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Found 6 papers, 2 papers with code
Cross-Class Feature Augmentation for Class Incremental Learning
The proposed approach has a unique perspective to utilize the previous knowledge in class incremental learning since it augments features of arbitrary target classes using examples in other classes via adversarial attacks on a previously learned classifier.
Multi-Modal Representation Learning with Text-Driven Soft Masks
We propose a visual-linguistic representation learning approach within a self-supervised learning framework by introducing a new operation, loss, and data augmentation strategy.
Class-Incremental Learning by Knowledge Distillation with Adaptive Feature Consolidation
We present a novel class incremental learning approach based on deep neural networks, which continually learns new tasks with limited memory for storing examples in the previous tasks.
Class-Incremental Learning for Action Recognition in Videos
We tackle catastrophic forgetting problem in the context of class-incremental learning for video recognition, which has not been explored actively despite the popularity of continual learning.
Learning to Adapt to Unseen Abnormal Activities under Weak Supervision
We present a meta-learning framework for weakly supervised anomaly detection in videos, where the detector learns to adapt to unseen types of abnormal activities effectively when only video-level annotations of binary labels are available.
Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud
We propose a local-to-global representation learning algorithm for 3D point cloud data, which is appropriate to handle various geometric transformations, especially rotation, without explicit data augmentation with respect to the transformations.
