5,567 research outputs found
Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting
For person re-identification, existing deep networks often focus on
representation learning. However, without transfer learning, the learned model
is fixed as is, which is not adaptable for handling various unseen scenarios.
In this paper, beyond representation learning, we consider how to formulate
person image matching directly in deep feature maps. We treat image matching as
finding local correspondences in feature maps, and construct query-adaptive
convolution kernels on the fly to achieve local matching. In this way, the
matching process and results are interpretable, and this explicit matching is
more generalizable than representation features to unseen scenarios, such as
unknown misalignments, pose or viewpoint changes. To facilitate end-to-end
training of this architecture, we further build a class memory module to cache
feature maps of the most recent samples of each class, so as to compute image
matching losses for metric learning. Through direct cross-dataset evaluation,
the proposed Query-Adaptive Convolution (QAConv) method gains large
improvements over popular learning methods (about 10%+ mAP), and achieves
comparable results to many transfer learning methods. Besides, a model-free
temporal cooccurrence based score weighting method called TLift is proposed,
which improves the performance to a further extent, achieving state-of-the-art
results in cross-dataset person re-identification. Code is available at
https://github.com/ShengcaiLiao/QAConv.Comment: This is the ECCV 2020 version, including the appendi
Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification
Person re-identification (re-id) aims to match pedestrians observed by
disjoint camera views. It attracts increasing attention in computer vision due
to its importance to surveillance system. To combat the major challenge of
cross-view visual variations, deep embedding approaches are proposed by
learning a compact feature space from images such that the Euclidean distances
correspond to their cross-view similarity metric. However, the global Euclidean
distance cannot faithfully characterize the ideal similarity in a complex
visual feature space because features of pedestrian images exhibit unknown
distributions due to large variations in poses, illumination and occlusion.
Moreover, intra-personal training samples within a local range are robust to
guide deep embedding against uncontrolled variations, which however, cannot be
captured by a global Euclidean distance. In this paper, we study the problem of
person re-id by proposing a novel sampling to mine suitable \textit{positives}
(i.e. intra-class) within a local range to improve the deep embedding in the
context of large intra-class variations. Our method is capable of learning a
deep similarity metric adaptive to local sample structure by minimizing each
sample's local distances while propagating through the relationship between
samples to attain the whole intra-class minimization. To this end, a novel
objective function is proposed to jointly optimize similarity metric learning,
local positive mining and robust deep embedding. This yields local
discriminations by selecting local-ranged positive samples, and the learned
features are robust to dramatic intra-class variations. Experiments on
benchmarks show state-of-the-art results achieved by our method.Comment: Published on Pattern Recognitio
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
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
Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks
Bilateral filters have wide spread use due to their edge-preserving
properties. The common use case is to manually choose a parametric filter type,
usually a Gaussian filter. In this paper, we will generalize the
parametrization and in particular derive a gradient descent algorithm so the
filter parameters can be learned from data. This derivation allows to learn
high dimensional linear filters that operate in sparsely populated feature
spaces. We build on the permutohedral lattice construction for efficient
filtering. The ability to learn more general forms of high-dimensional filters
can be used in several diverse applications. First, we demonstrate the use in
applications where single filter applications are desired for runtime reasons.
Further, we show how this algorithm can be used to learn the pairwise
potentials in densely connected conditional random fields and apply these to
different image segmentation tasks. Finally, we introduce layers of bilateral
filters in CNNs and propose bilateral neural networks for the use of
high-dimensional sparse data. This view provides new ways to encode model
structure into network architectures. A diverse set of experiments empirically
validates the usage of general forms of filters
An original framework for understanding human actions and body language by using deep neural networks
The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour.
By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way.
These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively.
While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements;
both are essential tasks in many computer vision applications, including event recognition, and video surveillance.
In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided.
The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements.
All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods
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