AG 1, INET, AG 5, RG1, SWS, AG 2, AG 4, D6, AG 3   Info

Public Audience

English

60 Minutes

Saarbrücken

In recent years, artificial intelligence (AI) has achieved great success in many fields. Although impressive advances have been made, AI algorithms still suffer from an important limitation: they rely on static and large-scale datasets. In contrast, human beings naturally possess the ability to learn novel knowledge from imperfect real-world data such as a small number of samples or a non-static continual data stream. Attaining such an ability is particularly appealing and will push the AI models one step further toward human-level Intelligence. In this talk, I will present my work on addressing these challenges in the context of class-incremental learning and few-shot learning. Specifically, I will first discuss how to get better exemplars for class-incremental learning based on optimization. I parameterize exemplars and optimize them in an end-to-end manner to obtain high-quality memory-efficient exemplars. I will present my work on how to apply incremental techniques to a more challenging and realistic scenario, object detection. I will provide algorithm design on a transformer-based incremental object detection framework. I will briefly mention my work on addressing other challenges and discuss future research directions.

Connie Balzert

+49 681 9325 2000

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