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Trabalho de evento
Accent classification is challenging but pre-training helps: a case study with novel brazilian portuguese datasets (2024)
Matos, Ariadne Nascimento ; Araújo, Gustavo Evangelista; Candido Junior, Arnaldo ; Ponti, Moacir Antonelli
Source: Proceedings. Conference titles: International Conference on Computational Processing of Portuguese - PROPOR. Unidade: ICMC
Subjects: SÍNTESE DE FALA, PROCESSAMENTO DE LINGUAGEM NATURAL, APRENDIZADO COMPUTACIONAL, PROSÓDIA, PORTUGUÊS DO BRASIL
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ABNT
MATOS, Ariadne Nascimento et al. Accent classification is challenging but pre-training helps: a case study with novel brazilian portuguese datasets. 2024, Anais.. Stroudsburg: ACL, 2024. Disponível em: https://aclanthology.org/2024.propor-1.37. Acesso em: 06 nov. 2024.
APA
Matos, A. N., Araújo, G. E., Candido Junior, A., & Ponti, M. A. (2024). Accent classification is challenging but pre-training helps: a case study with novel brazilian portuguese datasets. In Proceedings. Stroudsburg: ACL. Recuperado de https://aclanthology.org/2024.propor-1.37
NLM
Matos AN, Araújo GE, Candido Junior A, Ponti MA. Accent classification is challenging but pre-training helps: a case study with novel brazilian portuguese datasets [Internet]. Proceedings. 2024 ;[citado 2024 nov. 06 ] Available from: https://aclanthology.org/2024.propor-1.37
Vancouver
Matos AN, Araújo GE, Candido Junior A, Ponti MA. Accent classification is challenging but pre-training helps: a case study with novel brazilian portuguese datasets [Internet]. Proceedings. 2024 ;[citado 2024 nov. 06 ] Available from: https://aclanthology.org/2024.propor-1.37
Trabalho de evento
TTS applied to the generation of datasets for automatic speech recognition (2024)
Casanova, Edresson ; Aluísio, Sandra Maria ; Ponti, Moacir Antonelli
Source: Proceedings. Conference titles: International Conference on Computational Processing of Portuguese - PROPOR. Unidade: ICMC
Subjects: RECONHECIMENTO DA FALA, APRENDIZAGEM PROFUNDA, PORTUGUÊS DO BRASIL
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ABNT
CASANOVA, Edresson e ALUÍSIO, Sandra Maria e PONTI, Moacir Antonelli. TTS applied to the generation of datasets for automatic speech recognition. 2024, Anais.. Stroudsburg: ACL, 2024. Disponível em: https://aclanthology.org/2024.propor-1.73. Acesso em: 06 nov. 2024.
APA
Casanova, E., Aluísio, S. M., & Ponti, M. A. (2024). TTS applied to the generation of datasets for automatic speech recognition. In Proceedings. Stroudsburg: ACL. Recuperado de https://aclanthology.org/2024.propor-1.73
NLM
Casanova E, Aluísio SM, Ponti MA. TTS applied to the generation of datasets for automatic speech recognition [Internet]. Proceedings. 2024 ;[citado 2024 nov. 06 ] Available from: https://aclanthology.org/2024.propor-1.73
Vancouver
Casanova E, Aluísio SM, Ponti MA. TTS applied to the generation of datasets for automatic speech recognition [Internet]. Proceedings. 2024 ;[citado 2024 nov. 06 ] Available from: https://aclanthology.org/2024.propor-1.73
Trabalho de evento
Decoupling decision-making in fraud prevention through classifier calibration for business logic action (2024)
Luzio, Emanuele ; Ponti, Moacir Antonelli ; Ramirez Arevalo, Christian; Argerich, Luis
Source: Proceedings. Conference titles: ACM/SIGAPP Symposium on Applied Computing - SAC. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, FRAUDE NO COMÉRCIO E NA INDÚSTRIA, MÉTODOS PROBABILÍSTICOS
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LUZIO, Emanuele et al. Decoupling decision-making in fraud prevention through classifier calibration for business logic action. 2024, Anais.. New York: ACM, 2024. Disponível em: https://doi.org/10.1145/3605098.3636089. Acesso em: 06 nov. 2024.
APA
Luzio, E., Ponti, M. A., Ramirez Arevalo, C., & Argerich, L. (2024). Decoupling decision-making in fraud prevention through classifier calibration for business logic action. In Proceedings. New York: ACM. doi:10.1145/3605098.3636089
NLM
Luzio E, Ponti MA, Ramirez Arevalo C, Argerich L. Decoupling decision-making in fraud prevention through classifier calibration for business logic action [Internet]. Proceedings. 2024 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1145/3605098.3636089
Vancouver
Luzio E, Ponti MA, Ramirez Arevalo C, Argerich L. Decoupling decision-making in fraud prevention through classifier calibration for business logic action [Internet]. Proceedings. 2024 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1145/3605098.3636089
Trabalho de evento
A cross-dataset study on the Brazilian Sign Language translation (2023)
Sarmento, Amanda Hellen de Avellar; Ponti, Moacir Antonelli
Source: Proceedings. Conference titles: IEEE/CVF International Conference on Computer Vision Workshops - ICCVW. Unidade: ICMC
Subjects: APRENDIZAGEM PROFUNDA, RECONHECIMENTO DE IMAGEM, TRADUÇÃO AUTOMÁTICA, LÍNGUA BRASILEIRA DE SINAIS
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SARMENTO, Amanda Hellen de Avellar e PONTI, Moacir Antonelli. A cross-dataset study on the Brazilian Sign Language translation. 2023, Anais.. Los Alamitos: IEEE, 2023. Disponível em: https://doi.org/10.1109/ICCVW60793.2023.00300. Acesso em: 06 nov. 2024.
APA
Sarmento, A. H. de A., & Ponti, M. A. (2023). A cross-dataset study on the Brazilian Sign Language translation. In Proceedings. Los Alamitos: IEEE. doi:10.1109/ICCVW60793.2023.00300
NLM
Sarmento AH de A, Ponti MA. A cross-dataset study on the Brazilian Sign Language translation [Internet]. Proceedings. 2023 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1109/ICCVW60793.2023.00300
Vancouver
Sarmento AH de A, Ponti MA. A cross-dataset study on the Brazilian Sign Language translation [Internet]. Proceedings. 2023 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1109/ICCVW60793.2023.00300
Trabalho de evento
ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversion (2023)
Casanova, Edresson ; Shulby, Christopher Dane ; Korolev, Alexander; Candido Junior, Arnaldo ; Soares, Anderson da Silva ; Aluísio, Sandra Maria ; Ponti, Moacir Antonelli
Source: Proceedings. Conference titles: Annual Conference of the International Speech Communication Association - INTERSPEECH. Unidade: ICMC
Subjects: SÍNTESE DE FALA, PROCESSAMENTO DE LINGUAGEM NATURAL
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ABNT
CASANOVA, Edresson et al. ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversion. 2023, Anais.. Baixas: ISCA, 2023. Disponível em: https://doi.org/10.21437/Interspeech.2023-496. Acesso em: 06 nov. 2024.
APA
Casanova, E., Shulby, C. D., Korolev, A., Candido Junior, A., Soares, A. da S., Aluísio, S. M., & Ponti, M. A. (2023). ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversion. In Proceedings. Baixas: ISCA. doi:10.21437/Interspeech.2023-496
NLM
Casanova E, Shulby CD, Korolev A, Candido Junior A, Soares A da S, Aluísio SM, Ponti MA. ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversion [Internet]. Proceedings. 2023 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.21437/Interspeech.2023-496
Vancouver
Casanova E, Shulby CD, Korolev A, Candido Junior A, Soares A da S, Aluísio SM, Ponti MA. ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversion [Internet]. Proceedings. 2023 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.21437/Interspeech.2023-496
Trabalho de evento
Deep depth completion of low-cost sensor indoor RGB-D using Euclidean distance-based weighted loss and edge-aware refinement (2022)
Castro, Augusto Ribeiro ; Grassi Júnior, Valdir ; Ponti, Moacir Antonelli
Source: Proceedings. Conference titles: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - VISIGRAPP. Unidades: EESC, ICMC
Subjects: APRENDIZADO COMPUTACIONAL, VISÃO COMPUTACIONAL, PROCESSAMENTO DE IMAGENS, REDES NEURAIS
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CASTRO, Augusto Ribeiro e GRASSI JÚNIOR, Valdir e PONTI, Moacir Antonelli. Deep depth completion of low-cost sensor indoor RGB-D using Euclidean distance-based weighted loss and edge-aware refinement. 2022, Anais.. Lisboa: SciTePress, 2022. Disponível em: https://doi.org/10.5220/0010915300003124. Acesso em: 06 nov. 2024.
APA
Castro, A. R., Grassi Júnior, V., & Ponti, M. A. (2022). Deep depth completion of low-cost sensor indoor RGB-D using Euclidean distance-based weighted loss and edge-aware refinement. In Proceedings. Lisboa: SciTePress. doi:10.5220/0010915300003124
NLM
Castro AR, Grassi Júnior V, Ponti MA. Deep depth completion of low-cost sensor indoor RGB-D using Euclidean distance-based weighted loss and edge-aware refinement [Internet]. Proceedings. 2022 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.5220/0010915300003124
Vancouver
Castro AR, Grassi Júnior V, Ponti MA. Deep depth completion of low-cost sensor indoor RGB-D using Euclidean distance-based weighted loss and edge-aware refinement [Internet]. Proceedings. 2022 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.5220/0010915300003124
Trabalho de evento
SC-GlowTTS: an efficient zero-shot multi-speaker text-to-speech model (2021)
Casanova, Edresson ; Shulby, Christopher Dane ; Gölge, Eren; Müller, Nicolas Michael; Oliveira, Frederico Santos de; Candido Junior, Arnaldo ; Soares, Anderson da Silva ; Aluísio, Sandra Maria ; Ponti, Moacir Antonelli
Source: Proceedings. Conference titles: Annual Conference of the International Speech Communication Association - INTERSPEECH. Unidade: ICMC
Subjects: PROCESSAMENTO DE LINGUAGEM NATURAL, REDES NEURAIS, RECONHECIMENTO DE TEXTO, RECONHECIMENTO DE VOZ
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ABNT
CASANOVA, Edresson et al. SC-GlowTTS: an efficient zero-shot multi-speaker text-to-speech model. 2021, Anais.. Baixas: ISCA, 2021. Disponível em: https://doi.org/10.21437/Interspeech.2021-1774. Acesso em: 06 nov. 2024.
APA
Casanova, E., Shulby, C. D., Gölge, E., Müller, N. M., Oliveira, F. S. de, Candido Junior, A., et al. (2021). SC-GlowTTS: an efficient zero-shot multi-speaker text-to-speech model. In Proceedings. Baixas: ISCA. doi:10.21437/Interspeech.2021-1774
NLM
Casanova E, Shulby CD, Gölge E, Müller NM, Oliveira FS de, Candido Junior A, Soares A da S, Aluísio SM, Ponti MA. SC-GlowTTS: an efficient zero-shot multi-speaker text-to-speech model [Internet]. Proceedings. 2021 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.21437/Interspeech.2021-1774
Vancouver
Casanova E, Shulby CD, Gölge E, Müller NM, Oliveira FS de, Candido Junior A, Soares A da S, Aluísio SM, Ponti MA. SC-GlowTTS: an efficient zero-shot multi-speaker text-to-speech model [Internet]. Proceedings. 2021 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.21437/Interspeech.2021-1774
Trabalho de evento
Training deep networks from zero to hero: avoiding pitfalls and going beyond (2021)
Ponti, Moacir Antonelli ; Santos, Fernando Pereira dos ; Ribeiro, Leo Sampaio Ferraz ; Cavallari, Gabriel Biscaro
Source: Proceedings. Conference titles: Conference on Graphics, Patterns and Images - SIBGRAPI. Unidade: ICMC
Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE IMAGEM
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ABNT
PONTI, Moacir Antonelli et al. Training deep networks from zero to hero: avoiding pitfalls and going beyond. 2021, Anais.. Los Alamitos: IEEE, 2021. Disponível em: https://doi.org/10.1109/SIBGRAPI54419.2021.00011. Acesso em: 06 nov. 2024.
APA
Ponti, M. A., Santos, F. P. dos, Ribeiro, L. S. F., & Cavallari, G. B. (2021). Training deep networks from zero to hero: avoiding pitfalls and going beyond. In Proceedings. Los Alamitos: IEEE. doi:10.1109/SIBGRAPI54419.2021.00011
NLM
Ponti MA, Santos FP dos, Ribeiro LSF, Cavallari GB. Training deep networks from zero to hero: avoiding pitfalls and going beyond [Internet]. Proceedings. 2021 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1109/SIBGRAPI54419.2021.00011
Vancouver
Ponti MA, Santos FP dos, Ribeiro LSF, Cavallari GB. Training deep networks from zero to hero: avoiding pitfalls and going beyond [Internet]. Proceedings. 2021 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1109/SIBGRAPI54419.2021.00011
Trabalho de evento
Semi-supervised siamese network using self-supervision under scarce annotation improves class separability and robustness to attack (2021)
Cavallari, Gabriel Biscaro; Ponti, Moacir Antonelli
Source: Proceedings. Conference titles: Conference on Graphics, Patterns and Images - SIBGRAPI. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE IMAGEM
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ABNT
CAVALLARI, Gabriel Biscaro e PONTI, Moacir Antonelli. Semi-supervised siamese network using self-supervision under scarce annotation improves class separability and robustness to attack. 2021, Anais.. Los Alamitos: IEEE, 2021. Disponível em: https://doi.org/10.1109/SIBGRAPI54419.2021.00038. Acesso em: 06 nov. 2024.
APA
Cavallari, G. B., & Ponti, M. A. (2021). Semi-supervised siamese network using self-supervision under scarce annotation improves class separability and robustness to attack. In Proceedings. Los Alamitos: IEEE. doi:10.1109/SIBGRAPI54419.2021.00038
NLM
Cavallari GB, Ponti MA. Semi-supervised siamese network using self-supervision under scarce annotation improves class separability and robustness to attack [Internet]. Proceedings. 2021 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1109/SIBGRAPI54419.2021.00038
Vancouver
Cavallari GB, Ponti MA. Semi-supervised siamese network using self-supervision under scarce annotation improves class separability and robustness to attack [Internet]. Proceedings. 2021 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1109/SIBGRAPI54419.2021.00038
Trabalho de evento
Transfer learning and data augmentation techniques to the COVID-19 identification tasks in ComParE 2021 (2021)
Casanova, Edresson ; Candido Junior, Arnaldo ; Fernandes Jr, Ricardo Corso; Finger, Marcelo ; Gris, Lucas Rafael Stefanel ; Ponti, Moacir Antonelli ; Silva, Daniel Peixoto Pinto da
Source: Proceedings. Conference titles: Annual Conference of the International Speech Communication Association - INTERSPEECH. Unidades: IME, ICMC
Subjects: LINGUÍSTICA COMPUTACIONAL, PROCESSAMENTO DE LINGUAGEM NATURAL, APRENDIZAGEM PROFUNDA, REDES NEURAIS, COVID-19
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ABNT
CASANOVA, Edresson et al. Transfer learning and data augmentation techniques to the COVID-19 identification tasks in ComParE 2021. 2021, Anais.. Baixas: ISCA, 2021. Disponível em: https://doi.org/10.21437/Interspeech.2021-1798. Acesso em: 06 nov. 2024.
APA
Casanova, E., Candido Junior, A., Fernandes Jr, R. C., Finger, M., Gris, L. R. S., Ponti, M. A., & Silva, D. P. P. da. (2021). Transfer learning and data augmentation techniques to the COVID-19 identification tasks in ComParE 2021. In Proceedings. Baixas: ISCA. doi:10.21437/Interspeech.2021-1798
NLM
Casanova E, Candido Junior A, Fernandes Jr RC, Finger M, Gris LRS, Ponti MA, Silva DPP da. Transfer learning and data augmentation techniques to the COVID-19 identification tasks in ComParE 2021 [Internet]. Proceedings. 2021 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.21437/Interspeech.2021-1798
Vancouver
Casanova E, Candido Junior A, Fernandes Jr RC, Finger M, Gris LRS, Ponti MA, Silva DPP da. Transfer learning and data augmentation techniques to the COVID-19 identification tasks in ComParE 2021 [Internet]. Proceedings. 2021 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.21437/Interspeech.2021-1798
Trabalho de evento
Investigating 3D convolutional layers as feature extractors for anomaly detection systems applied to surveillance videos (2021)
Nazaré, Tiago Santana de ; Mello, Rodrigo Fernandes de ; Ponti, Moacir Antonelli
Source: Proceedings. Conference titles: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - VISIGRAPP. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, RECONHECIMENTO DE IMAGEM, VÍDEO
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NAZARÉ, Tiago Santana de e MELLO, Rodrigo Fernandes de e PONTI, Moacir Antonelli. Investigating 3D convolutional layers as feature extractors for anomaly detection systems applied to surveillance videos. 2021, Anais.. Lisboa: SciTePress, 2021. Disponível em: https://doi.org/10.5220/0010347907530762. Acesso em: 06 nov. 2024.
APA
Nazaré, T. S. de, Mello, R. F. de, & Ponti, M. A. (2021). Investigating 3D convolutional layers as feature extractors for anomaly detection systems applied to surveillance videos. In Proceedings. Lisboa: SciTePress. doi:10.5220/0010347907530762
NLM
Nazaré TS de, Mello RF de, Ponti MA. Investigating 3D convolutional layers as feature extractors for anomaly detection systems applied to surveillance videos [Internet]. Proceedings. 2021 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.5220/0010347907530762
Vancouver
Nazaré TS de, Mello RF de, Ponti MA. Investigating 3D convolutional layers as feature extractors for anomaly detection systems applied to surveillance videos [Internet]. Proceedings. 2021 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.5220/0010347907530762
Trabalho de evento
Interactive protocol for acquisition of migraine diaries with a mobile app and machine learning data analysis (2021)
Alves, Tatiane Vieira; Rodrigues, Kamila Rios da Hora ; Ponti, Moacir Antonelli
Source: Proceedings. Conference titles: Brazilian Symposium on Human Factors in Computing Systems - IHC. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, INTERAÇÃO USUÁRIO-COMPUTADOR, ENXAQUECA, TECNOLOGIAS DA SAÚDE
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ABNT
ALVES, Tatiane Vieira e RODRIGUES, Kamila Rios da Hora e PONTI, Moacir Antonelli. Interactive protocol for acquisition of migraine diaries with a mobile app and machine learning data analysis. 2021, Anais.. New York: ACM, 2021. Disponível em: https://doi.org/10.1145/3472301.3484322. Acesso em: 06 nov. 2024.
APA
Alves, T. V., Rodrigues, K. R. da H., & Ponti, M. A. (2021). Interactive protocol for acquisition of migraine diaries with a mobile app and machine learning data analysis. In Proceedings. New York: ACM. doi:10.1145/3472301.3484322
NLM
Alves TV, Rodrigues KR da H, Ponti MA. Interactive protocol for acquisition of migraine diaries with a mobile app and machine learning data analysis [Internet]. Proceedings. 2021 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1145/3472301.3484322
Vancouver
Alves TV, Rodrigues KR da H, Ponti MA. Interactive protocol for acquisition of migraine diaries with a mobile app and machine learning data analysis [Internet]. Proceedings. 2021 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1145/3472301.3484322
Trabalho de evento
Data augmentation guidelines for cross-dataset transfer learning and pseudo labeling (2021)
Santos, Fernando Pereira dos ; Thumé, Gabriela Salvador; Ponti, Moacir Antonelli
Source: Proceedings. Conference titles: Conference on Graphics, Patterns and Images - SIBGRAPI. Unidade: ICMC
Subjects: REDES NEURAIS, RECONHECIMENTO DE IMAGEM, APRENDIZADO COMPUTACIONAL
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SANTOS, Fernando Pereira dos e THUMÉ, Gabriela Salvador e PONTI, Moacir Antonelli. Data augmentation guidelines for cross-dataset transfer learning and pseudo labeling. 2021, Anais.. Los Alamitos: IEEE, 2021. Disponível em: https://doi.org/10.1109/SIBGRAPI54419.2021.00036. Acesso em: 06 nov. 2024.
APA
Santos, F. P. dos, Thumé, G. S., & Ponti, M. A. (2021). Data augmentation guidelines for cross-dataset transfer learning and pseudo labeling. In Proceedings. Los Alamitos: IEEE. doi:10.1109/SIBGRAPI54419.2021.00036
NLM
Santos FP dos, Thumé GS, Ponti MA. Data augmentation guidelines for cross-dataset transfer learning and pseudo labeling [Internet]. Proceedings. 2021 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1109/SIBGRAPI54419.2021.00036
Vancouver
Santos FP dos, Thumé GS, Ponti MA. Data augmentation guidelines for cross-dataset transfer learning and pseudo labeling [Internet]. Proceedings. 2021 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1109/SIBGRAPI54419.2021.00036
Trabalho de evento
Scene Designer: a unified model for scene search and synthesis from sketch (2021)
Ribeiro, Leo Sampaio Ferraz ; Bui, Tu ; Collomosse, John ; Ponti, Moacir Antonelli
Source: Proceedings. Conference titles: IEEE/CVF International Conference on Computer Vision Workshops - ICCVW. Unidade: ICMC
Subjects: RECONHECIMENTO DE IMAGEM, REDES NEURAIS, APRENDIZADO COMPUTACIONAL
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ABNT
RIBEIRO, Leo Sampaio Ferraz et al. Scene Designer: a unified model for scene search and synthesis from sketch. 2021, Anais.. Los Alamitos: IEEE, 2021. Disponível em: https://doi.org/10.1109/ICCVW54120.2021.00275. Acesso em: 06 nov. 2024.
APA
Ribeiro, L. S. F., Bui, T., Collomosse, J., & Ponti, M. A. (2021). Scene Designer: a unified model for scene search and synthesis from sketch. In Proceedings. Los Alamitos: IEEE. doi:10.1109/ICCVW54120.2021.00275
NLM
Ribeiro LSF, Bui T, Collomosse J, Ponti MA. Scene Designer: a unified model for scene search and synthesis from sketch [Internet]. Proceedings. 2021 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1109/ICCVW54120.2021.00275
Vancouver
Ribeiro LSF, Bui T, Collomosse J, Ponti MA. Scene Designer: a unified model for scene search and synthesis from sketch [Internet]. Proceedings. 2021 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1109/ICCVW54120.2021.00275
Trabalho de evento
Sketchformer: transformer-based representation for sketched structure (2020)
Ribeiro, Leo Sampaio Ferraz ; Bui, Tu ; Collomosse, John ; Ponti, Moacir Antonelli
Source: Proceedings. Conference titles: IEEE/CVF Conference on Computer Vision and Pattern Recognition - CVPR. Unidade: ICMC
Subjects: PROCESSAMENTO DE IMAGENS, RECONHECIMENTO DE IMAGEM, COMPUTAÇÃO GRÁFICA
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ABNT
RIBEIRO, Leo Sampaio Ferraz et al. Sketchformer: transformer-based representation for sketched structure. 2020, Anais.. Los Alamitos: IEEE, 2020. Disponível em: https://doi.org/10.1109/CVPR42600.2020.01416. Acesso em: 06 nov. 2024.
APA
Ribeiro, L. S. F., Bui, T., Collomosse, J., & Ponti, M. A. (2020). Sketchformer: transformer-based representation for sketched structure. In Proceedings. Los Alamitos: IEEE. doi:10.1109/CVPR42600.2020.01416
NLM
Ribeiro LSF, Bui T, Collomosse J, Ponti MA. Sketchformer: transformer-based representation for sketched structure [Internet]. Proceedings. 2020 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1109/CVPR42600.2020.01416
Vancouver
Ribeiro LSF, Bui T, Collomosse J, Ponti MA. Sketchformer: transformer-based representation for sketched structure [Internet]. Proceedings. 2020 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1109/CVPR42600.2020.01416
Trabalho de evento
Alignment of local and global features from multiple layers of convolutional neural network for image classification (2019)
Santos, Fernando Pereira dos ; Ponti, Moacir Antonelli
Source: Proceedings. Conference titles: Conference on Graphics, Patterns and Images - SIBGRAPI. Unidade: ICMC
Subjects: REDES NEURAIS, RECONHECIMENTO DE IMAGEM
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ABNT
SANTOS, Fernando Pereira dos e PONTI, Moacir Antonelli. Alignment of local and global features from multiple layers of convolutional neural network for image classification. 2019, Anais.. Los Alamitos: IEEE, 2019. Disponível em: https://doi.org/10.1109/SIBGRAPI.2019.00040. Acesso em: 06 nov. 2024.
APA
Santos, F. P. dos, & Ponti, M. A. (2019). Alignment of local and global features from multiple layers of convolutional neural network for image classification. In Proceedings. Los Alamitos: IEEE. doi:10.1109/SIBGRAPI.2019.00040
NLM
Santos FP dos, Ponti MA. Alignment of local and global features from multiple layers of convolutional neural network for image classification [Internet]. Proceedings. 2019 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1109/SIBGRAPI.2019.00040
Vancouver
Santos FP dos, Ponti MA. Alignment of local and global features from multiple layers of convolutional neural network for image classification [Internet]. Proceedings. 2019 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1109/SIBGRAPI.2019.00040
Trabalho de evento
Color quantization in transfer learning and noisy scenarios: an empirical analysis using convolutional networks (2018)
Nazaré, Tiago Santana de ; Costa, Gabriel B. Paranhos da; Mello, Rodrigo Fernandes de; Ponti, Moacir Antonelli
Source: Proceedings. Conference titles: Conference on Graphics, Patterns and Images - SIBGRAPI. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, VISÃO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
NAZARÉ, Tiago Santana de et al. Color quantization in transfer learning and noisy scenarios: an empirical analysis using convolutional networks. 2018, Anais.. Los Alamitos: IEEE, 2018. Disponível em: https://doi.org/10.1109/SIBGRAPI.2018.00053. Acesso em: 06 nov. 2024.
APA
Nazaré, T. S. de, Costa, G. B. P. da, Mello, R. F. de, & Ponti, M. A. (2018). Color quantization in transfer learning and noisy scenarios: an empirical analysis using convolutional networks. In Proceedings. Los Alamitos: IEEE. doi:10.1109/SIBGRAPI.2018.00053
NLM
Nazaré TS de, Costa GBP da, Mello RF de, Ponti MA. Color quantization in transfer learning and noisy scenarios: an empirical analysis using convolutional networks [Internet]. Proceedings. 2018 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1109/SIBGRAPI.2018.00053
Vancouver
Nazaré TS de, Costa GBP da, Mello RF de, Ponti MA. Color quantization in transfer learning and noisy scenarios: an empirical analysis using convolutional networks [Internet]. Proceedings. 2018 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1109/SIBGRAPI.2018.00053
Trabalho de evento
Robust feature spaces from pre-trained deep network layers for skin lesion classification (2018)
Santos, Fernando Pereira dos ; Ponti, Moacir Antonelli
Source: Proceedings. Conference titles: Conference on Graphics, Patterns and Images - SIBGRAPI. Unidade: ICMC
Subjects: TECNOLOGIAS DA SAÚDE, RECONHECIMENTO DE IMAGEM, APRENDIZADO COMPUTACIONAL, NEOPLASIAS CUTÂNEAS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SANTOS, Fernando Pereira dos e PONTI, Moacir Antonelli. Robust feature spaces from pre-trained deep network layers for skin lesion classification. 2018, Anais.. Los Alamitos: IEEE, 2018. Disponível em: https://doi.org/10.1109/SIBGRAPI.2018.00031. Acesso em: 06 nov. 2024.
APA
Santos, F. P. dos, & Ponti, M. A. (2018). Robust feature spaces from pre-trained deep network layers for skin lesion classification. In Proceedings. Los Alamitos: IEEE. doi:10.1109/SIBGRAPI.2018.00031
NLM
Santos FP dos, Ponti MA. Robust feature spaces from pre-trained deep network layers for skin lesion classification [Internet]. Proceedings. 2018 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1109/SIBGRAPI.2018.00031
Vancouver
Santos FP dos, Ponti MA. Robust feature spaces from pre-trained deep network layers for skin lesion classification [Internet]. Proceedings. 2018 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1109/SIBGRAPI.2018.00031
Trabalho de evento
Unsupervised representation learning using convolutional and stacked auto-encoders: a domain and cross-domain feature space analysis (2018)
Cavallari, Gabriel B; Ribeiro, Leonardo Sampaio F; Ponti, Moacir Antonelli
Source: Proceedings. Conference titles: Conference on Graphics, Patterns and Images - SIBGRAPI. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, PROCESSAMENTO DE IMAGENS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CAVALLARI, Gabriel B e RIBEIRO, Leonardo Sampaio F e PONTI, Moacir Antonelli. Unsupervised representation learning using convolutional and stacked auto-encoders: a domain and cross-domain feature space analysis. 2018, Anais.. Los Alamitos: IEEE, 2018. Disponível em: https://doi.org/10.1109/SIBGRAPI.2018.00063. Acesso em: 06 nov. 2024.
APA
Cavallari, G. B., Ribeiro, L. S. F., & Ponti, M. A. (2018). Unsupervised representation learning using convolutional and stacked auto-encoders: a domain and cross-domain feature space analysis. In Proceedings. Los Alamitos: IEEE. doi:10.1109/SIBGRAPI.2018.00063
NLM
Cavallari GB, Ribeiro LSF, Ponti MA. Unsupervised representation learning using convolutional and stacked auto-encoders: a domain and cross-domain feature space analysis [Internet]. Proceedings. 2018 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1109/SIBGRAPI.2018.00063
Vancouver
Cavallari GB, Ribeiro LSF, Ponti MA. Unsupervised representation learning using convolutional and stacked auto-encoders: a domain and cross-domain feature space analysis [Internet]. Proceedings. 2018 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1109/SIBGRAPI.2018.00063
Trabalho de evento
Everything you wanted to know about deep learning for computer vision but were afraid to ask (2017)
Ponti, Moacir Antonelli ; Ribeiro, Leonardo S. F; Nazaré, Tiago Santana de ; Bui, Tu; Collomosse, John
Source: Proceedings. Conference titles: Conference on Graphics, Patterns and Images - SIBGRAPI. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, VISÃO COMPUTACIONAL, PROCESSAMENTO DE IMAGENS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
PONTI, Moacir Antonelli et al. Everything you wanted to know about deep learning for computer vision but were afraid to ask. 2017, Anais.. Los Alamitos: IEEE, 2017. Disponível em: https://doi.org/10.1109/SIBGRAPI-T.2017.12. Acesso em: 06 nov. 2024.
APA
Ponti, M. A., Ribeiro, L. S. F., Nazaré, T. S. de, Bui, T., & Collomosse, J. (2017). Everything you wanted to know about deep learning for computer vision but were afraid to ask. In Proceedings. Los Alamitos: IEEE. doi:10.1109/SIBGRAPI-T.2017.12
NLM
Ponti MA, Ribeiro LSF, Nazaré TS de, Bui T, Collomosse J. Everything you wanted to know about deep learning for computer vision but were afraid to ask [Internet]. Proceedings. 2017 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1109/SIBGRAPI-T.2017.12
Vancouver
Ponti MA, Ribeiro LSF, Nazaré TS de, Bui T, Collomosse J. Everything you wanted to know about deep learning for computer vision but were afraid to ask [Internet]. Proceedings. 2017 ;[citado 2024 nov. 06 ] Available from: https://doi.org/10.1109/SIBGRAPI-T.2017.12

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