SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
Learning Pain from Action Unit Combinations: A Weakly Supervised Approach via Multiple Instance Learning
16 references·0 citing·48 second hopsampled
Learning Pain from Action Unit Combinations: A Weakly Supervised Approach via Multiple Instance Learning (2019)Learning Pain from Action Unit Combinatio…Regularizing face verification nets for pain intensity regression (2017)Regularizing face verificatio…Fusing Deep Learned and Hand-Crafted Features of Appearance, Shape, and Dynamics for Automatic Pain Estimation (2017)Fusing Deep Learned and Hand-…FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition (2017)FaceNet2ExpNet: Regularizing …Facial Expression Intensity Estimation Using Ordinal Information (2016)Facial Expression Intensity E…Automatic Pain Assessment with Facial Activity Descriptors (2016)Automatic Pain Assessment wit…The Automatic Detection of Chronic Pain-Related Expression: Requirements, Challenges and the Multimodal EmoPain Dataset (2015)The Automatic Detection of Ch…Pain recognition using spatiotemporal oriented energy of facial muscles (2015)Pain recognition using spatio…Classification and weakly supervised pain localization using multiple segment representation (2014)Classification and weakly sup…Towards Pain Monitoring: Facial Expression, Head Pose, a new Database, an Automatic System and Remaining Challenges (2013)Towards Pain Monitoring: Faci…Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild" (2013)Affectiva-MIT Facial Expressi…Towards the automatic detection of spontaneous agreement and disagreement based on nonverbal behaviour: A survey of related cues, databases, and tools (2012)Towards the automatic detecti…The computer expression recognition toolbox (CERT) (2011)The computer expression recog…Painful data: The UNBC-McMaster shoulder pain expression archive database (2011)Painful data: The UNBC-McMast…Automated Facial Action Coding System for dynamic analysis of facial expressions in neuropsychiatric disorders (2011)Automated Facial Action Codin…A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions (2008)A Survey of Affect Recognitio…Automatic Recognition of Facial Actions in Spontaneous Expressions (2006)Automatic Recognition of Faci…Facial Action Coding System (1978)Comprehensive database for facial expression analysis (2002)The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression (2010)Categorical and dimensional affect analysis in continuous input: Current trends and future directions (2012)AVEC 2016 (2016)Facial emotion recognition using convolutional neural networks (FERC) (2020)Facial expression recognition from video sequences: temporal and static modeling (2003)Web-Based Database for Facial Expression Analysis (2005)Depression recognition based on dynamic facial and vocal expression features using partial least square regression (2013)FERA 2015 - second Facial Expression Recognition and Analysis challenge (2015)Automatic Assessment of Depression Based on Visual Cues: A Systematic Review (2017)The SEMAINE Database: Annotated Multimodal Records of Emotionally Colored Conversations between a Person and a Limited Agent (2011)Video-based emotion recognition in the wild using deep transfer learning and score fusion (2017)Task-Dependent Algorithm Aversion (2019)Multimodal Emotion Recognition with Deep Learning: Advancements, challenges, and future directions (2023)Psychological Testing (1982)EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild (2016)Deep Affect Prediction in-the-Wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond (2019)Emotion recognition and artificial intelligence: A systematic review (2014–2023) and research recommendations (2023)Development and validation of brief measures of positive and negative affect: The PANAS scales. (1988)Development and validation of brief measures of positive and negative affect: The PANAS scales. (1988)Affective Computing (1997)Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications (2010)A Multimodal Database for Affect Recognition and Implicit Tagging (2011)Deep Facial Expression Recognition: A Survey (2020)The expression of the emotions in man and animals. (1872)The chameleon effect: The perception–behavior link and social interaction. (1999)It’s only a computer: Virtual humans increase willingness to disclose (2014)MSP-IMPROV: An Acted Corpus of Dyadic Interactions to Study Emotion Perception (2016)Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis (2016)The Measurement of Observer Agreement for Categorical Data (1977)The Hospital Anxiety and Depression Scale (1983)Forming inferences about some intraclass correlation coefficients. (1996)Updating the definition of pain (2016)NeuroSense: Short-term emotion recognition and understanding based on spiking neural network modelling of spatio-temporal EEG patterns (2021)Automatic assessment of pain based on deep learning methods: A systematic review (2023)Facial Expression Analysis (2005)Can Duchenne smiles be feigned? New evidence on felt and false smiles. (2009)Facial Expression Recognition with Identity and Emotion Joint Learning (2018)Automatic Detection of Pain from Facial Expressions: A Survey (2020)Intraclass correlations: Uses in assessing rater reliability. (1979)Deeper cascaded peak-piloted network for weak expression recognition (2017)Facial Expression Recognition: A Review of Trends and Techniques (2021)Deep Learning for Emotion Recognition on Small Datasets using Transfer Learning (2015)Going deeper in facial expression recognition using deep neural networks (2016)Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition (2020)Facial Emotion Recognition Using Transfer Learning in the Deep CNN (2021)Learning Deep Global Multi-Scale and Local Attention Features for Facial Expression Recognition in the Wild (2021)
this work references cited by second hop· second hop is a sample of the most-cited connections

References

  • 2008A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions2,797 cited
  • 2011Painful data: The UNBC-McMaster shoulder pain expression archive database617 cited
  • 2011The computer expression recognition toolbox (CERT)552 cited
  • 2006Automatic Recognition of Facial Actions in Spontaneous Expressions468 cited
  • 2017FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition416 cited
  • 2011Automated Facial Action Coding System for dynamic analysis of facial expressions in neuropsychiatric disorders288 cited
  • 2013Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"234 cited
  • 2015The Automatic Detection of Chronic Pain-Related Expression: Requirements, Challenges and the Multimodal EmoPain Dataset220 cited
  • 2016Automatic Pain Assessment with Facial Activity Descriptors166 cited
  • 2016Facial Expression Intensity Estimation Using Ordinal Information113 cited
  • 2013Towards Pain Monitoring: Facial Expression, Head Pose, a new Database, an Automatic System and Remaining Challenges95 cited
  • 2012Towards the automatic detection of spontaneous agreement and disagreement based on nonverbal behaviour: A survey of related cues, databases, and tools78 cited
  • 2017Regularizing face verification nets for pain intensity regression71 cited

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

2017
Fusing Deep Learned and Hand-Crafted Features of Appearance, Shape, and Dynamics for Automatic Pain Estimation
70 cited
  • 2014Classification and weakly supervised pain localization using multiple segment representation59 cited
  • 2015Pain recognition using spatiotemporal oriented energy of facial muscles38 cited
  • Cited by

    No citing works inside the corpus for this window.