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Paper Citation Record · LEDGER

Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video

As of 17 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2607.11120.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.11120 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T06:56:13.966770Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

23 of 23 outbound references displayed

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External citation measurements

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Outbound references

Observation 2d68d521-0104-4ade-8ce8-39e6064b0451 · outbound

This paper cites BAH Dataset for Ambivalence/Hesitancy Recognition in Videos for Digital Behavioural Change.

Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video BAH Dataset for Ambivalence/Hesitancy Recognition in Videos for Digital Behavioural Change

Reference 1

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source=pdf_text observed=2026-07-14T06:56:13.966770Z digest=sha256:802cb96e49e7ab85d05b20498651da306331672c3363a14c7ecf1a2713d7b8e0

Observation 8213a5da-2a24-4e0f-8153-a5300a718842 · outbound

This paper cites Conflict-aware multi- modal fusion for ambivalence and hesitancy recognition,.

Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video Conflict-aware multi- modal fusion for ambivalence and hesitancy recognition,

Reference 2

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source=pdf_text observed=2026-07-14T06:56:13.966770Z digest=sha256:5e8598351baed51c3795da711a11b17dab7028681747918320f3ae8837d8421a

Observation a73b7550-8d18-4c32-9fdc-cec8b4ad8eb9 · outbound

This paper cites HSEmotion Team at ABAW-8 Competition: Audiovisual Ambivalence/Hesitancy, Emotional Mimicry Intensity and Facial Expression Recognition.

Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video HSEmotion Team at ABAW-8 Competition: Audiovisual Ambivalence/Hesitancy, Emotional Mimicry Intensity and Facial Expression Recognition

Reference 3

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source=pdf_text observed=2026-07-14T06:56:13.966770Z digest=sha256:fdd6bcf9f1e190cb7e693334d7d66acadd979dd0e17c2fc65a64506746bfeb68

Observation 8d0f254b-22be-4e25-9e68-f8b8378e88eb · outbound

This paper cites The 6th affec- tive behavior analysis in-the-wild (abaw) competition,.

Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video The 6th affec- tive behavior analysis in-the-wild (abaw) competition,

Reference 4

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source=pdf_text observed=2026-07-14T06:56:13.966770Z digest=sha256:be7b6730c2e33ee1502cca760f589073c8b1b30b6ba781b30e1f7bb65300ab53

Observation 08a55e9e-99b4-41f7-89f1-ec068d77001f · outbound

This paper cites Context-dependent sen- timent analysis in user-generated videos,.

Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video Context-dependent sen- timent analysis in user-generated videos,

Reference 5

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source=pdf_text observed=2026-07-14T06:56:13.966770Z digest=sha256:109a90461d46d163c96585b6b16c1c5320f7b7509af93a400944f81880724f0f

Observation eba1441a-cb51-4604-94da-ae1f6375d3ff · outbound

This paper cites Verbal and nonverbal clues for real-life deception detection,.

Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video Verbal and nonverbal clues for real-life deception detection,

Reference 6

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source=pdf_text observed=2026-07-14T06:56:13.966770Z digest=sha256:84ac431106530a577e675b051b2ccd2127c03ecbdd8d53f55bd099af3947cf80

Observation 91d87e8c-50a2-44df-acfd-68eccaf69a81 · outbound

This paper cites Videomae: Masked autoencoders are data-efficient learners for self- supervised video pre-training,.

Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video Videomae: Masked autoencoders are data-efficient learners for self- supervised video pre-training,

Reference 7

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source=pdf_text observed=2026-07-14T06:56:13.966770Z digest=sha256:8ce85cb8bafbf1cc4deb6d6695b2a2e07c55b635c02e7f1a76df42ecf0153f73

Observation a533b303-1935-49e3-8f21-57684199402a · outbound

This paper cites Hubert: Self- supervised speech representation learning by masked pre- diction of hidden units,.

Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video Hubert: Self- supervised speech representation learning by masked pre- diction of hidden units,

Reference 8

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source=pdf_text observed=2026-07-14T06:56:13.966770Z digest=sha256:645f4ee92bec6faafe4b374c65f7f7800aef22188a9805a4d4f9c91e4f0252db

Observation 03c1026c-abac-443e-82d8-760aed9b4c35 · outbound

This paper cites Dawn of the transformer era in speech emotion recog- nition: closing the valence gap,.

Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video Dawn of the transformer era in speech emotion recog- nition: closing the valence gap,

Reference 9

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source=pdf_text observed=2026-07-14T06:56:13.966770Z digest=sha256:9be28f0c5521ae9777e3ba6334127f2926b1f3234dd40b855eb032870fea3561

Observation f1f90285-246c-4a54-a800-39991673c3c4 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 10

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source=pdf_text observed=2026-07-14T06:56:13.966770Z digest=sha256:52ea52d05813f24b4533d74cf91878a41994854d375166a5cb1fddde310ae602

Observation 29534ab9-534b-453c-a415-ec13352c8385 · outbound

This paper cites Goemotions: A dataset of fine-grained emotions,.

Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video Goemotions: A dataset of fine-grained emotions,

Reference 11

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source=pdf_text observed=2026-07-14T06:56:13.966770Z digest=sha256:f3f614504b5d95089f39049cc0308f6a8f4b218573b33ee38a494e8f16770aac

Observation e8199d11-9267-438e-95ee-20b14e83425e · outbound

This paper cites DeBERTa: Decoding-enhanced BERT with Disentangled Attention.

Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video DeBERTa: Decoding-enhanced BERT with Disentangled Attention

Reference 12

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source=pdf_text observed=2026-07-14T06:56:13.966770Z digest=sha256:9184600a6bba75de8f76d8ef83e7c72719102690300b065ba89fb13c0328050a

Observation 008f573a-bcd0-4f65-9b60-6141c06c66c0 · outbound

This paper cites On calibration of modern neural networks,.

Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video On calibration of modern neural networks,

Reference 13

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source=pdf_text observed=2026-07-14T06:56:13.966770Z digest=sha256:16687add0b90f60818c29243d241a682eb9cb2c5836ac5d97e931f57aa15521b

Observation b1c1e0f5-bed7-459f-83a0-b2fcfa5f994e · outbound

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

Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 14

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source=pdf_text observed=2026-07-14T06:56:13.966770Z digest=sha256:36da5ce9f5722a5d6fb1dadf038526f54124e7df7b8adfc023597a7228328a0b

Observation 9d7998e1-9932-4f38-9009-4a8d4cda96b5 · outbound

This paper cites wav2vec 2.0: A framework for self-supervised learning of speech representations,.

Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video wav2vec 2.0: A framework for self-supervised learning of speech representations,

Reference 15

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source=pdf_text observed=2026-07-14T06:56:13.966770Z digest=sha256:e0f03bc8e1431863a88993cb93815487b41a05c3371023ac9d1128bf454ea7b8

Observation d2e60520-42bf-4977-9cb8-27646d0a34be · outbound

This paper cites Rethinking the inception architecture for computer vision,.

Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video Rethinking the inception architecture for computer vision,

Reference 16

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source=pdf_text observed=2026-07-14T06:56:13.966770Z digest=sha256:7ce6b58a6f8b1b69e903d3fa2ee81283d01358c0641537e7f7a4b50c0befbf7b

Observation d6c0700b-eba1-4f5f-8270-bf59e8e92731 · outbound

This paper cites Robust speech recog- nition via large-scale weak supervision,.

Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video Robust speech recog- nition via large-scale weak supervision,

Reference 17

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Observation 7b979ff9-573b-4314-bd4f-80793cbb780b · outbound

This paper cites Decoupled Weight Decay Regularization.

Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video Decoupled Weight Decay Regularization

Reference 18

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source=pdf_text observed=2026-07-14T06:56:13.966770Z digest=sha256:8a9a6e3ac1462ab9eae2b2cd1b6928c13721fb0db41db59a603efdc5b9be0c77

Observation fe2d5514-f9e1-4557-ae6d-e1ef7aa8a9cf · outbound

This paper cites R-drop: Regularized dropout for neural networks,.

Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video R-drop: Regularized dropout for neural networks,

Reference 19

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Observation a51271b6-66c9-4d06-91ba-dbeab03e03cd · outbound

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

Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video Pytorch: An imperative style, high-performance deep learning library,

Reference 20

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source=pdf_text observed=2026-07-14T06:56:13.966770Z digest=sha256:dc8976dd75c9fc721e8b506ad8a7f8a6cffc6807629e81ff4b07d95181670426

Observation 982a2056-e8e4-422d-bbae-6810b73a47ae · outbound

This paper cites Transformers: State-of-the-art natural language process- ing,.

Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video Transformers: State-of-the-art natural language process- ing,

Reference 21

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source=pdf_text observed=2026-07-14T06:56:13.966770Z digest=sha256:ecdba9f2ccd4ea0b594007ebed9627d44247fe109f8eb69ee44e4492f4f317d2

Observation 99e7f937-23c8-4c38-bf0f-b17542b3d652 · outbound

This paper cites Efron and R.

Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video Efron and R

Reference 22

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source=pdf_text observed=2026-07-14T06:56:13.966770Z digest=sha256:4349f86f266dcfdf4d7de320a96a7075a7f683d1cddc32b0116e0e5f8ab38995

Observation 0b66660e-3f1f-4291-8729-ed3d0c723bfb · outbound

This paper cites MediaPipe: A Framework for Building Perception Pipelines.

Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video MediaPipe: A Framework for Building Perception Pipelines

Reference 23

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source=pdf_text observed=2026-07-14T06:56:13.966770Z digest=sha256:d7fa7335e4bdd69c3711e2f75d718ab20cf7aa39e8e02aeaf468cfb9d35a795f

Pith citing papers

No inbound Pith citation observations are available.