Search Results for author:
Found 22 papers, 15 papers with code
Gait Lateral Network: Learning Discriminative and Compact Representations for Gait Recognition
Gait recognition aims at identifying different people by the walking patterns, which can be conducted at a long distance without the cooperation of subjects.
Disentangled Diffusion-Based 3D Human Pose Estimation with Hierarchical Spatial and Temporal Denoiser
To address these problems, a Disentangled Diffusion-based 3D Human Pose Estimation method with Hierarchical Spatial and Temporal Denoiser is proposed, termed DDHPose.
QAGait: Revisit Gait Recognition from a Quality Perspective
Gait recognition is a promising biometric method that aims to identify pedestrians from their unique walking patterns.
FastPoseGait: A Toolbox and Benchmark for Efficient Pose-based Gait Recognition
Our toolbox supports a set of cutting-edge pose-based gait recognition algorithms and a variety of related benchmarks.
Free Lunch for Gait Recognition: A Novel Relation Descriptor
Gait recognition is to seek correct matches for query individuals by their unique walking patterns.
Unsupervised Gait Recognition with Selective Fusion
So to make the pre-trained gait recognition model able to be fine-tuned on unlabeled datasets, we propose a new task: Unsupervised Gait Recognition (UGR).
GPGait: Generalized Pose-based Gait Recognition
Recent works on pose-based gait recognition have demonstrated the potential of using such simple information to achieve results comparable to silhouette-based methods.
Exploring Deep Models for Practical Gait Recognition
Gait recognition is a rapidly advancing vision technique for person identification from a distance.
An In-Depth Exploration of Person Re-Identification and Gait Recognition in Cloth-Changing Conditions
For the cloth-changing problem, video-based ReID is rarely studied due to the lack of a suitable cloth-changing benchmark, and gait recognition is often researched under controlled conditions.
OpenGait: Revisiting Gait Recognition Towards Better Practicality
To this end, we first develop a flexible and efficient gait recognition codebase named OpenGait.
OpenGait: Revisiting Gait Recognition Toward Better Practicality
To this end, we first develop a flexible and efficient gait recognition codebase named OpenGait.
Deep Learning-based Occluded Person Re-identification: A Survey
After that, we summarize and compare the performance of recent occluded person Re-ID methods on four popular datasets: Partial-ReID, Partial-iLIDS, Occluded-ReID, and Occluded-DukeMTMC.
Progressive Feature Learning for Realistic Cloth-Changing Gait Recognition
Gait recognition is instrumental in crime prevention and social security, for it can be conducted at a long distance to figure out the identity of persons.
Learning Gait Representation from Massive Unlabelled Walking Videos: A Benchmark
As far as we know, GaitLU-1M is the first large-scale unlabelled gait dataset, and GaitSSB is the first method that achieves remarkable unsupervised results on the aforementioned benchmarks.
GaitEdge: Beyond Plain End-to-end Gait Recognition for Better Practicality
Gait is one of the most promising biometrics to identify individuals at a long distance.
Probabilistic Contrastive Learning for Domain Adaptation
However, it is undesirably observed that the standard contrastive paradigm (features+$\ell_{2}$ normalization) only brings little help for domain adaptation.
Learning a Unified Classifier Incrementally via Rebalancing
However, it has been observed that incremental learning is subject to a fundamental difficulty -- catastrophic forgetting, namely adapting a model to new data often results in severe performance degradation on previous tasks or classes.
Lifelong Learning via Progressive Distillation and Retrospection
Lifelong learning aims at adapting a learned model to new tasks while retaining the knowledge gained earlier.
Towards Human-Level License Plate Recognition
License plate recognition (LPR) is a fundamental component of various intelligent transport systems, which is always expected to be accurate and efficient enough.
DualNet: Learn Complementary Features for Image Recognition
Here two parallel neural networks are coordinated to learn complementary features and thus a wider network is constructed.
VegFru: A Domain-Specific Dataset for Fine-Grained Visual Categorization
While the existing datasets for FGVC are mainly focused on animal breeds or man-made objects with limited labelled data, VegFru is a larger dataset consisting of vegetables and fruits which are closely associated with the daily life of everyone.
Deeply Exploit Depth Information for Object Detection
This paper addresses the issue on how to more effectively coordinate the depth with RGB aiming at boosting the performance of RGB-D object detection.
