98,413 research outputs found
Joint Geometrical and Statistical Alignment for Visual Domain Adaptation
This paper presents a novel unsupervised domain adaptation method for
cross-domain visual recognition. We propose a unified framework that reduces
the shift between domains both statistically and geometrically, referred to as
Joint Geometrical and Statistical Alignment (JGSA). Specifically, we learn two
coupled projections that project the source domain and target domain data into
low dimensional subspaces where the geometrical shift and distribution shift
are reduced simultaneously. The objective function can be solved efficiently in
a closed form. Extensive experiments have verified that the proposed method
significantly outperforms several state-of-the-art domain adaptation methods on
a synthetic dataset and three different real world cross-domain visual
recognition tasks
Subspace Alignment Based Domain Adaptation for RCNN Detector
In this paper, we propose subspace alignment based domain adaptation of the
state of the art RCNN based object detector. The aim is to be able to achieve
high quality object detection in novel, real world target scenarios without
requiring labels from the target domain. While, unsupervised domain adaptation
has been studied in the case of object classification, for object detection it
has been relatively unexplored. In subspace based domain adaptation for
objects, we need access to source and target subspaces for the bounding box
features. The absence of supervision (labels and bounding boxes are absent)
makes the task challenging. In this paper, we show that we can still adapt sub-
spaces that are localized to the object by obtaining detections from the RCNN
detector trained on source and applied on target. Then we form localized
subspaces from the detections and show that subspace alignment based adaptation
between these subspaces yields improved object detection. This evaluation is
done by considering challenging real world datasets of PASCAL VOC as source and
validation set of Microsoft COCO dataset as target for various categories.Comment: 26th British Machine Vision Conference, Swansea, U
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
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