942 research outputs found
Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classification
This paper addresses the task of zero-shot image classification. The key
contribution of the proposed approach is to control the semantic embedding of
images -- one of the main ingredients of zero-shot learning -- by formulating
it as a metric learning problem. The optimized empirical criterion associates
two types of sub-task constraints: metric discriminating capacity and accurate
attribute prediction. This results in a novel expression of zero-shot learning
not requiring the notion of class in the training phase: only pairs of
image/attributes, augmented with a consistency indicator, are given as ground
truth. At test time, the learned model can predict the consistency of a test
image with a given set of attributes , allowing flexible ways to produce
recognition inferences. Despite its simplicity, the proposed approach gives
state-of-the-art results on four challenging datasets used for zero-shot
recognition evaluation.Comment: in ECCV 2016, Oct 2016, amsterdam, Netherlands. 201
Structure propagation for zero-shot learning
The key of zero-shot learning (ZSL) is how to find the information transfer
model for bridging the gap between images and semantic information (texts or
attributes). Existing ZSL methods usually construct the compatibility function
between images and class labels with the consideration of the relevance on the
semantic classes (the manifold structure of semantic classes). However, the
relationship of image classes (the manifold structure of image classes) is also
very important for the compatibility model construction. It is difficult to
capture the relationship among image classes due to unseen classes, so that the
manifold structure of image classes often is ignored in ZSL. To complement each
other between the manifold structure of image classes and that of semantic
classes information, we propose structure propagation (SP) for improving the
performance of ZSL for classification. SP can jointly consider the manifold
structure of image classes and that of semantic classes for approximating to
the intrinsic structure of object classes. Moreover, the SP can describe the
constrain condition between the compatibility function and these manifold
structures for balancing the influence of the structure propagation iteration.
The SP solution provides not only unseen class labels but also the relationship
of two manifold structures that encode the positive transfer in structure
propagation. Experimental results demonstrate that SP can attain the promising
results on the AwA, CUB, Dogs and SUN databases
Learning Hypergraph-regularized Attribute Predictors
We present a novel attribute learning framework named Hypergraph-based
Attribute Predictor (HAP). In HAP, a hypergraph is leveraged to depict the
attribute relations in the data. Then the attribute prediction problem is
casted as a regularized hypergraph cut problem in which HAP jointly learns a
collection of attribute projections from the feature space to a hypergraph
embedding space aligned with the attribute space. The learned projections
directly act as attribute classifiers (linear and kernelized). This formulation
leads to a very efficient approach. By considering our model as a multi-graph
cut task, our framework can flexibly incorporate other available information,
in particular class label. We apply our approach to attribute prediction,
Zero-shot and -shot learning tasks. The results on AWA, USAA and CUB
databases demonstrate the value of our methods in comparison with the
state-of-the-art approaches.Comment: This is an attribute learning paper accepted by CVPR 201
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