Official repository for the paper "Mask and Compress: Efficient Skeleton-based Action Recognition in Continual Learning" (ICPR 2024). This repository is based on the original Mammoth. Backbones, datasets and models are kept for compatibility with the original repository.

Mammoth - An Extendible (General) Continual Learning Framework for Pytorch

Setup for CHARON

• Use ./utils/main.py to run experiments.
• Use argument --model CHARON to run our model.

Models

• Continual Human Action Recognition On skeletoNs (CHARON)
• eXtended-DER (X-DER)
• Dark Experience Replay (DER)
• Dark Experience Replay++ (DER++)
• Learning a Unified Classifier Incrementally via Rebalancing (LUCIR)
• Greedy Sampler and Dumb Learner (GDumb)
• Bias Correction (BiC)
• Regular Polytope Classifier (RPC)
• Gradient Episodic Memory (GEM)
• Averaged GEM (A-GEM)
• A-GEM with Reservoir (A-GEM-R)
• Experience Replay (ER)
• Meta-Experience Replay (MER)
• Function Distance Regularization (FDR)
• Greedy gradient-based Sample Selection (GSS)
• Hindsight Anchor Learning (HAL)
• Incremental Classifier and Representation Learning (iCaRL)
• online Elastic Weight Consolidation (oEWC)
• Synaptic Intelligence (SI)
• Learning without Forgetting (LwF)
• Progressive Neural Networks (PNN)

Datasets

• Sequential NTU-60 (Class-Il / Task-IL)
• Sequential NTU-120 (Class-Il / Task-IL)
• Sequential MNIST (Class-Il / Task-IL)
• Sequential CIFAR-10 (Class-Il / Task-IL)
• Sequential CIFAR-100 (Class-Il / Task-IL)
• Sequential Tiny ImageNet (Class-Il / Task-IL)
• Permuted MNIST (Domain-IL)
• Rotated MNIST (Domain-IL)
• MNIST-360 (General Continual Learning)

Citing this work

@inproceedings{mosconi2024mask,
  title={Mask and Compress: Efficient Skeleton-based Action Recognition in Continual Learning},
  author={Mosconi, Matteo and Sorokin, Andriy and Panariello, Aniello and Porrello, Angelo and Bonato, Jacopo and Cotogni, Marco and Sabetta, Luigi and Calderara, Simone and Cucchiara, Rita},
  booktitle={International Conference on Pattern Recognition},
  year=2024
}