Z

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Observation 2976e7fd-00cc-434e-b26f-74cc4206d205 · outbound

This paper cites Scikit-learn: Machine learning in python,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Scikit-learn: Machine learning in python,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:45.796873Z

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Observation e3914b61-4134-4c90-b972-c61ac60c35fb · outbound

This paper cites Facial Expression Recognition in Video Using 3D-CNN Deep Features Discrimination,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Facial Expression Recognition in Video Using 3D-CNN Deep Features Discrimination,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:47.235954Z

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Observation 4eeed171-2013-46c7-ac93-cd6fc33a1d0e · outbound

This paper cites Leveraging part-and-sensitive attention network and transformer for learner engagement detection,.

Supervised Contrastive Learning for Ordinal Engagement Measurement Leveraging part-and-sensitive attention network and transformer for learner engagement detection,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:47.035781Z

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Observation a07ab651-cfaf-4a7f-ba2d-6df8d8b77ba2 · outbound

This paper cites Available: https://doi.org/10.1038/s42256-020-00285-2.

Supervised Contrastive Learning for Ordinal Engagement Measurement Available: https://doi.org/10.1038/s42256-020-00285-2

Reference 2021

Resolution: verified exact; doi, observed 2026-08-07T13:53:46.398621Z

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