55,412 research outputs found
A Benchmark of Video-Based Clothes-Changing Person Re-Identification
Person re-identification (Re-ID) is a classical computer vision task and has
achieved great progress so far. Recently, long-term Re-ID with clothes-changing
has attracted increasing attention. However, existing methods mainly focus on
image-based setting, where richer temporal information is overlooked. In this
paper, we focus on the relatively new yet practical problem of clothes-changing
video-based person re-identification (CCVReID), which is less studied. We
systematically study this problem by simultaneously considering the challenge
of the clothes inconsistency issue and the temporal information contained in
the video sequence for the person Re-ID problem. Based on this, we develop a
two-branch confidence-aware re-ranking framework for handling the CCVReID
problem. The proposed framework integrates two branches that consider both the
classical appearance features and cloth-free gait features through a
confidence-guided re-ranking strategy. This method provides the baseline method
for further studies. Also, we build two new benchmark datasets for CCVReID
problem, including a large-scale synthetic video dataset and a real-world one,
both containing human sequences with various clothing changes. We will release
the benchmark and code in this work to the public
Learning Clothing and Pose Invariant 3D Shape Representation for Long-Term Person Re-Identification
Long-Term Person Re-Identification (LT-ReID) has become increasingly crucial
in computer vision and biometrics. In this work, we aim to extend LT-ReID
beyond pedestrian recognition to include a wider range of real-world human
activities while still accounting for cloth-changing scenarios over large time
gaps. This setting poses additional challenges due to the geometric
misalignment and appearance ambiguity caused by the diversity of human pose and
clothing. To address these challenges, we propose a new approach 3DInvarReID
for (i) disentangling identity from non-identity components (pose, clothing
shape, and texture) of 3D clothed humans, and (ii) reconstructing accurate 3D
clothed body shapes and learning discriminative features of naked body shapes
for person ReID in a joint manner. To better evaluate our study of LT-ReID, we
collect a real-world dataset called CCDA, which contains a wide variety of
human activities and clothing changes. Experimentally, we show the superior
performance of our approach for person ReID.Comment: 10 pages, 7 figures, accepted by ICCV 202
Exploring Shape Embedding for Cloth-Changing Person Re-Identification via 2D-3D Correspondences
Cloth-Changing Person Re-Identification (CC-ReID) is a common and realistic
problem since fashion constantly changes over time and people's aesthetic
preferences are not set in stone. While most existing cloth-changing ReID
methods focus on learning cloth-agnostic identity representations from coarse
semantic cues (e.g. silhouettes and part segmentation maps), they neglect the
continuous shape distributions at the pixel level. In this paper, we propose
Continuous Surface Correspondence Learning (CSCL), a new shape embedding
paradigm for cloth-changing ReID. CSCL establishes continuous correspondences
between a 2D image plane and a canonical 3D body surface via pixel-to-vertex
classification, which naturally aligns a person image to the surface of a 3D
human model and simultaneously obtains pixel-wise surface embeddings. We
further extract fine-grained shape features from the learned surface embeddings
and then integrate them with global RGB features via a carefully designed
cross-modality fusion module. The shape embedding paradigm based on 2D-3D
correspondences remarkably enhances the model's global understanding of human
body shape. To promote the study of ReID under clothing change, we construct 3D
Dense Persons (DP3D), which is the first large-scale cloth-changing ReID
dataset that provides densely annotated 2D-3D correspondences and a precise 3D
mesh for each person image, while containing diverse cloth-changing cases over
all four seasons. Experiments on both cloth-changing and cloth-consistent ReID
benchmarks validate the effectiveness of our method.Comment: Accepted by ACM MM 202
Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification
Cloth-changing person Re-IDentification (Re-ID) is a particularly challenging
task, suffering from two limitations of inferior identity-relevant features and
limited training samples. Existing methods mainly leverage auxiliary
information to facilitate discriminative feature learning, including
soft-biometrics features of shapes and gaits, and additional labels of
clothing. However, these information may be unavailable in real-world
applications. In this paper, we propose a novel FIne-grained Representation and
Recomposition (FIRe) framework to tackle both limitations without any
auxiliary information. Specifically, we first design a Fine-grained Feature
Mining (FFM) module to separately cluster images of each person. Images with
similar so-called fine-grained attributes (e.g., clothes and viewpoints) are
encouraged to cluster together. An attribute-aware classification loss is
introduced to perform fine-grained learning based on cluster labels, which are
not shared among different people, promoting the model to learn
identity-relevant features. Furthermore, by taking full advantage of the
clustered fine-grained attributes, we present a Fine-grained Attribute
Recomposition (FAR) module to recompose image features with different
attributes in the latent space. It can significantly enhance representations
for robust feature learning. Extensive experiments demonstrate that FIRe
can achieve state-of-the-art performance on five widely-used cloth-changing
person Re-ID benchmarks
Identity-Guided Collaborative Learning for Cloth-Changing Person Reidentification
Cloth-changing person reidentification (ReID) is a newly emerging research
topic that is aimed at addressing the issues of large feature variations due to
cloth-changing and pedestrian view/pose changes. Although significant progress
has been achieved by introducing extra information (e.g., human contour
sketching information, human body keypoints, and 3D human information),
cloth-changing person ReID is still challenging due to impressionable
pedestrian representations. Moreover, human semantic information and pedestrian
identity information are not fully explored. To solve these issues, we propose
a novel identity-guided collaborative learning scheme (IGCL) for cloth-changing
person ReID, where the human semantic is fully utilized and the identity is
unchangeable to guide collaborative learning. First, we design a novel clothing
attention degradation stream to reasonably reduce the interference caused by
clothing information where clothing attention and mid-level collaborative
learning are employed. Second, we propose a human semantic attention and body
jigsaw stream to highlight the human semantic information and simulate
different poses of the same identity. In this way, the extraction features not
only focus on human semantic information that is unrelated to the background
but also are suitable for pedestrian pose variations. Moreover, a pedestrian
identity enhancement stream is further proposed to enhance the identity
importance and extract more favorable identity robust features. Most
importantly, all these streams are jointly explored in an end-to-end unified
framework, and the identity is utilized to guide the optimization. Extensive
experiments on five public clothing person ReID datasets demonstrate that the
proposed IGCL significantly outperforms SOTA methods and that the extracted
feature is more robust, discriminative, and clothing-irrelevant
- …