59 citations · 154 across the 18 of their papers we have counts for
9 papers · 1 filter
Graph Neural Networks for Surgical Scene Segmentation
Yihan Li, Nikhil Churamani, Maria Robu +2
Purpose: Accurate identification of hepatocystic anatomy is critical to preventing surgical complications during laparoscopic cholecystectomy. Deep learning models often struggle w…
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…
Towards Fair Affective Robotics: Continual Learning for Mitigating Bias in Facial Expression and Action Unit Recognition
Ozgur Kara, Nikhil Churamani, Hatice Gunes
As affective robots become integral in human life, these agents must be able to fairly evaluate human affective expressions without discriminating against specific demographic grou…
Domain-Incremental Continual Learning for Mitigating Bias in Facial Expression and Action Unit Recognition
Nikhil Churamani, Ozgur Kara, Hatice Gunes
As Facial Expression Recognition (FER) systems become integrated into our daily lives, these systems need to prioritise making fair decisions instead of aiming at higher individual…
Spatio-Temporal Analysis of Facial Actions using Lifecycle-Aware Capsule Networks
Nikhil Churamani, Sinan Kalkan, Hatice Gunes
Most state-of-the-art approaches for Facial Action Unit (AU) detection rely upon evaluating facial expressions from static frames, encoding a snapshot of heightened facial activity…
The FaceChannel: A Fast & Furious Deep Neural Network for Facial Expression Recognition
Pablo Barros, Nikhil Churamani, Alessandra Sciutti
Current state-of-the-art models for automatic Facial Expression Recognition (FER) are based on very deep neural networks that are effective but rather expensive to train. Given the…
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…
Continual Learning for Affective Computing
Nikhil Churamani
Real-world application requires affect perception models to be sensitive to individual differences in expression. As each user is different and expresses differently, these models…
The FaceChannel: A Light-weight Deep Neural Network for Facial Expression Recognition
Pablo Barros, Nikhil Churamani, Alessandra Sciutti
Current state-of-the-art models for automatic FER are based on very deep neural networks that are difficult to train. This makes it challenging to adapt these models to changing co…