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Found 23 papers, 17 papers with code
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
BigGait: Learning Gait Representation You Want by Large Vision Models
Gait recognition stands as one of the most pivotal remote identification technologies and progressively expands across research and industry communities.
Cross-Covariate Gait Recognition: A Benchmark
Compared to existing datasets, CCGR has both population and individual-level diversity.
A Multi-Stage Adaptive Feature Fusion Neural Network for Multimodal Gait Recognition
In this paper, by considering the temporal and spatial characteristics of gait data, we propose a multi-stage feature fusion strategy (MSFFS), which performs multimodal fusions at different stages in the feature extraction process.
Chasing Fairness in Graphs: A GNN Architecture Perspective
To this end, we aim to achieve fairness via a new GNN architecture.
Deeper into Self-Supervised Monocular Indoor Depth Estimation
One is the large areas of low-texture regions and the other is the complex ego-motion on indoor training datasets.
SkeletonGait: Gait Recognition Using Skeleton Maps
In this paper, we introduce a novel skeletal gait representation named skeleton map, together with SkeletonGait, a skeleton-based method to exploit structural information from human skeleton maps.
FairMobi-Net: A Fairness-aware Deep Learning Model for Urban Mobility Flow Generation
Generating realistic human flows across regions is essential for our understanding of urban structures and population activity patterns, enabling important applications in the fields of urban planning and management.
Pedestrian Attribute Editing for Gait Recognition and Anonymization
As a kind of biometrics, the gait information of pedestrians has attracted widespread attention from both industry and academia since it can be acquired from long distances without the cooperation of targets.
Exploring Deep Models for Practical Gait Recognition
Gait recognition is a rapidly advancing vision technique for person identification from a distance.
OpenGait: Revisiting Gait Recognition Towards Better Practicality
To this end, we first develop a flexible and efficient gait recognition codebase named OpenGait.
LidarGait: Benchmarking 3D Gait Recognition with Point Clouds
Video-based gait recognition has achieved impressive results in constrained scenarios.
OpenGait: Revisiting Gait Recognition Toward Better Practicality
To this end, we first develop a flexible and efficient gait recognition codebase named OpenGait.
Graph Attention Networks Unveil Determinants of Intra- and Inter-city Health Disparity
Understanding the determinants underlying variations in urban health status is important for informing urban design and planning, as well as public health policies.
Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes
To this end, this study creates a network embedding model capturing cross-county visitation networks, as well as heterogeneous features to uncover clusters of counties in the United States based on their pandemic spread transmission trajectories.
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.
FMP: Toward Fair Graph Message Passing against Topology Bias
Despite recent advances in achieving fair representations and predictions through regularization, adversarial debiasing, and contrastive learning in graph neural networks (GNNs), the working mechanism (i. e., message passing) behind GNNs inducing unfairness issue remains unknown.
Neural Embeddings of Urban Big Data Reveal Emergent Structures in Cities
In this study, we propose using a neural embedding model-graph neural network (GNN)- that leverages the heterogeneous features of urban areas and their interactions captured by human mobility network to obtain vector representations of these areas.
Generalized Demographic Parity for Group Fairness
We show the understanding of GDP from the probability perspective and theoretically reveal the connection between GDP regularizer and adversarial debiasing.
Adaptive Reinforcement Learning Model for Simulation of Urban Mobility during Crises
The objective of this study is to propose and test an adaptive reinforcement learning model that can learn the patterns of human mobility in a normal context and simulate the mobility during perturbations caused by crises, such as flooding, wildfire, and hurricanes.
DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and their Interactions
In this paper, we propose a deep learning model to forecast the range of increase in COVID-19 infected cases in future days and we present a novel method to compute equidimensional representations of multivariate time series and multivariate spatial time series data.
GaitPart: Temporal Part-Based Model for Gait Recognition
Gait recognition, applied to identify individual walking patterns in a long-distance, is one of the most promising video-based biometric technologies.
Ranked #1 on
Multiview Gait Recognition
on OU-MVLP
