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- W3158996831 abstract "Current object recognition techniques are based on deep learning and require substantial training samples in order to achieve a good performance. Nonetheless, there are many applications in which no (or only a few) training images of the targets are available, whilst they are well-known by domain experts. Zero-shot learning is used in use cases with no training examples. However, current zero-shot learning techniques mostly tackle cases based on simple attributes and offer no solutions for rare, compositional objects such as a new product, or new home-made weapons. In this paper we propose ZERO: a zero-shot learning method which learns to recognize objects by their parts. Knowledge about the object composition is combined with state-of-the-art few-shot detection models, which detects the parts. ZERO is tested on the example use case of bicycle recognition, for which it outperforms few-shot object detection techniques. The object recognition is extended to detection by localizing it, by taking into account knowledge about the object’s composition, of which the results are studied qualitatively" @default.
- W3158996831 created "2021-05-10" @default.
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- W3158996831 creator A5060327141 @default.
- W3158996831 date "2021-01-01" @default.
- W3158996831 modified "2023-09-26" @default.
- W3158996831 title "ZERO – Detect objects without training examples by knowing their parts" @default.
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