1,000 research outputs found
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
What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identification
Matching pedestrians across disjoint camera views, known as person
re-identification (re-id), is a challenging problem that is of importance to
visual recognition and surveillance. Most existing methods exploit local
regions within spatial manipulation to perform matching in local
correspondence. However, they essentially extract \emph{fixed} representations
from pre-divided regions for each image and perform matching based on the
extracted representation subsequently. For models in this pipeline, local finer
patterns that are crucial to distinguish positive pairs from negative ones
cannot be captured, and thus making them underperformed. In this paper, we
propose a novel deep multiplicative integration gating function, which answers
the question of \emph{what-and-where to match} for effective person re-id. To
address \emph{what} to match, our deep network emphasizes common local patterns
by learning joint representations in a multiplicative way. The network
comprises two Convolutional Neural Networks (CNNs) to extract convolutional
activations, and generates relevant descriptors for pedestrian matching. This
thus, leads to flexible representations for pair-wise images. To address
\emph{where} to match, we combat the spatial misalignment by performing
spatially recurrent pooling via a four-directional recurrent neural network to
impose spatial dependency over all positions with respect to the entire image.
The proposed network is designed to be end-to-end trainable to characterize
local pairwise feature interactions in a spatially aligned manner. To
demonstrate the superiority of our method, extensive experiments are conducted
over three benchmark data sets: VIPeR, CUHK03 and Market-1501.Comment: Published at Pattern Recognition, Elsevie
Scale-Adaptive Neural Dense Features: Learning via Hierarchical Context Aggregation
How do computers and intelligent agents view the world around them? Feature
extraction and representation constitutes one the basic building blocks towards
answering this question. Traditionally, this has been done with carefully
engineered hand-crafted techniques such as HOG, SIFT or ORB. However, there is
no ``one size fits all'' approach that satisfies all requirements. In recent
years, the rising popularity of deep learning has resulted in a myriad of
end-to-end solutions to many computer vision problems. These approaches, while
successful, tend to lack scalability and can't easily exploit information
learned by other systems. Instead, we propose SAND features, a dedicated deep
learning solution to feature extraction capable of providing hierarchical
context information. This is achieved by employing sparse relative labels
indicating relationships of similarity/dissimilarity between image locations.
The nature of these labels results in an almost infinite set of dissimilar
examples to choose from. We demonstrate how the selection of negative examples
during training can be used to modify the feature space and vary it's
properties. To demonstrate the generality of this approach, we apply the
proposed features to a multitude of tasks, each requiring different properties.
This includes disparity estimation, semantic segmentation, self-localisation
and SLAM. In all cases, we show how incorporating SAND features results in
better or comparable results to the baseline, whilst requiring little to no
additional training. Code can be found at:
https://github.com/jspenmar/SAND_featuresComment: CVPR201
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