5,241 research outputs found
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
Group Membership Prediction
The group membership prediction (GMP) problem involves predicting whether or
not a collection of instances share a certain semantic property. For instance,
in kinship verification given a collection of images, the goal is to predict
whether or not they share a {\it familial} relationship. In this context we
propose a novel probability model and introduce latent {\em view-specific} and
{\em view-shared} random variables to jointly account for the view-specific
appearance and cross-view similarities among data instances. Our model posits
that data from each view is independent conditioned on the shared variables.
This postulate leads to a parametric probability model that decomposes group
membership likelihood into a tensor product of data-independent parameters and
data-dependent factors. We propose learning the data-independent parameters in
a discriminative way with bilinear classifiers, and test our prediction
algorithm on challenging visual recognition tasks such as multi-camera person
re-identification and kinship verification. On most benchmark datasets, our
method can significantly outperform the current state-of-the-art.Comment: accepted for ICCV 201
Fine-grained Categorization and Dataset Bootstrapping using Deep Metric Learning with Humans in the Loop
Existing fine-grained visual categorization methods often suffer from three
challenges: lack of training data, large number of fine-grained categories, and
high intraclass vs. low inter-class variance. In this work we propose a generic
iterative framework for fine-grained categorization and dataset bootstrapping
that handles these three challenges. Using deep metric learning with humans in
the loop, we learn a low dimensional feature embedding with anchor points on
manifolds for each category. These anchor points capture intra-class variances
and remain discriminative between classes. In each round, images with high
confidence scores from our model are sent to humans for labeling. By comparing
with exemplar images, labelers mark each candidate image as either a "true
positive" or a "false positive". True positives are added into our current
dataset and false positives are regarded as "hard negatives" for our metric
learning model. Then the model is retrained with an expanded dataset and hard
negatives for the next round. To demonstrate the effectiveness of the proposed
framework, we bootstrap a fine-grained flower dataset with 620 categories from
Instagram images. The proposed deep metric learning scheme is evaluated on both
our dataset and the CUB-200-2001 Birds dataset. Experimental evaluations show
significant performance gain using dataset bootstrapping and demonstrate
state-of-the-art results achieved by the proposed deep metric learning methods.Comment: 10 pages, 9 figures, CVPR 201
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