DeepAI AI Chat
Log In Sign Up

Selective Zero-Shot Classification with Augmented Attributes

by   Jie Song, et al.
The University of Sydney
Zhejiang University

In this paper, we introduce a selective zero-shot classification problem: how can the classifier avoid making dubious predictions? Existing attribute-based zero-shot classification methods are shown to work poorly in the selective classification scenario. We argue the under-complete human defined attribute vocabulary accounts for the poor performance. We propose a selective zero-shot classifier based on both the human defined and the automatically discovered residual attributes. The proposed classifier is constructed by firstly learning the defined and the residual attributes jointly. Then the predictions are conducted within the subspace of the defined attributes. Finally, the prediction confidence is measured by both the defined and the residual attributes. Experiments conducted on several benchmarks demonstrate that our classifier produces a superior performance to other methods under the risk-coverage trade-off metric.


page 1

page 2

page 3

page 4


Zero Shot Recognition with Unreliable Attributes

In principle, zero-shot learning makes it possible to train a recognitio...

Make an Omelette with Breaking Eggs: Zero-Shot Learning for Novel Attribute Synthesis

Most of the existing algorithms for zero-shot classification problems ty...

Zero-Shot Object Recognition System based on Topic Model

Object recognition systems usually require fully complete manually label...

Hard Negative Mining for Metric Learning Based Zero-Shot Classification

Zero-Shot learning has been shown to be an efficient strategy for domain...

Zero-sample surface defect detection and classification based on semantic feedback neural network

Defect detection and classification technology has changed from traditio...

Combining Deep Universal Features, Semantic Attributes, and Hierarchical Classification for Zero-Shot Learning

We address zero-shot (ZS) learning, building upon prior work in hierarch...

Field-Guide-Inspired Zero-Shot Learning

Modern recognition systems require large amounts of supervision to achie...