166 research outputs found
Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition
A key challenge in fine-grained recognition is how to find and represent
discriminative local regions. Recent attention models are capable of learning
discriminative region localizers only from category labels with reinforcement
learning. However, not utilizing any explicit part information, they are not
able to accurately find multiple distinctive regions. In this work, we
introduce an attribute-guided attention localization scheme where the local
region localizers are learned under the guidance of part attribute
descriptions. By designing a novel reward strategy, we are able to learn to
locate regions that are spatially and semantically distinctive with
reinforcement learning algorithm. The attribute labeling requirement of the
scheme is more amenable than the accurate part location annotation required by
traditional part-based fine-grained recognition methods. Experimental results
on the CUB-200-2011 dataset demonstrate the superiority of the proposed scheme
on both fine-grained recognition and attribute recognition
Semantic Graph for Zero-Shot Learning
Zero-shot learning aims to classify visual objects without any training data
via knowledge transfer between seen and unseen classes. This is typically
achieved by exploring a semantic embedding space where the seen and unseen
classes can be related. Previous works differ in what embedding space is used
and how different classes and a test image can be related. In this paper, we
utilize the annotation-free semantic word space for the former and focus on
solving the latter issue of modeling relatedness. Specifically, in contrast to
previous work which ignores the semantic relationships between seen classes and
focus merely on those between seen and unseen classes, in this paper a novel
approach based on a semantic graph is proposed to represent the relationships
between all the seen and unseen class in a semantic word space. Based on this
semantic graph, we design a special absorbing Markov chain process, in which
each unseen class is viewed as an absorbing state. After incorporating one test
image into the semantic graph, the absorbing probabilities from the test data
to each unseen class can be effectively computed; and zero-shot classification
can be achieved by finding the class label with the highest absorbing
probability. The proposed model has a closed-form solution which is linear with
respect to the number of test images. We demonstrate the effectiveness and
computational efficiency of the proposed method over the state-of-the-arts on
the AwA (animals with attributes) dataset.Comment: 9 pages, 5 figure
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