77 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
Category level object segmentation - learning to segment objects with latent aspect models
International audienceWe propose a new method for learning to segment objects in images. This method is based on a latent variables model used for representing images and objects, inspired by the LDA model. Like the LDA model, our model is capable of automatically discovering which visual information comes from which object. We extend LDA by considering that images are made of multiple overlapping regions, treated as distinct documents, giving more chance to small objects to be discovered. This model is extremely well suited for assigning image patches to objects (even if they are small), and therefore for segmenting objects. We apply this method on objects belonging to categories with high intra-class variations and strong viewpoint changes
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