183,591 research outputs found
Joint Learning of Body and Part Representation for Person Re-Identification
© 2013 IEEE. Person re-identification (ReID), aiming to identify people among multiple camera views, has attracted an increasing attention due to the potential of application in surveillance security. Large variations in subjects' postures, view angles, and illuminating conditions as well as non-ideal human detection significantly increase the difficulty of person ReID. Learning a robust metric for measuring the similarity between different person images is another under-addressed problem. In this paper, following the recent success of part-based models, in order to generate a discriminative and robust feature representation, we first propose to learn global and weighted local body-part features from pedestrian images. Then, in the training phase, angular loss and part-level classification loss are employed jointly as a similarity measure to train the network, which significantly improves the robustness of the resultant network against feature variance. Experimental results on several benchmark data sets demonstrate that our method outperforms the state-of-the-art methods
Pose-Guided Multi-Granularity Attention Network for Text-Based Person Search
Text-based person search aims to retrieve the corresponding person images in
an image database by virtue of a describing sentence about the person, which
poses great potential for various applications such as video surveillance.
Extracting visual contents corresponding to the human description is the key to
this cross-modal matching problem. Moreover, correlated images and descriptions
involve different granularities of semantic relevance, which is usually ignored
in previous methods. To exploit the multilevel corresponding visual contents,
we propose a pose-guided multi-granularity attention network (PMA). Firstly, we
propose a coarse alignment network (CA) to select the related image regions to
the global description by a similarity-based attention. To further capture the
phrase-related visual body part, a fine-grained alignment network (FA) is
proposed, which employs pose information to learn latent semantic alignment
between visual body part and textual noun phrase. To verify the effectiveness
of our model, we perform extensive experiments on the CUHK Person Description
Dataset (CUHK-PEDES) which is currently the only available dataset for
text-based person search. Experimental results show that our approach
outperforms the state-of-the-art methods by 15 \% in terms of the top-1 metric.Comment: published in AAAI2020(oral
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
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