dc.contributor.author | Ji, Zongliang | |
dc.date.accessioned | 2021-09-21 00:02:07 (GMT) | |
dc.date.available | 2021-09-21 00:02:07 (GMT) | |
dc.date.issued | 2021-09-20 | |
dc.date.submitted | 2021-09-13 | |
dc.identifier.uri | http://hdl.handle.net/10012/17430 | |
dc.description.abstract | Weakly supervised segmentation signi cantly reduces user annotation e ort. Recently,
regularized loss was proposed for single object class segmentation under image-level weak
supervision. Regularized loss consists of several components. Each component, if used in
isolation, would lead to some trivial solution. However, a weighted combination of the loss
components introduces a balance between the individual biases. The weight of each component
in regularized loss is controlled by a hyperparameter. We propose an approach that
searches for regularized loss hyperparameters. The main idea is to set the most important
regularized loss component to a high weight while ensuring the other loss components are
set to weights just su ciently high to prevent the trivial solution favoured by the most
important component. Our approach results in a signi cantly improved performance over
prior work with xed hyperparameters and improves the state of the art in salient and
semantic image level supervised segmentation.
In addition to image level weak supervision, we propose a new approach for semantic
segmentation with weak supervision using bounding box annotations. Our new approach to
weak supervision from bounding boxes also makes use of hyperparameter search regularized
loss. Previous work on weak supervision from bounding boxes constructs pseudo-ground
truth by segmenting each box into the object and the background for each box independently
from all the other boxes in the dataset. We argue that the collection of boxes for
the same class naturally provides a dataset from which we can learn the appearance of that
object class. Learning a good appearance model, in turn, leads to a better segmentation of
each individual box. Thus for each class, we propose to train a segmentation CNN as from
the dataset consisting of the bounding boxes for that class using our proposed single object
approach. After we train these single-class CNNs, we apply them back to the training
bounding boxes to obtain object/background segmentations and merge them to construct
pseudo-ground truth. The obtained pseudo-ground truth is used for training a standard
segmentation CNN. We improve the state of the art on Pascal VOC 2012 benchmark in
bounding box weak supervision setting. | en |
dc.language.iso | en | en |
dc.publisher | University of Waterloo | en |
dc.subject | Computer Vision | en |
dc.subject | Artificial Intelligence | en |
dc.subject | Machine Learning | en |
dc.subject | Semantic Segmentation | en |
dc.title | Weakly-supervised Semantic Segmentation with Regularized Loss Hyperparameter Search | en |
dc.type | Master Thesis | en |
dc.pending | false | |
uws-etd.degree.department | David R. Cheriton School of Computer Science | en |
uws-etd.degree.discipline | Computer Science | en |
uws-etd.degree.grantor | University of Waterloo | en |
uws-etd.degree | Master of Mathematics | en |
uws-etd.embargo.terms | 0 | en |
uws.contributor.advisor | Veksler, Olga | |
uws.contributor.affiliation1 | Faculty of Mathematics | en |
uws.published.city | Waterloo | en |
uws.published.country | Canada | en |
uws.published.province | Ontario | en |
uws.typeOfResource | Text | en |
uws.peerReviewStatus | Unreviewed | en |
uws.scholarLevel | Graduate | en |