1 research outputs found
Zero-Shot Learning with Sparse Attribute Propagation
Zero-shot learning (ZSL) aims to recognize a set of unseen classes without
any training images. The standard approach to ZSL requires a set of training
images annotated with seen class labels and a semantic descriptor for
seen/unseen classes (attribute vector is the most widely used). Class
label/attribute annotation is expensive; it thus severely limits the
scalability of ZSL. In this paper, we define a new ZSL setting where only a few
annotated images are collected from each seen class. This is clearly more
challenging yet more realistic than the conventional ZSL setting. To overcome
the resultant image-level attribute sparsity, we propose a novel inductive ZSL
model termed sparse attribute propagation (SAP) by propagating attribute
annotations to more unannotated images using sparse coding. This is followed by
learning bidirectional projections between features and attributes for ZSL. An
efficient solver is provided, together with rigorous theoretic algorithm
analysis. With our SAP, we show that a ZSL training dataset can now be
augmented by the abundant web images returned by image search engine, to
further improve the model performance. Moreover, the general applicability of
SAP is demonstrated on solving the social image annotation (SIA) problem.
Extensive experiments show that our model achieves superior performance on both
ZSL and SIA