From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition | Paper Citation Record

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This paper cites The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression,

Reference 1

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Observation 8f33e461-9f0b-45e0-87f3-fc36ada3e9af · outbound

This paper cites Induced disgust, happiness and surprise: an addition to the mmi facial expression database,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Induced disgust, happiness and surprise: an addition to the mmi facial expression database,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:09.788431Z

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Observation c7d2cc0f-ba65-45e7-82ba-d384efa2e394 · outbound

This paper cites Facial expression recognition from near-infrared videos,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Facial expression recognition from near-infrared videos,

Reference 3

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Observation 697b9dc7-4c81-4042-ba30-c4b59b5cdca8 · outbound

This paper cites Dfew: A large-scale database for recognizing dynamic facial expressions in the wild,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Dfew: A large-scale database for recognizing dynamic facial expressions in the wild,

Reference 4

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This paper cites Mafw: A large-scale, multi-modal, compound affective database for dynamic facial expression recognition in the wild,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Mafw: A large-scale, multi-modal, compound affective database for dynamic facial expression recognition in the wild,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:08.923395Z

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This paper cites Ferv39k: A large-scale multi-scene dataset for facial expres- sion recognition in videos,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Ferv39k: A large-scale multi-scene dataset for facial expres- sion recognition in videos,

Reference 6

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This paper cites Dep-fer: Facial expression recognition in depressed patients based on voluntary facial expression mimicry,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Dep-fer: Facial expression recognition in depressed patients based on voluntary facial expression mimicry,

Reference 7

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Observation 770416af-30bd-44e0-ab48-7554f99f46f0 · outbound

This paper cites Efficient facial expression recognition with representation reinforcement network and transfer self-training for human–machine interaction,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Efficient facial expression recognition with representation reinforcement network and transfer self-training for human–machine interaction,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:08.367493Z

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Observation afccfec2-b81d-41ca-baa9-d4b7a9b331e4 · outbound

This paper cites Predicting personal- ized image emotion perceptions in social networks,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Predicting personal- ized image emotion perceptions in social networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:08.225797Z

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Observation 8e3d8a74-1cb2-45fd-9683-5afaff8dc945 · outbound

This paper cites Spatio-temporal convolutional features with nested lstm for facial expression recognition,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Spatio-temporal convolutional features with nested lstm for facial expression recognition,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:08.056382Z

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This paper cites Saanet: Siamese action-units attention network for improving dynamic facial expression recognition,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Saanet: Siamese action-units attention network for improving dynamic facial expression recognition,

Reference 11

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This paper cites Deep residual learning for image recognition,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Deep residual learning for image recognition,

Reference 12

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This paper cites Former-dfer: Dynamic facial expression recog- nition transformer,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Former-dfer: Dynamic facial expression recog- nition transformer,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:07.867624Z

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This paper cites Ex- pression snippet transformer for robust video-based facial expression recognition,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Ex- pression snippet transformer for robust video-based facial expression recognition,

Reference 14

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This paper cites Freq-hd: An interpretable frequency-based high-dynamics affective clip selection method for in-the-wild facial expression recognition in videos,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Freq-hd: An interpretable frequency-based high-dynamics affective clip selection method for in-the-wild facial expression recognition in videos,

Reference 16

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Observation 18c7a973-d1b5-4155-985c-bc9ab4855e05 · outbound

This paper cites Facial expression recognition with adaptive frame rate based on multiple testing correction,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Facial expression recognition with adaptive frame rate based on multiple testing correction,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:07.651357Z

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Observation a028bf9e-5a31-4acf-b5d9-cbfd4792e5d4 · outbound

This paper cites Video-based facial micro-expression analysis: A survey of datasets, features and algorithms,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Video-based facial micro-expression analysis: A survey of datasets, features and algorithms,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:07.455501Z

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Observation 7fd07951-24e8-4737-90d0-a71bd023e60d · outbound

This paper cites Dynamic facial expression recognition under partial occlusion with optical flow reconstruction,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Dynamic facial expression recognition under partial occlusion with optical flow reconstruction,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:07.145228Z

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This paper cites From static to dynamic: Adapting landmark-aware image models for facial expression recognition in videos,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition From static to dynamic: Adapting landmark-aware image models for facial expression recognition in videos,

Reference 21

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This paper cites A Survey on Facial Expression Recognition of Static and Dynamic Emotions.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition A Survey on Facial Expression Recognition of Static and Dynamic Emotions

Reference 22

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This paper cites Prompting Visual-Language Models for Dynamic Facial Expression Recognition.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Prompting Visual-Language Models for Dynamic Facial Expression Recognition

Reference 23

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This paper cites Emoclip: A vision-language method for zero-shot video facial expression recognition,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Emoclip: A vision-language method for zero-shot video facial expression recognition,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:06.252373Z

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This paper cites Domain knowledge enhanced vision-language pretrained model for dynamic facial expression recognition,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Domain knowledge enhanced vision-language pretrained model for dynamic facial expression recognition,

Reference 25

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This paper cites Enhancing zero-shot facial expression recognition by llm knowledge transfer,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Enhancing zero-shot facial expression recognition by llm knowledge transfer,

Reference 26

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This paper cites Finecliper: Multi-modal fine-grained clip for dynamic facial expression recognition with adapters,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Finecliper: Multi-modal fine-grained clip for dynamic facial expression recognition with adapters,

Reference 28

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This paper cites Describe your facial expressions by linking image encoders and large language models.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Describe your facial expressions by linking image encoders and large language models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:05.528965Z

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This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 31

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This paper cites Hierarchical Transformers for Multi-Document Summarization.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Hierarchical Transformers for Multi-Document Summarization

Reference 32

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This paper cites HDT: Hierarchical Document Transformer.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition HDT: Hierarchical Document Transformer

Reference 33

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This paper cites Multi-task learning of hierarchical vision-language representation,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Multi-task learning of hierarchical vision-language representation,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:05.349116Z

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This paper cites HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training

Reference 35

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This paper cites Hierarchical modular network for video captioning,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Hierarchical modular network for video captioning,

Reference 36

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This paper cites Learning transferable visual models from natural language supervision,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Learning transferable visual models from natural language supervision,

Reference 37

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This paper cites Ceprompt: Cross-modal emotion-aware prompting for facial expression recognition,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Ceprompt: Cross-modal emotion-aware prompting for facial expression recognition,

Reference 38

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This paper cites Sinkhorn distances: Lightspeed computation of optimal transport,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Sinkhorn distances: Lightspeed computation of optimal transport,

Reference 39

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From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Recent advances in optimal transport for machine learning,

Reference 40

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This paper cites Reliable weighted optimal transport for unsupervised domain adaptation,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Reliable weighted optimal transport for unsupervised domain adaptation,

Reference 41

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From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Unsupervised learning of visual features by contrasting cluster as- signments,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:04.349557Z

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From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Optimal partial transport based sentence selection for long-form document matching,

Reference 43

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This paper cites Learning to align sequential actions in the wild,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Learning to align sequential actions in the wild,

Reference 44

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This paper cites What when and where? self-supervised spatio-temporal grounding in untrimmed multi-action videos from narrated instructions,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition What when and where? self-supervised spatio-temporal grounding in untrimmed multi-action videos from narrated instructions,

Reference 45

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Observation a0a922a5-baee-4e19-869d-6cc760f1c08f · outbound

This paper cites Multi-granularity Correspondence Learning from Long-term Noisy Videos.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Multi-granularity Correspondence Learning from Long-term Noisy Videos

Reference 46

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This paper cites Spatial- temporal graphs plus transformers for geometry-guided facial expression recognition,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Spatial- temporal graphs plus transformers for geometry-guided facial expression recognition,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:06.842125Z

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This paper cites Multimodal transformer for unaligned multimodal language sequences,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Multimodal transformer for unaligned multimodal language sequences,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:02.956146Z

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This paper cites Vilt: Vision-and-language transformer without convolution or region supervision,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Vilt: Vision-and-language transformer without convolution or region supervision,

Reference 50

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This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,

Reference 51

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This paper cites Cliper: A unified vision-language framework for in-the-wild facial expression recognition,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Cliper: A unified vision-language framework for in-the-wild facial expression recognition,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:02.695424Z

Source-reported events for the cited work

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Observation a771a906-1941-46cd-9384-d776e716758d · outbound

This paper cites Video-LLaVA: Learning United Visual Representation by Alignment Before Projection.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Video-LLaVA: Learning United Visual Representation by Alignment Before Projection

Reference 53

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Observation 2ef3652f-b4ba-4a6a-9be8-6020b5bdcb3b · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Exploring the limits of transfer learning with a unified text-to-text transformer,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T17:08:56.854748Z

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Observation 0ff0bbb6-eb8a-41d8-ba58-e1eb879b040d · outbound

This paper cites VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T17:08:56.989600Z

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Observation d7fdddc8-f420-4089-a31d-40ca86630da1 · outbound

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

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Videomae: Masked autoen- coders are data-efficient learners for self-supervised video pre-training,

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T17:08:57.095778Z

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Observation c0f13447-3841-4612-bf12-eab2e9546562 · outbound

This paper cites Mae-dfer: Efficient masked autoencoder for self-supervised dynamic facial expression recognition,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Mae-dfer: Efficient masked autoencoder for self-supervised dynamic facial expression recognition,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T17:08:57.212905Z

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Observation 40c53bc4-6176-455b-b879-1db2e02025b8 · outbound

This paper cites Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image cap- 14 tioning,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image cap- 14 tioning,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:02.372262Z

Source-reported events for the cited work

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Observation 0d0349cd-faf4-4122-9945-7563471fbdbb · outbound

This paper cites Frozen in time: A joint video and image encoder for end-to-end retrieval,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Frozen in time: A joint video and image encoder for end-to-end retrieval,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:02.133934Z

Source-reported events for the cited work

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Observation c3208862-0942-423d-9cbc-8b85e139500c · outbound

This paper cites Emotion recognition using imperfect speech recognition,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Emotion recognition using imperfect speech recognition,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:01.900774Z

Source-reported events for the cited work

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Observation 0edd08c9-2483-4ae2-b727-2e19eecae091 · outbound

This paper cites Posterior calibration for multi- class paralinguistic classification,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Posterior calibration for multi- class paralinguistic classification,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:01.577572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b06a9716-7b97-40fa-a894-d07f957a2a4c · outbound

This paper cites Rethinking the learning paradigm for dynamic facial expression recog- nition,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Rethinking the learning paradigm for dynamic facial expression recog- nition,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:01.317518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e9fc0632-63cf-4362-9066-95f90e68fbd1 · outbound

This paper cites A$^{3}$lign-DFER: Pioneering Comprehensive Dynamic Affective Alignment for Dynamic Facial Expression Recognition with CLIP.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition A$^{3}$lign-DFER: Pioneering Comprehensive Dynamic Affective Alignment for Dynamic Facial Expression Recognition with CLIP

Reference 63

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Source-reported events for the cited work

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Observation bb674402-af34-407a-86c6-4a7b01908490 · outbound

This paper cites Clip-aware expressive feature learning for video-based facial expression recognition,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Clip-aware expressive feature learning for video-based facial expression recognition,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:01.028233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5c53bea4-72b1-4926-8ec7-ed45a3ceb9b8 · outbound

This paper cites NR-DFERNet: Noise-Robust Network for Dynamic Facial Expression Recognition.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition NR-DFERNet: Noise-Robust Network for Dynamic Facial Expression Recognition

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T17:08:58.322047Z

Source-reported events for the cited work

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Observation c01291c4-fcdc-4459-8ccd-dd3fbabce61f · outbound

This paper cites Logo-former: Local-global spatio-temporal transformer for dynamic facial expression recognition,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Logo-former: Local-global spatio-temporal transformer for dynamic facial expression recognition,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T17:08:58.457153Z

Source-reported events for the cited work

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Observation 025acfcc-723b-4024-9ea7-4130c4153e70 · outbound

This paper cites Intensity-aware loss for dynamic facial expression recognition in the wild,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Intensity-aware loss for dynamic facial expression recognition in the wild,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:03.269880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 44e6292c-dc8c-490b-8ef8-c2126613213e · outbound

This paper cites Transformer-based multimodal emotional perception for dynamic facial expression recogni- tion in the wild,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Transformer-based multimodal emotional perception for dynamic facial expression recogni- tion in the wild,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:00.714047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6d6c81c3-f280-4671-9ef9-d9f219746c94 · outbound

This paper cites Svfap: Self-supervised video facial affect perceiver,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Svfap: Self-supervised video facial affect perceiver,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:00.483481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 56f4fb15-b13d-4326-8e4b-6bd0070b72bd · outbound

This paper cites Hicmae: Hierarchical contrastive masked autoencoder for self-supervised audio-visual emotion recogni- tion,.

From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition Hicmae: Hierarchical contrastive masked autoencoder for self-supervised audio-visual emotion recogni- tion,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:09:00.250849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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