12,240 research outputs found
Person Re-identification with Deep Similarity-Guided Graph Neural Network
The person re-identification task requires to robustly estimate visual
similarities between person images. However, existing person re-identification
models mostly estimate the similarities of different image pairs of probe and
gallery images independently while ignores the relationship information between
different probe-gallery pairs. As a result, the similarity estimation of some
hard samples might not be accurate. In this paper, we propose a novel deep
learning framework, named Similarity-Guided Graph Neural Network (SGGNN) to
overcome such limitations. Given a probe image and several gallery images,
SGGNN creates a graph to represent the pairwise relationships between
probe-gallery pairs (nodes) and utilizes such relationships to update the
probe-gallery relation features in an end-to-end manner. Accurate similarity
estimation can be achieved by using such updated probe-gallery relation
features for prediction. The input features for nodes on the graph are the
relation features of different probe-gallery image pairs. The probe-gallery
relation feature updating is then performed by the messages passing in SGGNN,
which takes other nodes' information into account for similarity estimation.
Different from conventional GNN approaches, SGGNN learns the edge weights with
rich labels of gallery instance pairs directly, which provides relation fusion
more precise information. The effectiveness of our proposed method is validated
on three public person re-identification datasets.Comment: accepted to ECCV 201
Query-guided End-to-End Person Search
Person search has recently gained attention as the novel task of finding a
person, provided as a cropped sample, from a gallery of non-cropped images,
whereby several other people are also visible. We believe that i. person
detection and re-identification should be pursued in a joint optimization
framework and that ii. the person search should leverage the query image
extensively (e.g. emphasizing unique query patterns). However, so far, no prior
art realizes this. We introduce a novel query-guided end-to-end person search
network (QEEPS) to address both aspects. We leverage a most recent joint
detector and re-identification work, OIM [37]. We extend this with i. a
query-guided Siamese squeeze-and-excitation network (QSSE-Net) that uses global
context from both the query and gallery images, ii. a query-guided region
proposal network (QRPN) to produce query-relevant proposals, and iii. a
query-guided similarity subnetwork (QSimNet), to learn a query-guided
reidentification score. QEEPS is the first end-to-end query-guided detection
and re-id network. On both the most recent CUHK-SYSU [37] and PRW [46]
datasets, we outperform the previous state-of-the-art by a large margin.Comment: Accepted as poster in CVPR 201
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
Pedestrian Attribute Recognition: A Survey
Recognizing pedestrian attributes is an important task in computer vision
community due to it plays an important role in video surveillance. Many
algorithms has been proposed to handle this task. The goal of this paper is to
review existing works using traditional methods or based on deep learning
networks. Firstly, we introduce the background of pedestrian attributes
recognition (PAR, for short), including the fundamental concepts of pedestrian
attributes and corresponding challenges. Secondly, we introduce existing
benchmarks, including popular datasets and evaluation criterion. Thirdly, we
analyse the concept of multi-task learning and multi-label learning, and also
explain the relations between these two learning algorithms and pedestrian
attribute recognition. We also review some popular network architectures which
have widely applied in the deep learning community. Fourthly, we analyse
popular solutions for this task, such as attributes group, part-based,
\emph{etc}. Fifthly, we shown some applications which takes pedestrian
attributes into consideration and achieve better performance. Finally, we
summarized this paper and give several possible research directions for
pedestrian attributes recognition. The project page of this paper can be found
from the following website:
\url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey:
https://sites.google.com/view/ahu-pedestrianattributes
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