34 research outputs found
A Generative Model For Zero Shot Learning Using Conditional Variational Autoencoders
Zero shot learning in Image Classification refers to the setting where images
from some novel classes are absent in the training data but other information
such as natural language descriptions or attribute vectors of the classes are
available. This setting is important in the real world since one may not be
able to obtain images of all the possible classes at training. While previous
approaches have tried to model the relationship between the class attribute
space and the image space via some kind of a transfer function in order to
model the image space correspondingly to an unseen class, we take a different
approach and try to generate the samples from the given attributes, using a
conditional variational autoencoder, and use the generated samples for
classification of the unseen classes. By extensive testing on four benchmark
datasets, we show that our model outperforms the state of the art, particularly
in the more realistic generalized setting, where the training classes can also
appear at the test time along with the novel classes
Generalized Zero-Shot Learning via Synthesized Examples
We present a generative framework for generalized zero-shot learning where
the training and test classes are not necessarily disjoint. Built upon a
variational autoencoder based architecture, consisting of a probabilistic
encoder and a probabilistic conditional decoder, our model can generate novel
exemplars from seen/unseen classes, given their respective class attributes.
These exemplars can subsequently be used to train any off-the-shelf
classification model. One of the key aspects of our encoder-decoder
architecture is a feedback-driven mechanism in which a discriminator (a
multivariate regressor) learns to map the generated exemplars to the
corresponding class attribute vectors, leading to an improved generator. Our
model's ability to generate and leverage examples from unseen classes to train
the classification model naturally helps to mitigate the bias towards
predicting seen classes in generalized zero-shot learning settings. Through a
comprehensive set of experiments, we show that our model outperforms several
state-of-the-art methods, on several benchmark datasets, for both standard as
well as generalized zero-shot learning.Comment: Accepted in CVPR'1
Zero-Annotation Object Detection with Web Knowledge Transfer
Object detection is one of the major problems in computer vision, and has
been extensively studied. Most of the existing detection works rely on
labor-intensive supervision, such as ground truth bounding boxes of objects or
at least image-level annotations. On the contrary, we propose an object
detection method that does not require any form of human annotation on target
tasks, by exploiting freely available web images. In order to facilitate
effective knowledge transfer from web images, we introduce a multi-instance
multi-label domain adaption learning framework with two key innovations. First
of all, we propose an instance-level adversarial domain adaptation network with
attention on foreground objects to transfer the object appearances from web
domain to target domain. Second, to preserve the class-specific semantic
structure of transferred object features, we propose a simultaneous transfer
mechanism to transfer the supervision across domains through pseudo strong
label generation. With our end-to-end framework that simultaneously learns a
weakly supervised detector and transfers knowledge across domains, we achieved
significant improvements over baseline methods on the benchmark datasets.Comment: Accepted in ECCV 201
Automatic Discovery, Association Estimation and Learning of Semantic Attributes for a Thousand Categories
Attribute-based recognition models, due to their impressive performance and
their ability to generalize well on novel categories, have been widely adopted
for many computer vision applications. However, usually both the attribute
vocabulary and the class-attribute associations have to be provided manually by
domain experts or large number of annotators. This is very costly and not
necessarily optimal regarding recognition performance, and most importantly, it
limits the applicability of attribute-based models to large scale data sets. To
tackle this problem, we propose an end-to-end unsupervised attribute learning
approach. We utilize online text corpora to automatically discover a salient
and discriminative vocabulary that correlates well with the human concept of
semantic attributes. Moreover, we propose a deep convolutional model to
optimize class-attribute associations with a linguistic prior that accounts for
noise and missing data in text. In a thorough evaluation on ImageNet, we
demonstrate that our model is able to efficiently discover and learn semantic
attributes at a large scale. Furthermore, we demonstrate that our model
outperforms the state-of-the-art in zero-shot learning on three data sets:
ImageNet, Animals with Attributes and aPascal/aYahoo. Finally, we enable
attribute-based learning on ImageNet and will share the attributes and
associations for future research.Comment: Accepted as a conference paper at CVPR 201