15,360 research outputs found
Visual and semantic knowledge transfer for large scale semi-supervised object detection
Deep CNN-based object detection systems have achieved remarkable success on several large-scale object detection benchmarks. However, training such detectors requires a large number of labeled bounding boxes, which are more difficult to obtain than image-level annotations. Previous work addresses this issue by transforming image-level classifiers into object detectors. This is done by modeling the differences between the two on categories with both image-level and bounding box annotations, and transferring this information to convert classifiers to detectors for categories without bounding box annotations. We improve this previous work by incorporating knowledge about object similarities from visual and semantic domains during the transfer process. The intuition behind our proposed method is that visually and semantically similar categories should exhibit more common transferable properties than dissimilar categories, e.g. a better detector would result by transforming the differences between a dog classifier and a dog detector onto the cat class, than would by transforming from the violin class. Experimental results on the challenging ILSVRC2013 detection dataset demonstrate that each of our proposed object similarity based knowledge transfer methods outperforms the baseline methods. We found strong evidence that visual similarity and semantic relatedness are complementary for the task, and when combined notably improve detection, achieving state-of-the-art detection performance in a semi-supervised setting
Large Scale Semi-supervised Object Detection using Visual and Semantic Knowledge Transfer
Deep CNN-based object detection systems have achieved
remarkable success on several large-scale object detection
benchmarks. However, training such detectors requires a
large number of labeled bounding boxes, which are more
difficult to obtain than image-level annotations. Previous
work addresses this issue by transforming image-level classifiers
into object detectors. This is done by modeling the
differences between the two on categories with both imagelevel
and bounding box annotations, and transferring this
information to convert classifiers to detectors for categories
without bounding box annotations. We improve this previous
work by incorporating knowledge about object similarities
from visual and semantic domains during the transfer
process. The intuition behind our proposed method is that
visually and semantically similar categories should exhibit
more common transferable properties than dissimilar categories,
e.g. a better detector would result by transforming
the differences between a dog classifier and a dog detector
onto the cat class, than would by transforming from
the violin class. Experimental results on the challenging
ILSVRC2013 detection dataset demonstrate that each of our
proposed object similarity based knowledge transfer methods
outperforms the baseline methods. We found strong evidence
that visual similarity and semantic relatedness are
complementary for the task, and when combined notably
improve detection, achieving state-of-the-art detection performance
in a semi-supervised setting
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
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