CrossTransformers: spatially-aware few-shot transfer

22 Jul 2020Carl DoerschAnkush GuptaAndrew Zisserman

Given new tasks with very little data--such as new classes in a classification problem or a domain shift in the input--performance of modern vision systems degrades remarkably quickly. In this work, we illustrate how the neural network representations which underpin modern vision systems are subject to supervision collapse, whereby they lose any information that is not necessary for performing the training task, including information that may be necessary for transfer to new tasks or domains... (read more)

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