Field-Guide-Inspired Zero-Shot Learning

• URL: http://arxiv.org/abs/2108.10967v1
• Date: Tue, 24 Aug 2021 21:36:05 GMT
• Title: Field-Guide-Inspired Zero-Shot Learning
• Authors: Utkarsh Mall, Bharath Hariharan, and Kavita Bala
• Abstract summary: 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.
• Score: 39.10156282444968
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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