1,543 research outputs found
Learning to rank using privileged information
Many computer vision problems have an asymmetric distribution of information between training and test time. In this work, we study the case where we are given additional information about the training data, which however will not be available at test time. This situation is called learning using privileged information (LUPI). We introduce two maximum-margin techniques that are able to make use of this additional source of information, and we show that the framework is applicable to several scenarios that have been studied in computer vision before. Experiments with attributes, bounding boxes, image tags and rationales as additional information in object classification show promising results
Fidelity-Weighted Learning
Training deep neural networks requires many training samples, but in practice
training labels are expensive to obtain and may be of varying quality, as some
may be from trusted expert labelers while others might be from heuristics or
other sources of weak supervision such as crowd-sourcing. This creates a
fundamental quality versus-quantity trade-off in the learning process. Do we
learn from the small amount of high-quality data or the potentially large
amount of weakly-labeled data? We argue that if the learner could somehow know
and take the label-quality into account when learning the data representation,
we could get the best of both worlds. To this end, we propose
"fidelity-weighted learning" (FWL), a semi-supervised student-teacher approach
for training deep neural networks using weakly-labeled data. FWL modulates the
parameter updates to a student network (trained on the task we care about) on a
per-sample basis according to the posterior confidence of its label-quality
estimated by a teacher (who has access to the high-quality labels). Both
student and teacher are learned from the data. We evaluate FWL on two tasks in
information retrieval and natural language processing where we outperform
state-of-the-art alternative semi-supervised methods, indicating that our
approach makes better use of strong and weak labels, and leads to better
task-dependent data representations.Comment: Published as a conference paper at ICLR 201
IST Austria Thesis
The human ability to recognize objects in complex scenes has driven research in the computer vision field over couple of decades. This thesis focuses on the object recognition task in images. That is, given the image, we want the computer system to be able to predict the class of the object that appears in the image. A recent successful attempt to bridge semantic understanding of the image perceived by humans and by computers uses attribute-based models. Attributes are semantic properties of the objects shared across different categories, which humans and computers can decide on. To explore the attribute-based models we take a statistical machine learning approach, and address two key learning challenges in view of object recognition task: learning augmented attributes as mid-level discriminative feature representation, and learning with attributes as privileged information. Our main contributions are parametric and non-parametric models and algorithms to solve these frameworks. In the parametric approach, we explore an autoencoder model combined with the large margin nearest neighbor principle for mid-level feature learning, and linear support vector machines for learning with privileged information. In the non-parametric approach, we propose a supervised Indian Buffet Process for automatic augmentation of semantic attributes, and explore the Gaussian Processes classification framework for learning with privileged information. A thorough experimental analysis shows the effectiveness of the proposed models in both parametric and non-parametric views
Visible-Infrared Person Re-Identification Using Privileged Intermediate Information
Visible-infrared person re-identification (ReID) aims to recognize a same
person of interest across a network of RGB and IR cameras. Some deep learning
(DL) models have directly incorporated both modalities to discriminate persons
in a joint representation space. However, this cross-modal ReID problem remains
challenging due to the large domain shift in data distributions between RGB and
IR modalities. % This paper introduces a novel approach for a creating
intermediate virtual domain that acts as bridges between the two main domains
(i.e., RGB and IR modalities) during training. This intermediate domain is
considered as privileged information (PI) that is unavailable at test time, and
allows formulating this cross-modal matching task as a problem in learning
under privileged information (LUPI). We devised a new method to generate images
between visible and infrared domains that provide additional information to
train a deep ReID model through an intermediate domain adaptation. In
particular, by employing color-free and multi-step triplet loss objectives
during training, our method provides common feature representation spaces that
are robust to large visible-infrared domain shifts. % Experimental results on
challenging visible-infrared ReID datasets indicate that our proposed approach
consistently improves matching accuracy, without any computational overhead at
test time. The code is available at:
\href{https://github.com/alehdaghi/Cross-Modal-Re-ID-via-LUPI}{https://github.com/alehdaghi/Cross-Modal-Re-ID-via-LUPI
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