An Audio-Video Deep and Transfer Learning Framework for Multimodal Emotion Recognition in the wild
In this paper, we present our contribution to ABAW facial expression challenge. We report the proposed system and the official challenge results adhering to the challenge protocol. Using end-to-end deep learning and benefiting from transfer learning approaches, we reached a test set challenge perfor...
Saved in:
| Published in | arXiv.org |
|---|---|
| Main Authors | , , , , , |
| Format | Paper |
| Language | English |
| Published |
Ithaca
Cornell University Library, arXiv.org
02.11.2020
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | In this paper, we present our contribution to ABAW facial expression challenge. We report the proposed system and the official challenge results adhering to the challenge protocol. Using end-to-end deep learning and benefiting from transfer learning approaches, we reached a test set challenge performance measure of 42.10%. |
|---|---|
| AbstractList | In this paper, we present our contribution to ABAW facial expression challenge. We report the proposed system and the official challenge results adhering to the challenge protocol. Using end-to-end deep learning and benefiting from transfer learning approaches, we reached a test set challenge performance measure of 42.10%. |
| Author | Dresvyanskiy, Denis Minker, Wolfgang Karpov, Alexey Kaya, Heysem Markitantov, Maxim Ryumina, Elena |
| Author_xml | – sequence: 1 givenname: Denis surname: Dresvyanskiy fullname: Dresvyanskiy, Denis – sequence: 2 givenname: Elena surname: Ryumina fullname: Ryumina, Elena – sequence: 3 givenname: Heysem surname: Kaya fullname: Kaya, Heysem – sequence: 4 givenname: Maxim surname: Markitantov fullname: Markitantov, Maxim – sequence: 5 givenname: Alexey surname: Karpov fullname: Karpov, Alexey – sequence: 6 givenname: Wolfgang surname: Minker fullname: Minker, Wolfgang |
| BookMark | eNqNissKwjAQAIMo-Oo_LHgu1KTxcRQfeNCLFK8SzVZT211NWvx9RfwATzMw0xdtYsKW6EmlxvEslbIrohCKJEnkZCq1Vj1xXhAsGus4PjqLDCvEBxiykHlDIUcPOzSeHF1h402FL_Z3yNnDvilrV7E1Jawrrh0THPDCV3JfdwT1DeHlSjsUndyUAaMfB2K0WWfLbfzw_Gww1KeCG0-fdJJpOtd6pnSq_rvejjBGww |
| ContentType | Paper |
| Copyright | 2020. This work is published under http://creativecommons.org/licenses/by-nc-sa/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2020. This work is published under http://creativecommons.org/licenses/by-nc-sa/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | 8FE 8FG ABJCF ABUWG AFKRA AZQEC BENPR BGLVJ CCPQU DWQXO HCIFZ L6V M7S PIMPY PQEST PQQKQ PQUKI PRINS PTHSS |
| DatabaseName | ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials AUTh Library subscriptions: ProQuest Central Technology Collection ProQuest One Community College ProQuest Central SciTech Premium Collection (Proquest) (PQ_SDU_P3) ProQuest Engineering Collection Engineering Database Publicly Available Content Database ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection |
| DatabaseTitle | Publicly Available Content Database Engineering Database Technology Collection ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central ProQuest Engineering Collection ProQuest One Academic UKI Edition ProQuest Central Korea Materials Science & Engineering Collection ProQuest One Academic Engineering Collection |
| DatabaseTitleList | Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Physics |
| EISSN | 2331-8422 |
| Genre | Working Paper/Pre-Print |
| GroupedDBID | 8FE 8FG ABJCF ABUWG AFKRA ALMA_UNASSIGNED_HOLDINGS AZQEC BENPR BGLVJ CCPQU DWQXO FRJ HCIFZ L6V M7S M~E PIMPY PQEST PQQKQ PQUKI PRINS PTHSS |
| ID | FETCH-proquest_journals_24495583543 |
| IEDL.DBID | 8FG |
| IngestDate | Thu Oct 10 18:32:15 EDT 2024 |
| IsOpenAccess | true |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-proquest_journals_24495583543 |
| OpenAccessLink | https://www.proquest.com/docview/2449558354?pq-origsite=%requestingapplication% |
| PQID | 2449558354 |
| PQPubID | 2050157 |
| ParticipantIDs | proquest_journals_2449558354 |
| PublicationCentury | 2000 |
| PublicationDate | 20201102 |
| PublicationDateYYYYMMDD | 2020-11-02 |
| PublicationDate_xml | – month: 11 year: 2020 text: 20201102 day: 02 |
| PublicationDecade | 2020 |
| PublicationPlace | Ithaca |
| PublicationPlace_xml | – name: Ithaca |
| PublicationTitle | arXiv.org |
| PublicationYear | 2020 |
| Publisher | Cornell University Library, arXiv.org |
| Publisher_xml | – name: Cornell University Library, arXiv.org |
| SSID | ssj0002672553 |
| Score | 3.2923996 |
| SecondaryResourceType | preprint |
| Snippet | In this paper, we present our contribution to ABAW facial expression challenge. We report the proposed system and the official challenge results adhering to... |
| SourceID | proquest |
| SourceType | Aggregation Database |
| SubjectTerms | Emotion recognition Machine learning |
| Title | An Audio-Video Deep and Transfer Learning Framework for Multimodal Emotion Recognition in the wild |
| URI | https://www.proquest.com/docview/2449558354 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LSwMxEB60RfDmEx-1DOg1uM02u9uTVN21CC2lqPRWdpOpFOxu7ePqb3cSd_Ug9JYQCElIZr758iUDcBMGNDVtqQWjDRLtLOyIyCcSng6igBGG1O63_f4g6L22n8dqXBJuq1JWWdlEZ6hNoS1HfstuqKOUpSnuFp_CZo2yt6tlCo1dqLdkGFpJV5Q8_XIsMggZMfv_zKzzHckB1IfpgpaHsEP5Eew5yaVeHUPWzbG7MbNCvM0MFfhItECO69G5jyktsfz79B2TSkGFDDHRvZmdFyb9wPgnCQ-OKhkQl2c5MqhDxsDmBK6T-OWhJ6qBTcqts5r8TdQ_hVpe5HQGyCCi42vPTzPDR06ZNFLSI-VJ08qMr9U5NLb1dLG9-RL2pQ0jLVsqG1BbLzd0xb52nTXdgjahfh8PhiOu9b_ib6LHiZc |
| link.rule.ids | 783,787,12778,21401,33386,33757,43613,43818 |
| linkProvider | ProQuest |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3fS8MwED50RfTNn_hj6oG-Bmu6dN2TTF2puo0xpuxttMlNCtrWdfv_TWKrD8LeAoGQhOTuuy9f7gCu2z7NVYtLptEGsVbS7rDAI2Ku9ANfIwwubbb9wdCPXlvPUzGtCLeyklXWNtEaapVLw5HfaDfUEcLQFHfFFzNVo8zralVCYxMck6oqaIBz3xuOxr8sC_fbGjN7_wyt9R7hLjijuKDFHmxQtg9bVnQpywNIuhl2VyrN2VuqKMdHogJ1ZI_WgcxpgVX203cMaw0VapCJ9tfsZ67iD-z9lOHBcS0E0u00Qw3rUKNgdQhXYW_yELF6YrPq8JSzv6V6R9DI8oyOATWM6HjS9eJE6UsnVBwI7pJwubpNlCfFCTTXjXS6vvsStqPJoD_rPw1fzmCHm6DScKe8CY3lYkXn2vMuk4tqe78B-f-LHQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=An+Audio-Video+Deep+and+Transfer+Learning+Framework+for+Multimodal+Emotion+Recognition+in+the+wild&rft.jtitle=arXiv.org&rft.au=Dresvyanskiy%2C+Denis&rft.au=Ryumina%2C+Elena&rft.au=Kaya%2C+Heysem&rft.au=Markitantov%2C+Maxim&rft.date=2020-11-02&rft.pub=Cornell+University+Library%2C+arXiv.org&rft.eissn=2331-8422 |