Ontology-based n-ball Concept Embeddings Informing Few-shot Image Classification

no code implementations • 19 Sep 2021 • Mirantha Jayathilaka, Tingting Mu, Uli Sattler

The approach consists of two components - converting symbolic knowledge of an ontology into continuous space by learning n-ball embeddings that capture properties of subsumption and disjointness, and guiding the training and inference of a vision model using the learnt embeddings.

Few-Shot Image Classification

Visual-Semantic Embedding Model Informed by Structured Knowledge

no code implementations • 21 Sep 2020 • Mirantha Jayathilaka, Tingting Mu, Uli Sattler

With respect to both standard and zero-shot image classification, our approach shows superior performance compared with the original approach, which uses word embeddings.

Classification General Classification +3

Enhancing Generalization of First-Order Meta-Learning

no code implementations • ICLR Workshop LLD 2019 • Mirantha Jayathilaka

In this study we focus on first-order meta-learning algorithms that aim to learn a parameter initialization of a network which can quickly adapt to new concepts, given a few examples.

Few-Shot Image Classification Meta-Learning