15,483 research outputs found
AnchorNet: A Weakly Supervised Network to Learn Geometry-sensitive Features For Semantic Matching
Despite significant progress of deep learning in recent years,
state-of-the-art semantic matching methods still rely on legacy features such
as SIFT or HoG. We argue that the strong invariance properties that are key to
the success of recent deep architectures on the classification task make them
unfit for dense correspondence tasks, unless a large amount of supervision is
used. In this work, we propose a deep network, termed AnchorNet, that produces
image representations that are well-suited for semantic matching. It relies on
a set of filters whose response is geometrically consistent across different
object instances, even in the presence of strong intra-class, scale, or
viewpoint variations. Trained only with weak image-level labels, the final
representation successfully captures information about the object structure and
improves results of state-of-the-art semantic matching methods such as the
deformable spatial pyramid or the proposal flow methods. We show positive
results on the cross-instance matching task where different instances of the
same object category are matched as well as on a new cross-category semantic
matching task aligning pairs of instances each from a different object class.Comment: Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition. 201
SCNet: Learning Semantic Correspondence
This paper addresses the problem of establishing semantic correspondences
between images depicting different instances of the same object or scene
category. Previous approaches focus on either combining a spatial regularizer
with hand-crafted features, or learning a correspondence model for appearance
only. We propose instead a convolutional neural network architecture, called
SCNet, for learning a geometrically plausible model for semantic
correspondence. SCNet uses region proposals as matching primitives, and
explicitly incorporates geometric consistency in its loss function. It is
trained on image pairs obtained from the PASCAL VOC 2007 keypoint dataset, and
a comparative evaluation on several standard benchmarks demonstrates that the
proposed approach substantially outperforms both recent deep learning
architectures and previous methods based on hand-crafted features.Comment: ICCV 201
Proposal Flow: Semantic Correspondences from Object Proposals
Finding image correspondences remains a challenging problem in the presence
of intra-class variations and large changes in scene layout. Semantic flow
methods are designed to handle images depicting different instances of the same
object or scene category. We introduce a novel approach to semantic flow,
dubbed proposal flow, that establishes reliable correspondences using object
proposals. Unlike prevailing semantic flow approaches that operate on pixels or
regularly sampled local regions, proposal flow benefits from the
characteristics of modern object proposals, that exhibit high repeatability at
multiple scales, and can take advantage of both local and geometric consistency
constraints among proposals. We also show that the corresponding sparse
proposal flow can effectively be transformed into a conventional dense flow
field. We introduce two new challenging datasets that can be used to evaluate
both general semantic flow techniques and region-based approaches such as
proposal flow. We use these benchmarks to compare different matching
algorithms, object proposals, and region features within proposal flow, to the
state of the art in semantic flow. This comparison, along with experiments on
standard datasets, demonstrates that proposal flow significantly outperforms
existing semantic flow methods in various settings.Comment: arXiv admin note: text overlap with arXiv:1511.0506
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