1,287 research outputs found
Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification
Person re-identification (re-id) aims to match pedestrians observed by
disjoint camera views. It attracts increasing attention in computer vision due
to its importance to surveillance system. To combat the major challenge of
cross-view visual variations, deep embedding approaches are proposed by
learning a compact feature space from images such that the Euclidean distances
correspond to their cross-view similarity metric. However, the global Euclidean
distance cannot faithfully characterize the ideal similarity in a complex
visual feature space because features of pedestrian images exhibit unknown
distributions due to large variations in poses, illumination and occlusion.
Moreover, intra-personal training samples within a local range are robust to
guide deep embedding against uncontrolled variations, which however, cannot be
captured by a global Euclidean distance. In this paper, we study the problem of
person re-id by proposing a novel sampling to mine suitable \textit{positives}
(i.e. intra-class) within a local range to improve the deep embedding in the
context of large intra-class variations. Our method is capable of learning a
deep similarity metric adaptive to local sample structure by minimizing each
sample's local distances while propagating through the relationship between
samples to attain the whole intra-class minimization. To this end, a novel
objective function is proposed to jointly optimize similarity metric learning,
local positive mining and robust deep embedding. This yields local
discriminations by selecting local-ranged positive samples, and the learned
features are robust to dramatic intra-class variations. Experiments on
benchmarks show state-of-the-art results achieved by our method.Comment: Published on Pattern Recognitio
Level Playing Field for Million Scale Face Recognition
Face recognition has the perception of a solved problem, however when tested
at the million-scale exhibits dramatic variation in accuracies across the
different algorithms. Are the algorithms very different? Is access to good/big
training data their secret weapon? Where should face recognition improve? To
address those questions, we created a benchmark, MF2, that requires all
algorithms to be trained on same data, and tested at the million scale. MF2 is
a public large-scale set with 672K identities and 4.7M photos created with the
goal to level playing field for large scale face recognition. We contrast our
results with findings from the other two large-scale benchmarks MegaFace
Challenge and MS-Celebs-1M where groups were allowed to train on any
private/public/big/small set. Some key discoveries: 1) algorithms, trained on
MF2, were able to achieve state of the art and comparable results to algorithms
trained on massive private sets, 2) some outperformed themselves once trained
on MF2, 3) invariance to aging suffers from low accuracies as in MegaFace,
identifying the need for larger age variations possibly within identities or
adjustment of algorithms in future testings
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