29,264 research outputs found
Adaptive Deep Learning through Visual Domain Localization
A commercial robot, trained by its manufacturer to recognize a predefined number and type of objects, might be used in many settings, that will in general differ in their illumination conditions, background, type and degree of clutter, and so on. Recent computer vision works tackle this generalization issue through domain adaptation methods, assuming as source the visual domain where the system is trained and as target the domain of deployment. All approaches assume to have access to images from all classes of the target during training, an unrealistic condition in robotics applications. We address this issue proposing an algorithm that takes into account the specific needs of robot vision. Our intuition is that the nature of the domain shift experienced mostly in robotics is local. We exploit this through the learning of maps that spatially ground the domain and quantify the degree of shift, embedded into an end-to-end deep domain adaptation architecture. By explicitly localizing the roots of the domain shift we significantly reduce the number of parameters of the architecture to tune, we gain the flexibility necessary to deal with subset of categories in the target domain at training time, and we provide a clear feedback on the rationale behind any classification decision, which can be exploited in human-robot interactions. Experiments on two different settings of the iCub World database confirm the suitability of our method for robot vision
Learning Deep Context-aware Features over Body and Latent Parts for Person Re-identification
Person Re-identification (ReID) is to identify the same person across
different cameras. It is a challenging task due to the large variations in
person pose, occlusion, background clutter, etc How to extract powerful
features is a fundamental problem in ReID and is still an open problem today.
In this paper, we design a Multi-Scale Context-Aware Network (MSCAN) to learn
powerful features over full body and body parts, which can well capture the
local context knowledge by stacking multi-scale convolutions in each layer.
Moreover, instead of using predefined rigid parts, we propose to learn and
localize deformable pedestrian parts using Spatial Transformer Networks (STN)
with novel spatial constraints. The learned body parts can release some
difficulties, eg pose variations and background clutters, in part-based
representation. Finally, we integrate the representation learning processes of
full body and body parts into a unified framework for person ReID through
multi-class person identification tasks. Extensive evaluations on current
challenging large-scale person ReID datasets, including the image-based
Market1501, CUHK03 and sequence-based MARS datasets, show that the proposed
method achieves the state-of-the-art results.Comment: Accepted by CVPR 201
Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking
Discriminative Correlation Filters (DCF) have demonstrated excellent
performance for visual object tracking. The key to their success is the ability
to efficiently exploit available negative data by including all shifted
versions of a training sample. However, the underlying DCF formulation is
restricted to single-resolution feature maps, significantly limiting its
potential. In this paper, we go beyond the conventional DCF framework and
introduce a novel formulation for training continuous convolution filters. We
employ an implicit interpolation model to pose the learning problem in the
continuous spatial domain. Our proposed formulation enables efficient
integration of multi-resolution deep feature maps, leading to superior results
on three object tracking benchmarks: OTB-2015 (+5.1% in mean OP), Temple-Color
(+4.6% in mean OP), and VOT2015 (20% relative reduction in failure rate).
Additionally, our approach is capable of sub-pixel localization, crucial for
the task of accurate feature point tracking. We also demonstrate the
effectiveness of our learning formulation in extensive feature point tracking
experiments. Code and supplementary material are available at
http://www.cvl.isy.liu.se/research/objrec/visualtracking/conttrack/index.html.Comment: Accepted at ECCV 201
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