Dustin Carrión-Ojeda

I'm a PhD student at the Image and Video Analysis Group (IVA) at TU Darmstadt, under the supervision of Prof. Dr. Sc. Simone Schaub-Meyer. My research focuses on low-shot and multimodal learning.

I received my MSc in Artificial Intelligence from Université Paris-Saclay, where I worked with Prof. Isabelle Guyon, and my BEng in Information Technology from Yachay Tech University, where I was advised by Prof. Israel Pineda and Prof. Rigoberto Fonseca-Delgado.

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Research

I'm currently focused on learning from limited data and multiple modalities. My main research interests include developing meta-learning methods for image classification and segmentation, as well as multi-modal techniques for video understanding. Additionally, I have experience working with EEG-based biometric systems.

Publications

	Evaluation of features and channels of electroencephalographic signals for biometric systems Dustin Carrión-Ojeda, Paola Martínez-Arias, Rigoberto Fonseca-Delgado, Israel Pineda, Héctor Mejía-Vallejo 2024 Accepted to EURASIP Journal on Advances in Signal Processing [Paper] An analysis of EEG-based biometrics identifying the most relevant features and channels, reducing required electrodes from 32 to 11 while maintaining performance, with standard deviation from wavelet coefficients as the best feature.
	RADENN: A domain-specific language for the RApid DEvelopment of Neural Networks Israel Pineda, Dustin Carrión-Ojeda, Rigoberto Fonseca-Delgado 2023 Accepted to IEEE Access [Paper \| Code] A domain-specific language built on Keras, designed for fast and accessible neural network development with minimal coding.
	NeurIPS'22 Cross-Domain MetaDL Challenge: Results and lessons learned Dustin Carrión-Ojeda, et al. 2022 Accepted to PMLR [Paper \| Code] An analysis of the NeurIPS'22 Cross-Domain MetaDL Challenge results, emphasizing the impact of pre-trained backbones, overfitting prevention, and domain adaptation techniques in improving meta-learning performance across 10 domains.
	Meta-Album: Multi-domain meta-dataset for few-shot image classification Ihsan Ullah, Dustin Carrión-Ojeda, Sergio Escalera, Isabelle Guyon, Mike Huisman, Felix Mohr, Jan N. van Rijn, Haozhe Sun, Joaquin Vanschoren, Phan Anh Vu 2022 NeurIPS D&B Track [Paper \| arXiv \| Code] A diverse meta-dataset designed for few-shot image classification and other applications, featuring 40 curated datasets from 10 domains, standardized formatting, and multiple dataset versions.
	NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results Dustin Carrión-Ojeda, Hong Chen, Adrian El Baz, Segio Escalera, Chaoyu Guan, Isabelle Guyon, Ihsan Ullah, Xin Wang, Wenwu Zhu 2022 ECML/PKDD Workshop: Meta-Knowledge Transfer [Paper \| arXiv \| Code] A challenge on "any-way any-shot" image classification in a cross-domain setting. Winning solutions were blind-tested and open-sourced for broader impact.
	Performance evaluation of dissemination protocols over vehicular networks for an automatic speed fine system Dustin Carrión-Ojeda, Cristhian Iza, Mónica Aguilar Igartua 2021 Accepted to IEEE Access [Paper] An automated system for issuing fines for speed limit violations using vehicular networks, featuring a dissemination protocol that achieves approximately 95% fine delivery in urban scenarios.
	EBAPy: A Python framework for analyzing the factors that have an influence in the performance of EEG-based applications Dustin Carrión-Ojeda, Paola Martínez-Arias, Rigoberto Fonseca-Delgado, Israel Pineda 2021 Accepted to Software Impacts [Paper \| Code] A user-friendly Python framework for developing EEG-based applications, enabling in-depth analysis of factors that influence the performance of the system and its computational cost.
	Analysis of factors that influence the performance of biometric systems based on EEG signals Dustin Carrión-Ojeda, Rigoberto Fonseca-Delgado, Israel Pineda 2021 Accepted to Expert Systems with Applications [Paper \| Code] An analysis of factors influencing the performance of EEG-based biometrics, showing that EEG recording time significantly impacts classifier accuracy.
	A method for studying how much time of EEG recording is needed to have a good user identification. Dustin Carrión-Ojeda, Héctor Mejía-Vallejo, Rigoberto Fonseca-Delgado, Pilar Gómez-Gil, Manuel Ramírez-Cortés 2019 IEEE LA-CCI [Paper] A novel EEG-based biometric system that uses Discrete Wavelet Transform (DWT) for feature extraction and achieves approximately 90% accuracy with just 2 seconds of EEG recording.

Posters

Analysis of meta-learning methods in a more realistic cross-domain scenario
Dustin Carrión-Ojeda, Stefan Roth, Simone Schaub-Meyer
2023 ICCV Workshop: LatinX in Artificial Intelligence Research

Results of the NeurIPS'22 Cross-Domain MetaDL competition
Dustin Carrión-Ojeda, Ihsan Ullah, Sergio Escalera, Isabelle Guyon, Felix Mohr, Manh Hung Nguyen, Joaquin Vanschoren
2022 NeurIPS Competition Track

Analysis of factors that influence the performance of biometric systems based on EEG signals
Dustin Carrión-Ojeda, Rigoberto Fonseca-Delgado, Israel Pineda
2020 NeurIPS Workshop: LatinX in Artificial Intelligence Research

Biometric system based on electroencephalogram analysis
Dustin Carrión-Ojeda, Héctor Mejía-Vallejo, Rigoberto Fonseca-Delgado, Pilar Gómez-Gil, Manuel Ramírez-Cortés
2019 NeurIPS Workshop: LatinX in Artificial Intelligence Research

Based on the design and source code from Jon Barron's website