Modern recognition systems require large amounts of supervision to achieve accuracy. Adapting to new domains requires significant data from experts, which is onerous and can become too expensive. Zero-shot learning requires an annotated set of attributes for a novel category. Annotating the full set of attributes for a novel category proves to be a tedious and expensive task in deployment. This is especially the case when the recognition domain is an expert domain. We introduce a new field-guide-inspired approach to zero-shot annotation where the learner model interactively asks for the most useful attributes that define a class. We evaluate our method on classification benchmarks with attribute annotations like CUB, SUN, and AWA2 and show that our model achieves the performance of a model with full annotations at the cost of a significantly fewer number of annotations. Since the time of experts is precious, decreasing annotation cost can be very valuable for real-world deployment.

介绍了一种新的基于野外指南的的零样本注释方法, 这种方法可以显著减少注释所需的数量, 适用于专家领域的识别任务。在经过以属性注释为基础的分类基准测试后, 我们的模型证明了在注释数量成本显著降低的前提下, 与需要完整注释的模型的性能相当。

受自然导向的零样本学习