Topic
incremental learning
New Framework for Class-Incremental Motion Forecasting Enables Autonomous Vehicles to Adapt to Novel Objects
Researchers introduce class-incremental motion forecasting, a setting where autonomous vehicles learn new object classes over time. They propose the first end-to-end framework that adapts to novel classes while mitigating catastrophic forgetting, using pseudo-labels and open-vocabulary segmentation. Evaluations on nuScenes and Argoverse 2 show preserved performance on known classes and effective adaptation to new ones.
Prototype Adaptation and Pseudo Class-Variable Training Boost Few-Shot Audio Classification
Researchers propose a method for few-shot class-variable incremental audio classification, handling both increases and decreases in the number of classes. The approach uses a prototype adaptation network and pseudo class-variable training. Experiments on three public datasets show improved average accuracy over previous methods.