103,001 research outputs found
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
STA: Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification
In this work, we propose a novel Spatial-Temporal Attention (STA) approach to
tackle the large-scale person re-identification task in videos. Different from
the most existing methods, which simply compute representations of video clips
using frame-level aggregation (e.g. average pooling), the proposed STA adopts a
more effective way for producing robust clip-level feature representation.
Concretely, our STA fully exploits those discriminative parts of one target
person in both spatial and temporal dimensions, which results in a 2-D
attention score matrix via inter-frame regularization to measure the
importances of spatial parts across different frames. Thus, a more robust
clip-level feature representation can be generated according to a weighted sum
operation guided by the mined 2-D attention score matrix. In this way, the
challenging cases for video-based person re-identification such as pose
variation and partial occlusion can be well tackled by the STA. We conduct
extensive experiments on two large-scale benchmarks, i.e. MARS and
DukeMTMC-VideoReID. In particular, the mAP reaches 87.7% on MARS, which
significantly outperforms the state-of-the-arts with a large margin of more
than 11.6%.Comment: Accepted as a conference paper at AAAI 201
Learning Deep Context-aware Features over Body and Latent Parts for Person Re-identification
Person Re-identification (ReID) is to identify the same person across
different cameras. It is a challenging task due to the large variations in
person pose, occlusion, background clutter, etc How to extract powerful
features is a fundamental problem in ReID and is still an open problem today.
In this paper, we design a Multi-Scale Context-Aware Network (MSCAN) to learn
powerful features over full body and body parts, which can well capture the
local context knowledge by stacking multi-scale convolutions in each layer.
Moreover, instead of using predefined rigid parts, we propose to learn and
localize deformable pedestrian parts using Spatial Transformer Networks (STN)
with novel spatial constraints. The learned body parts can release some
difficulties, eg pose variations and background clutters, in part-based
representation. Finally, we integrate the representation learning processes of
full body and body parts into a unified framework for person ReID through
multi-class person identification tasks. Extensive evaluations on current
challenging large-scale person ReID datasets, including the image-based
Market1501, CUHK03 and sequence-based MARS datasets, show that the proposed
method achieves the state-of-the-art results.Comment: Accepted by CVPR 201
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