31,382 research outputs found
Person Re-Identification by Deep Joint Learning of Multi-Loss Classification
Existing person re-identification (re-id) methods rely mostly on either
localised or global feature representation alone. This ignores their joint
benefit and mutual complementary effects. In this work, we show the advantages
of jointly learning local and global features in a Convolutional Neural Network
(CNN) by aiming to discover correlated local and global features in different
context. Specifically, we formulate a method for joint learning of local and
global feature selection losses designed to optimise person re-id when using
only generic matching metrics such as the L2 distance. We design a novel CNN
architecture for Jointly Learning Multi-Loss (JLML) of local and global
discriminative feature optimisation subject concurrently to the same re-id
labelled information. Extensive comparative evaluations demonstrate the
advantages of this new JLML model for person re-id over a wide range of
state-of-the-art re-id methods on five benchmarks (VIPeR, GRID, CUHK01, CUHK03,
Market-1501).Comment: Accepted by IJCAI 201
Sketch-a-Net that Beats Humans
We propose a multi-scale multi-channel deep neural network framework that,
for the first time, yields sketch recognition performance surpassing that of
humans. Our superior performance is a result of explicitly embedding the unique
characteristics of sketches in our model: (i) a network architecture designed
for sketch rather than natural photo statistics, (ii) a multi-channel
generalisation that encodes sequential ordering in the sketching process, and
(iii) a multi-scale network ensemble with joint Bayesian fusion that accounts
for the different levels of abstraction exhibited in free-hand sketches. We
show that state-of-the-art deep networks specifically engineered for photos of
natural objects fail to perform well on sketch recognition, regardless whether
they are trained using photo or sketch. Our network on the other hand not only
delivers the best performance on the largest human sketch dataset to date, but
also is small in size making efficient training possible using just CPUs.Comment: Accepted to BMVC 2015 (oral
Picasso, Matisse, or a Fake? Automated Analysis of Drawings at the Stroke Level for Attribution and Authentication
This paper proposes a computational approach for analysis of strokes in line
drawings by artists. We aim at developing an AI methodology that facilitates
attribution of drawings of unknown authors in a way that is not easy to be
deceived by forged art. The methodology used is based on quantifying the
characteristics of individual strokes in drawings. We propose a novel algorithm
for segmenting individual strokes. We designed and compared different
hand-crafted and learned features for the task of quantifying stroke
characteristics. We also propose and compare different classification methods
at the drawing level. We experimented with a dataset of 300 digitized drawings
with over 80 thousands strokes. The collection mainly consisted of drawings of
Pablo Picasso, Henry Matisse, and Egon Schiele, besides a small number of
representative works of other artists. The experiments shows that the proposed
methodology can classify individual strokes with accuracy 70%-90%, and
aggregate over drawings with accuracy above 80%, while being robust to be
deceived by fakes (with accuracy 100% for detecting fakes in most settings)
SCNet: Learning Semantic Correspondence
This paper addresses the problem of establishing semantic correspondences
between images depicting different instances of the same object or scene
category. Previous approaches focus on either combining a spatial regularizer
with hand-crafted features, or learning a correspondence model for appearance
only. We propose instead a convolutional neural network architecture, called
SCNet, for learning a geometrically plausible model for semantic
correspondence. SCNet uses region proposals as matching primitives, and
explicitly incorporates geometric consistency in its loss function. It is
trained on image pairs obtained from the PASCAL VOC 2007 keypoint dataset, and
a comparative evaluation on several standard benchmarks demonstrates that the
proposed approach substantially outperforms both recent deep learning
architectures and previous methods based on hand-crafted features.Comment: ICCV 201
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