70 research outputs found
Learning Discriminative Features with Class Encoder
Deep neural networks usually benefit from unsupervised pre-training, e.g.
auto-encoders. However, the classifier further needs supervised fine-tuning
methods for good discrimination. Besides, due to the limits of full-connection,
the application of auto-encoders is usually limited to small, well aligned
images. In this paper, we incorporate the supervised information to propose a
novel formulation, namely class-encoder, whose training objective is to
reconstruct a sample from another one of which the labels are identical.
Class-encoder aims to minimize the intra-class variations in the feature space,
and to learn a good discriminative manifolds on a class scale. We impose the
class-encoder as a constraint into the softmax for better supervised training,
and extend the reconstruction on feature-level to tackle the parameter size
issue and translation issue. The experiments show that the class-encoder helps
to improve the performance on benchmarks of classification and face
recognition. This could also be a promising direction for fast training of face
recognition models.Comment: Accepted by CVPR2016 Workshop of Robust Features for Computer Visio
Deep Learning Face Representation by Joint Identification-Verification
The key challenge of face recognition is to develop effective feature
representations for reducing intra-personal variations while enlarging
inter-personal differences. In this paper, we show that it can be well solved
with deep learning and using both face identification and verification signals
as supervision. The Deep IDentification-verification features (DeepID2) are
learned with carefully designed deep convolutional networks. The face
identification task increases the inter-personal variations by drawing DeepID2
extracted from different identities apart, while the face verification task
reduces the intra-personal variations by pulling DeepID2 extracted from the
same identity together, both of which are essential to face recognition. The
learned DeepID2 features can be well generalized to new identities unseen in
the training data. On the challenging LFW dataset, 99.15% face verification
accuracy is achieved. Compared with the best deep learning result on LFW, the
error rate has been significantly reduced by 67%
Learning Social Relation Traits from Face Images
Social relation defines the association, e.g, warm, friendliness, and
dominance, between two or more people. Motivated by psychological studies, we
investigate if such fine-grained and high-level relation traits can be
characterised and quantified from face images in the wild. To address this
challenging problem we propose a deep model that learns a rich face
representation to capture gender, expression, head pose, and age-related
attributes, and then performs pairwise-face reasoning for relation prediction.
To learn from heterogeneous attribute sources, we formulate a new network
architecture with a bridging layer to leverage the inherent correspondences
among these datasets. It can also cope with missing target attribute labels.
Extensive experiments show that our approach is effective for fine-grained
social relation learning in images and videos.Comment: To appear in International Conference on Computer Vision (ICCV) 201
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