94 citations · 163 across the 15 of their papers we have counts for
19 papers
Continual Facial Expression Recognition: A Benchmark
Nikhil Churamani, Tolga Dimlioglu, German I. Parisi +1
Understanding human affective behaviour, especially in the dynamics of real-world settings, requires Facial Expression Recognition (FER) models to continuously adapt to individual…
Avalanche: an End-to-End Library for Continual Learning
Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu +25
Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing…
CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions
Vincenzo Lomonaco, Lorenzo Pellegrini, Pau Rodriguez +12
In the last few years, we have witnessed a renewed and fast-growing interest in continual learning with deep neural networks with the shared objective of making current AI systems…
IROS 2019 Lifelong Robotic Vision Challenge -- Lifelong Object Recognition Report
Qi She, Fan Feng, Qi Liu +33
This report summarizes IROS 2019-Lifelong Robotic Vision Competition (Lifelong Object Recognition Challenge) with methods and results from the top finalists (out of over~…
Online Continual Learning on Sequences
German I. Parisi, Vincenzo Lomonaco
Online continual learning (OCL) refers to the ability of a system to learn over time from a continuous stream of data without having to revisit previously encountered training samp…
Human Action Recognition and Assessment via Deep Neural Network Self-Organization
German I. Parisi
The robust recognition and assessment of human actions are crucial in human-robot interaction (HRI) domains. While state-of-the-art models of action perception show remarkable resu…
Rethinking Continual Learning for Autonomous Agents and Robots
German I. Parisi, Christopher Kanan
Continual learning refers to the ability of a biological or artificial system to seamlessly learn from continuous streams of information while preventing catastrophic forgetting, i…
A Personalized Affective Memory Neural Model for Improving Emotion Recognition
Pablo Barros, German I. Parisi, Stefan Wermter
Recent models of emotion recognition strongly rely on supervised deep learning solutions for the distinction of general emotion expressions. However, they are not reliable when rec…
On the role of neurogenesis in overcoming catastrophic forgetting
German I. Parisi, Xu Ji, Stefan Wermter
Lifelong learning capabilities are crucial for artificial autonomous agents operating on real-world data, which is typically non-stationary and temporally correlated. In this work,…
Assessing the Contribution of Semantic Congruency to Multisensory Integration and Conflict Resolution
Di Fu, Pablo Barros, German I. Parisi +4
The efficient integration of multisensory observations is a key property of the brain that yields the robust interaction with the environment. However, artificial multisensory perc…
Multi-modal Feedback for Affordance-driven Interactive Reinforcement Learning
Francisco Cruz, German I. Parisi, Stefan Wermter
Interactive reinforcement learning (IRL) extends traditional reinforcement learning (RL) by allowing an agent to interact with parent-like trainers during a task. In this paper, we…
Towards Modeling the Interaction of Spatial-Associative Neural Network Representations for Multisensory Perception
German I. Parisi, Jonathan Tong, Pablo Barros +2
Our daily perceptual experience is driven by different neural mechanisms that yield multisensory interaction as the interplay between exogenous stimuli and endogenous expectations.…
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization
German I. Parisi, Jun Tani, Cornelius Weber +1
Artificial autonomous agents and robots interacting in complex environments are required to continually acquire and fine-tune knowledge over sustained periods of time. The ability…
Closing the loop on multisensory interactions: A neural architecture for multisensory causal inference and recalibration
Jonathan Tong, German I. Parisi, Stefan Wermter +1
When the brain receives input from multiple sensory systems, it is faced with the question of whether it is appropriate to process the inputs in combination, as if they originated…
A Neurorobotic Experiment for Crossmodal Conflict Resolution in Complex Environments
German I. Parisi, Pablo Barros, Di Fu +4
Crossmodal conflict resolution is crucial for robot sensorimotor coupling through the interaction with the environment, yielding swift and robust behaviour also in noisy conditions…
Continual Lifelong Learning with Neural Networks: A Review
German I. Parisi, Ronald Kemker, Jose L. Part +2
Humans and animals have the ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning,…
Expectation Learning for Adaptive Crossmodal Stimuli Association
Pablo Barros, German I. Parisi, Di Fu +2
The human brain is able to learn, generalize, and predict crossmodal stimuli. Learning by expectation fine-tunes crossmodal processing at different levels, thus enhancing our power…
An Incremental Self-Organizing Architecture for Sensorimotor Learning and Prediction
Luiza Mici, German I. Parisi, Stefan Wermter
During visuomotor tasks, robots must compensate for temporal delays inherent in their sensorimotor processing systems. Delay compensation becomes crucial in a dynamic environment w…
A self-organizing neural network architecture for learning human-object interactions
Luiza Mici, German I. Parisi, Stefan Wermter
The visual recognition of transitive actions comprising human-object interactions is a key component for artificial systems operating in natural environments. This challenging task…