13 research outputs found
Pixel-wise Graph Attention Networks for Person Re-identification
Graph convolutional networks (GCN) is widely used to handle irregular data
since it updates node features by using the structure information of graph.
With the help of iterated GCN, high-order information can be obtained to
further enhance the representation of nodes. However, how to apply GCN to
structured data (such as pictures) has not been deeply studied. In this paper,
we explore the application of graph attention networks (GAT) in image feature
extraction. First of all, we propose a novel graph generation algorithm to
convert images into graphs through matrix transformation. It is one magnitude
faster than the algorithm based on K Nearest Neighbors (KNN). Then, GAT is used
on the generated graph to update the node features. Thus, a more robust
representation is obtained. These two steps are combined into a module called
pixel-wise graph attention module (PGA). Since the graph obtained by our graph
generation algorithm can still be transformed into a picture after processing,
PGA can be well combined with CNN. Based on these two modules, we consulted the
ResNet and design a pixel-wise graph attention network (PGANet). The PGANet is
applied to the task of person re-identification in the datasets Market1501,
DukeMTMC-reID and Occluded-DukeMTMC (outperforms state-of-the-art by 0.8\%,
1.1\% and 11\% respectively, in mAP scores). Experiment results show that it
achieves the state-of-the-art performance.
\href{https://github.com/wenyu1009/PGANet}{The code is available here}
Towards Privacy-Preserving Person Re-identification via Person Identify Shift
Recently privacy concerns of person re-identification (ReID) raise more and
more attention and preserving the privacy of the pedestrian images used by ReID
methods become essential. De-identification (DeID) methods alleviate privacy
issues by removing the identity-related of the ReID data. However, most of the
existing DeID methods tend to remove all personal identity-related information
and compromise the usability of de-identified data on the ReID task. In this
paper, we aim to develop a technique that can achieve a good trade-off between
privacy protection and data usability for person ReID. To achieve this, we
propose a novel de-identification method designed explicitly for person ReID,
named Person Identify Shift (PIS). PIS removes the absolute identity in a
pedestrian image while preserving the identity relationship between image
pairs. By exploiting the interpolation property of variational auto-encoder,
PIS shifts each pedestrian image from the current identity to another with a
new identity, resulting in images still preserving the relative identities.
Experimental results show that our method has a better trade-off between
privacy-preserving and model performance than existing de-identification
methods and can defend against human and model attacks for data privacy
Dynamic Prototype Mask for Occluded Person Re-Identification
Although person re-identification has achieved an impressive improvement in
recent years, the common occlusion case caused by different obstacles is still
an unsettled issue in real application scenarios. Existing methods mainly
address this issue by employing body clues provided by an extra network to
distinguish the visible part. Nevertheless, the inevitable domain gap between
the assistant model and the ReID datasets has highly increased the difficulty
to obtain an effective and efficient model. To escape from the extra
pre-trained networks and achieve an automatic alignment in an end-to-end
trainable network, we propose a novel Dynamic Prototype Mask (DPM) based on two
self-evident prior knowledge. Specifically, we first devise a Hierarchical Mask
Generator which utilizes the hierarchical semantic to select the visible
pattern space between the high-quality holistic prototype and the feature
representation of the occluded input image. Under this condition, the occluded
representation could be well aligned in a selected subspace spontaneously.
Then, to enrich the feature representation of the high-quality holistic
prototype and provide a more complete feature space, we introduce a Head Enrich
Module to encourage different heads to aggregate different patterns
representation in the whole image. Extensive experimental evaluations conducted
on occluded and holistic person re-identification benchmarks demonstrate the
superior performance of the DPM over the state-of-the-art methods. The code is
released at https://github.com/stone96123/DPM.Comment: Accepted by ACM MM 202
Semantic-aware Consistency Network for Cloth-changing Person Re-Identification
Cloth-changing Person Re-Identification (CC-ReID) is a challenging task that
aims to retrieve the target person across multiple surveillance cameras when
clothing changes might happen. Despite recent progress in CC-ReID, existing
approaches are still hindered by the interference of clothing variations since
they lack effective constraints to keep the model consistently focused on
clothing-irrelevant regions. To address this issue, we present a Semantic-aware
Consistency Network (SCNet) to learn identity-related semantic features by
proposing effective consistency constraints. Specifically, we generate the
black-clothing image by erasing pixels in the clothing area, which explicitly
mitigates the interference from clothing variations. In addition, to fully
exploit the fine-grained identity information, a head-enhanced attention module
is introduced, which learns soft attention maps by utilizing the proposed
part-based matching loss to highlight head information. We further design a
semantic consistency loss to facilitate the learning of high-level
identity-related semantic features, forcing the model to focus on semantically
consistent cloth-irrelevant regions. By using the consistency constraint, our
model does not require any extra auxiliary segmentation module to generate the
black-clothing image or locate the head region during the inference stage.
Extensive experiments on four cloth-changing person Re-ID datasets (LTCC, PRCC,
Vc-Clothes, and DeepChange) demonstrate that our proposed SCNet makes
significant improvements over prior state-of-the-art approaches. Our code is
available at: https://github.com/Gpn-star/SCNet.Comment: Accepted by ACM MM 202
Learning Disentangled Representation Implicitly via Transformer for Occluded Person Re-Identification
Person re-identification (re-ID) under various occlusions has been a
long-standing challenge as person images with different types of occlusions
often suffer from misalignment in image matching and ranking. Most existing
methods tackle this challenge by aligning spatial features of body parts
according to external semantic cues or feature similarities but this alignment
approach is complicated and sensitive to noises. We design DRL-Net, a
disentangled representation learning network that handles occluded re-ID
without requiring strict person image alignment or any additional supervision.
Leveraging transformer architectures, DRL-Net achieves alignment-free re-ID via
global reasoning of local features of occluded person images. It measures image
similarity by automatically disentangling the representation of undefined
semantic components, e.g., human body parts or obstacles, under the guidance of
semantic preference object queries in the transformer. In addition, we design a
decorrelation constraint in the transformer decoder and impose it over object
queries for better focus on different semantic components. To better eliminate
interference from occlusions, we design a contrast feature learning technique
(CFL) for better separation of occlusion features and discriminative ID
features. Extensive experiments over occluded and holistic re-ID benchmarks
(Occluded-DukeMTMC, Market1501 and DukeMTMC) show that the DRL-Net achieves
superior re-ID performance consistently and outperforms the state-of-the-art by
large margins for Occluded-DukeMTMC