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Improved facial expression recognition based on DWT feature for deep CNN
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In: ISSN: 2079-9292 ; Electronics ; https://hal.archives-ouvertes.fr/hal-03143597 ; Electronics, MDPI, 2019, 8 (3), pp.324. ⟨10.3390/electronics8030324⟩ (2019)
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The Airbus Air Traffic Control speech recognition 2018 challenge: towards ATC automatic transcription and call sign detection
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In: Proceedings of INTERSPEECH 2019 ; 20th Annual Conference of the International Speech Communication Association (INTERSPEECH 2019) ; https://hal.archives-ouvertes.fr/hal-02419437 ; 20th Annual Conference of the International Speech Communication Association (INTERSPEECH 2019), Sep 2019, Graz, Austria. pp.2993-2997 (2019)
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Challenges in Audio Processing of Terrorist-Related Data
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In: International Conference on Multimedia Modeling ; https://hal.archives-ouvertes.fr/hal-02415176 ; International Conference on Multimedia Modeling, Springer, Jan 2019, Thessaloniki, Greece (2019)
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Impact and Detection of Facial Beautification in Face Recognition: An Overview
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In: ISSN: 2169-3536 ; EISSN: 2169-3536 ; IEEE Access ; https://hal.inria.fr/hal-02378939 ; IEEE Access, IEEE, 2019, ⟨10.1109/ACCESS.2019.DOI⟩ (2019)
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Char+CV-CTC: Combining Graphemes and Consonant/Vowel Units for CTC-Based ASR Using Multitask Learning
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In: Proceedings of INTERSPEECH 2019 ; 20th Annual Conference of the International Speech Communication Association (INTERSPEECH 2019) ; https://hal.archives-ouvertes.fr/hal-02419431 ; 20th Annual Conference of the International Speech Communication Association (INTERSPEECH 2019), Sep 2019, Graz, Austria. pp.1611-1615 (2019)
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Interests of using Automatic Speech recognition for Speech-Language Therapists
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In: World Congress of the International Association of Logopedics and Phoniatrics ; https://hal.archives-ouvertes.fr/hal-03012571 ; World Congress of the International Association of Logopedics and Phoniatrics, IALP : International Association of Logopedics and Phoniatrics, Aug 2019, Taipei, Taiwan. pp.(electronic medium) ; http://www.ialptaipei2019.org/ (2019)
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Challenges in Audio Processing of Terrorist-Related Data
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In: International Conference on Multimedia Modeling ; https://hal.archives-ouvertes.fr/hal-02387373 ; International Conference on Multimedia Modeling, Springer, Jan 2019, Thessaloniki, Greece (2019)
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A replicable comparison study of NER software: StanfordNLP, NLTK, OpenNLP, SpaCy, Gate
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In: Sixth International Conference on Social Networks Analysis, Management and Security, SNAMS 2019 ; https://hal.archives-ouvertes.fr/hal-03002671 ; Sixth International Conference on Social Networks Analysis, Management and Security, SNAMS 2019, Oct 2019, Grenada, Spain. ⟨10.1109/SNAMS.2019.8931850⟩ (2019)
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Extractive Text-Based Summarization of Arabic videos: Issues, Approaches and Evaluations
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In: ICALP: International Conference on Arabic Language Processing ; https://hal.archives-ouvertes.fr/hal-02314238 ; ICALP: International Conference on Arabic Language Processing, Oct 2019, Nancy, France. pp.65-78, ⟨10.1007/978-3-030-32959-4_5⟩ (2019)
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Brain and perceptual representations of faces, voices, and person identity
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Judging Normality and Attractiveness in Faces: Direct Evidence of a More Refined Representation for Own-Race, Young Adult Faces
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Extracting Data From Line Charts in Scanned Medical Documents
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Segmentation-Free Bangla Offline Handwriting Recognition Using Sequential Detection of Characters and Diacritics with a Faster R-CNN
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In: Electrical and Computer Engineering Faculty Publications and Presentations (2019)
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Sign Language Video Analysis For Automatic Recognition and Detection
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In: 14th IEEE International Conference on Automatic Face and Gesture Recognition ; https://hal.archives-ouvertes.fr/hal-02146366 ; 14th IEEE International Conference on Automatic Face and Gesture Recognition, May 2019, Lille, France (2019)
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Phoneme‐Order Encoding During Spoken Word Recognition: A Priming Investigation
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In: ISSN: 0364-0213 ; EISSN: 1551-6709 ; Cognitive Science ; https://hal.archives-ouvertes.fr/hal-02292742 ; Cognitive Science, Wiley, 2019, 43 (10), pp.1-16. ⟨10.1111/cogs.12785⟩ (2019)
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Multimodal deep networks for text and image-based document classification ; Réseau de neurones multimodal pour la classification de documents image/texte
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In: Conférence Nationale sur les Applications Pratiques de l'Intelligence Artificielle (APIA) ; https://hal.archives-ouvertes.fr/hal-02163257 ; Conférence Nationale sur les Applications Pratiques de l'Intelligence Artificielle (APIA), Jul 2019, Toulouse, France (2019)
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Abstract:
International audience ; Classification of document images is a critical step for archival of old manuscripts, online subscription and administrative procedures. Computer vision and deep learning have been suggested as a first solution to classify documents based on their visual appearance. However, achieving the fine-grained classification that is required in real-world setting cannot be achieved by visual analysis alone. Often, the relevant information is in the actual text content of the document. We design a multimodal neural network that is able to learn from word embeddings, computed on text extracted by OCR, and from the image. We show that this approach boosts pure image accuracy by 3% on Tobacco3482 and RVL-CDIP augmented by our new QS-OCR text dataset (https://github.com/Quicksign/ocrized-text-dataset), even without clean text information. ; La classification automatique de documents numérisés est im-portante pour la dématérialisation de documents historiques comme de procédures administratives. De premières ap-proches ont été suggérées en appliquant des réseaux con-volutifs aux images de documents en exploitant leur aspect visuel. Toutefois, la précision des classes demandée dans un contexte réel dépend souvent de l'information réellement contenue dans le texte, et pas seulement dans l'image. Nous introduisons un réseau de neurones multimodal capable d'apprendre à partir d'un plongement lexical du texte ex-trait par reconnaissance de caractères et des caractéris-tiques visuelles de l'image. Nous démontrons la pertinence de cette approche sur Tobacco3482 et RVL-CDIP, augmen-tés de notre jeu de données textuel QS-OCR (https://github.com/Quicksign/ocrized-text-dataset), sur lesquels nous améliorons les performances d'un modèle image de 3% grâce à l'information sémantique textuelle.
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Keyword:
[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]; [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]; [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]; apprentissage multimodal; apprentissage profond; classification de documents; data fusion; deep learning; Document classification; fusion de données; multimodal learning
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URL: https://hal.archives-ouvertes.fr/hal-02163257 https://hal.archives-ouvertes.fr/hal-02163257/document https://hal.archives-ouvertes.fr/hal-02163257/file/article_apia.pdf
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Adapting a FrameNet Semantic Parser for Spoken Language Understanding Using Adversarial Learning
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In: Interspeech 2019 ; https://hal.archives-ouvertes.fr/hal-02298417 ; Interspeech 2019, Sep 2019, Graz, Austria. pp.799-803, ⟨10.21437/Interspeech.2019-2732⟩ (2019)
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A unified multilingual handwriting recognition system using multigrams sub-lexical units
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In: ISSN: 0167-8655 ; Pattern Recognition Letters ; https://hal-normandie-univ.archives-ouvertes.fr/hal-02075654 ; Pattern Recognition Letters, Elsevier, 2019, 121, pp.68-76. ⟨10.1016/j.patrec.2018.07.027⟩ (2019)
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